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matching</title> <meta name="description" content="Search results for: feature matching"> <meta name="keywords" content="feature matching"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="feature matching"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2020</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: feature matching</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2020</span> K-Means Based Matching Algorithm for Multi-Resolution Feature Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shao-Tzu%20Huang">Shao-Tzu Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chen-Chien%20Hsu"> Chen-Chien Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Yen%20Wang"> Wei-Yen Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Matching high dimensional features between images is computationally expensive for exhaustive search approaches in computer vision. Although the dimension of the feature can be degraded by simplifying the prior knowledge of homography, matching accuracy may degrade as a tradeoff. In this paper, we present a feature matching method based on k-means algorithm that reduces the matching cost and matches the features between images instead of using a simplified geometric assumption. Experimental results show that the proposed method outperforms the previous linear exhaustive search approaches in terms of the inlier ratio of matched pairs. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title="feature matching">feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=k-means%20clustering" title=" k-means clustering"> k-means clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=RANSAC" title=" RANSAC"> RANSAC</a> </p> <a href="https://publications.waset.org/abstracts/73493/k-means-based-matching-algorithm-for-multi-resolution-feature-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/73493.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">357</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2019</span> Automated Feature Detection and Matching Algorithms for Breast IR Sequence Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Yen%20Lee">Chia-Yen Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao-Jen%20Wang"> Hao-Jen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhih-Hao%20Lai"> Jhih-Hao Lai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Harris%20corner" title="Harris corner">Harris corner</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20image" title=" infrared image"> infrared image</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20detection" title=" feature detection"> feature detection</a>, <a href="https://publications.waset.org/abstracts/search?q=registration" title=" registration"> registration</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a> </p> <a href="https://publications.waset.org/abstracts/16915/automated-feature-detection-and-matching-algorithms-for-breast-ir-sequence-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16915.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">304</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2018</span> SIFT and Perceptual Zoning Applied to CBIR Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Simone%20B.%20K.%20Aires">Simone B. K. Aires</a>, <a href="https://publications.waset.org/abstracts/search?q=Cinthia%20O.%20de%20A.%20Freitas"> Cinthia O. de A. Freitas</a>, <a href="https://publications.waset.org/abstracts/search?q=Luiz%20E.%20S.%20Oliveira"> Luiz E. S. Oliveira</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper contributes to the CBIR systems applied to trademark retrieval. The proposed model includes aspects from visual perception of the shapes, by means of feature extractor associated to a non-symmetrical perceptual zoning mechanism based on the Principles of Gestalt. Thus, the feature set were performed using Scale Invariant Feature Transform (SIFT). We carried out experiments using four different zonings strategies (Z = 4, 5H, 5V, 7) for matching and retrieval tasks. Our proposal method achieved the normalized recall (Rn) equal to 0.84. Experiments show that the non-symmetrical zoning could be considered as a tool to build more reliable trademark retrieval systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CBIR" title="CBIR">CBIR</a>, <a href="https://publications.waset.org/abstracts/search?q=Gestalt" title=" Gestalt"> Gestalt</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=non-symmetrical%20zoning" title=" non-symmetrical zoning"> non-symmetrical zoning</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a> </p> <a href="https://publications.waset.org/abstracts/15764/sift-and-perceptual-zoning-applied-to-cbir-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15764.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2017</span> A Study of Effective Stereo Matching Method for Long-Wave Infrared Camera Module</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Koo%20Kim">Hyun-Koo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonghun%20Kim"> Yonghun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Hoon%20Kim"> Yong-Hoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Hee%20Lee"> Ju Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Myungho%20Song"> Myungho Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20driver%20assistance%20system" title="advanced driver assistance system">advanced driver assistance system</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title=" pedestrian detection"> pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20matching%20method" title=" stereo matching method"> stereo matching method</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20long-wave%20IR%20camera" title=" stereo long-wave IR camera"> stereo long-wave IR camera</a> </p> <a href="https://publications.waset.org/abstracts/58413/a-study-of-effective-stereo-matching-method-for-long-wave-infrared-camera-module" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58413.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">413</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2016</span> Myanmar Character Recognition Using Eight Direction Chain Code Frequency Features </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kyi%20Pyar%20Zaw">Kyi Pyar Zaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Zin%20Mar%20Kyu"> Zin Mar Kyu </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chain%20code%20frequency" title="chain code frequency">chain code frequency</a>, <a href="https://publications.waset.org/abstracts/search?q=character%20recognition" title=" character recognition"> character recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=features%20matching" title=" features matching"> features matching</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/77278/myanmar-character-recognition-using-eight-direction-chain-code-frequency-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77278.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2015</span> Generation of Photo-Mosaic Images through Block Matching and Color Adjustment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hae-Yeoun%20Lee">Hae-Yeoun Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Mosaic refers to a technique that makes image by gathering lots of small materials in various colours. This paper presents an automatic algorithm that makes the photomosaic image using photos. The algorithm is composed of four steps: Partition and feature extraction, block matching, redundancy removal and colour adjustment. The input image is partitioned in the small block to extract feature. Each block is matched to find similar photo in database by comparing similarity with Euclidean difference between blocks. The intensity of the block is adjusted to enhance the similarity of image by replacing the value of light and darkness with that of relevant block. Further, the quality of image is improved by minimizing the redundancy of tiles in the adjacent blocks. Experimental results support that the proposed algorithm is excellent in quantitative analysis and qualitative analysis. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photomosaic" title="photomosaic">photomosaic</a>, <a href="https://publications.waset.org/abstracts/search?q=Euclidean%20distance" title=" Euclidean distance"> Euclidean distance</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20matching" title=" block matching"> block matching</a>, <a href="https://publications.waset.org/abstracts/search?q=intensity%20adjustment" title=" intensity adjustment"> intensity adjustment</a> </p> <a href="https://publications.waset.org/abstracts/7022/generation-of-photo-mosaic-images-through-block-matching-and-color-adjustment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/7022.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">278</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2014</span> Video Stabilization Using Feature Point Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shamsundar%20Kulkarni">Shamsundar Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20stabilization" title="video stabilization">video stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20feature%20matching" title=" point feature matching"> point feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=salient%20points" title=" salient points"> salient points</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality%20measurement" title=" image quality measurement"> image quality measurement</a> </p> <a href="https://publications.waset.org/abstracts/57341/video-stabilization-using-feature-point-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57341.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2013</span> Indoor Real-Time Positioning and Mapping Based on Manhattan Hypothesis Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linhang%20Zhu">Linhang Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Zhu"> Hongyu Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahe%20Liu"> Jiahe Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Manhattan%20world%20hypothesis" title="Manhattan world hypothesis">Manhattan world hypothesis</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20positioning%20and%20mapping" title=" real-time positioning and mapping"> real-time positioning and mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title=" feature matching"> feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=loopback%20detection" title=" loopback detection"> loopback detection</a> </p> <a href="https://publications.waset.org/abstracts/173745/indoor-real-time-positioning-and-mapping-based-on-manhattan-hypothesis-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173745.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">61</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2012</span> Registration of Multi-Temporal Unmanned Aerial Vehicle Images for Facility Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongyeob%20Han">Dongyeob Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Jungwon%20Huh"> Jungwon Huh</a>, <a href="https://publications.waset.org/abstracts/search?q=Quang%20Huy%20Tran"> Quang Huy Tran</a>, <a href="https://publications.waset.org/abstracts/search?q=Choonghyun%20Kang"> Choonghyun Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned Aerial Vehicles (UAVs) have been used for surveillance, monitoring, inspection, and mapping. In this paper, we present a systematic approach for automatic registration of UAV images for monitoring facilities such as building, green house, and civil structures. The two-step process is applied; 1) an image matching technique based on SURF (Speeded up Robust Feature) and RANSAC (Random Sample Consensus), 2) bundle adjustment of multi-temporal images. Image matching to find corresponding points is one of the most important steps for the precise registration of multi-temporal images. We used the SURF algorithm to find a quick and effective matching points. RANSAC algorithm was used in the process of finding matching points between images and in the bundle adjustment process. Experimental results from UAV images showed that our approach has a good accuracy to be applied to the change detection of facility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building" title="building">building</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicle" title=" unmanned aerial vehicle"> unmanned aerial vehicle</a> </p> <a href="https://publications.waset.org/abstracts/85064/registration-of-multi-temporal-unmanned-aerial-vehicle-images-for-facility-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85064.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2011</span> BART Matching Method: Using Bayesian Additive Regression Tree for Data Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gianna%20Zou">Gianna Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BART" title="BART">BART</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/149989/bart-matching-method-using-bayesian-additive-regression-tree-for-data-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2010</span> Assisted Video Colorization Using Texture Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andre%20Peres%20Ramos">Andre Peres Ramos</a>, <a href="https://publications.waset.org/abstracts/search?q=Franklin%20Cesar%20Flores"> Franklin Cesar Flores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorization is the process of add colors to a monochromatic image or video. Usually, the process involves to segment the image in regions of interest and then apply colors to each one, for videos, this process is repeated for each frame, which makes it a tedious and time-consuming job. We propose a new assisted method for video colorization; the user only has to colorize one frame, and then the colors are propagated to following frames. The user can intervene at any time to correct eventual errors in color assignment. The method consists of to extract intensity and texture descriptors from the frames and then perform a feature matching to determine the best color for each segment. To reduce computation time and give a better spatial coherence we narrow the area of search and give weights for each feature to emphasize texture descriptors. To give a more natural result, we use an optimization algorithm to make the color propagation. Experimental results in several image sequences, compared to others existing methods, demonstrates that the proposed method perform a better colorization with less time and user interference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colorization" title="colorization">colorization</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title=" feature matching"> feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptors" title=" texture descriptors"> texture descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20segmentation" title=" video segmentation"> video segmentation</a> </p> <a href="https://publications.waset.org/abstracts/97191/assisted-video-colorization-using-texture-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97191.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">162</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2009</span> Visual Thing Recognition with Binary Scale-Invariant Feature Transform and Support Vector Machine Classifiers Using Color Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei-Jong%20Yang">Wei-Jong Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Wei-Hau%20Du"> Wei-Hau Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Pau-Choo%20Chang"> Pau-Choo Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jar-Ferr%20Yang"> Jar-Ferr Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pi-Hsia%20Hung"> Pi-Hsia Hung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=color%20moments" title="color moments">color moments</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20thing%20recognition%20system" title=" visual thing recognition system"> visual thing recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20SIFT" title=" color SIFT"> color SIFT</a> </p> <a href="https://publications.waset.org/abstracts/62857/visual-thing-recognition-with-binary-scale-invariant-feature-transform-and-support-vector-machine-classifiers-using-color-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62857.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">467</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2008</span> Iris Feature Extraction and Recognition Based on Two-Dimensional Gabor Wavelength Transform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bamidele%20Samson%20Alobalorun">Bamidele Samson Alobalorun</a>, <a href="https://publications.waset.org/abstracts/search?q=Ifedotun%20Roseline%20Idowu"> Ifedotun Roseline Idowu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Biometrics technologies apply the human body parts for their unique and reliable identification based on physiological traits. The iris recognition system is a biometric–based method for identification. The human iris has some discriminating characteristics which provide efficiency to the method. In order to achieve this efficiency, there is a need for feature extraction of the distinct features from the human iris in order to generate accurate authentication of persons. In this study, an approach for an iris recognition system using 2D Gabor for feature extraction is applied to iris templates. The 2D Gabor filter formulated the patterns that were used for training and equally sent to the hamming distance matching technique for recognition. A comparison of results is presented using two iris image subjects of different matching indices of 1,2,3,4,5 filter based on the CASIA iris image database. By comparing the two subject results, the actual computational time of the developed models, which is measured in terms of training and average testing time in processing the hamming distance classifier, is found with best recognition accuracy of 96.11% after capturing the iris localization or segmentation using the Daughman’s Integro-differential, the normalization is confined to the Daugman’s rubber sheet model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Daugman%20rubber%20sheet" title="Daugman rubber sheet">Daugman rubber sheet</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamming%20distance" title=" Hamming distance"> Hamming distance</a>, <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition%20system" title=" iris recognition system"> iris recognition system</a>, <a href="https://publications.waset.org/abstracts/search?q=2D%20Gabor%20wavelet%20transform" title=" 2D Gabor wavelet transform"> 2D Gabor wavelet transform</a> </p> <a href="https://publications.waset.org/abstracts/170345/iris-feature-extraction-and-recognition-based-on-two-dimensional-gabor-wavelength-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/170345.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">65</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2007</span> Least Support Orthogonal Matching Pursuit (LS-OMP) Recovery Method for Invisible Watermarking Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Israa%20Sh.%20Tawfic">Israa Sh. Tawfic</a>, <a href="https://publications.waset.org/abstracts/search?q=Sema%20Koc%20Kayhan"> Sema Koc Kayhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, first, we propose least support orthogonal matching pursuit (LS-OMP) algorithm to improve the performance, of the OMP (orthogonal matching pursuit) algorithm. LS-OMP algorithm adaptively chooses optimum L (least part of support), at each iteration. This modification helps to reduce the computational complexity significantly and performs better than OMP algorithm. Second, we give the procedure for the invisible image watermarking in the presence of compressive sampling. The image reconstruction based on a set of watermarked measurements is performed using LS-OMP. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20matching%20pursuit" title=" orthogonal matching pursuit"> orthogonal matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=restricted%20isometry%20property" title=" restricted isometry property"> restricted isometry property</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20reconstruction" title=" signal reconstruction"> signal reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20support%20orthogonal%20matching%20pursuit" title=" least support orthogonal matching pursuit"> least support orthogonal matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=watermark" title=" watermark"> watermark</a> </p> <a href="https://publications.waset.org/abstracts/15820/least-support-orthogonal-matching-pursuit-ls-omp-recovery-method-for-invisible-watermarking-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15820.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">338</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2006</span> Impedance Matching of Axial Mode Helical Antennas</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossein%20Mardani">Hossein Mardani</a>, <a href="https://publications.waset.org/abstracts/search?q=Neil%20Buchanan"> Neil Buchanan</a>, <a href="https://publications.waset.org/abstracts/search?q=Robert%20Cahill"> Robert Cahill</a>, <a href="https://publications.waset.org/abstracts/search?q=Vincent%20Fusco"> Vincent Fusco</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we study the input impedance characteristics of axial mode helical antennas to find an effective way for matching it to 50 Ω. The study is done on the important matching parameters such as like wire diameter and helix to the ground plane gap. It is intended that these parameters control the matching without detrimentally affecting the radiation pattern. Using transmission line theory, a simple broadband technique is proposed, which is applicable for perfect matching of antennas with similar design parameters. We provide design curves to help to choose the proper dimensions of the matching section based on the antenna’s unmatched input impedance. Finally, using the proposed technique, a 4-turn axial mode helix is designed at 2.5 GHz center frequency and the measurement results of the manufactured antenna will be included. This parametric study gives a good insight into the input impedance characteristics of axial mode helical antennas and the proposed impedance matching approach provides a simple, useful method for matching these types of antennas. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=antenna" title="antenna">antenna</a>, <a href="https://publications.waset.org/abstracts/search?q=helix" title=" helix"> helix</a>, <a href="https://publications.waset.org/abstracts/search?q=helical" title=" helical"> helical</a>, <a href="https://publications.waset.org/abstracts/search?q=axial%20mode" title=" axial mode"> axial mode</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20power%20transfer" title=" wireless power transfer"> wireless power transfer</a>, <a href="https://publications.waset.org/abstracts/search?q=impedance%20matching" title=" impedance matching"> impedance matching</a> </p> <a href="https://publications.waset.org/abstracts/134308/impedance-matching-of-axial-mode-helical-antennas" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134308.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">312</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2005</span> A Computer-Aided System for Tooth Shade Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zuhal%20Kurt">Zuhal Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Meral%20Kurt"> Meral Kurt</a>, <a href="https://publications.waset.org/abstracts/search?q=Bilge%20T.%20Bal"> Bilge T. Bal</a>, <a href="https://publications.waset.org/abstracts/search?q=Kemal%20Ozkan"> Kemal Ozkan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Shade matching and reproduction is the most important element of success in prosthetic dentistry. Until recently, shade matching procedure was implemented by dentists visual perception with the help of shade guides. Since many factors influence visual perception; tooth shade matching using visual devices (shade guides) is highly subjective and inconsistent. Subjective nature of this process has lead to the development of instrumental devices. Nowadays, colorimeters, spectrophotometers, spectroradiometers and digital image analysing systems are used for instrumental shade selection. Instrumental devices have advantages that readings are quantifiable, can obtain more rapidly and simply, objectively and precisely. However, these devices have noticeable drawbacks. For example, translucent structure and irregular surfaces of teeth lead to defects on measurement with these devices. Also between the results acquired by devices with different measurement principles may make inconsistencies. So, its obligatory to search for new methods for dental shade matching process. A computer-aided system device; digital camera has developed rapidly upon today. Currently, advances in image processing and computing have resulted in the extensive use of digital cameras for color imaging. This procedure has a much cheaper process than the use of traditional contact-type color measurement devices. Digital cameras can be taken by the place of contact-type instruments for shade selection and overcome their disadvantages. Images taken from teeth show morphology and color texture of teeth. In last decades, a new method was recommended to compare the color of shade tabs taken by a digital camera using color features. This method showed that visual and computer-aided shade matching systems should be used as concatenated. Recently using methods of feature extraction techniques are based on shape description and not used color information. However, color is mostly experienced as an essential property in depicting and extracting features from objects in the world around us. When local feature descriptors with color information are extended by concatenating color descriptor with the shape descriptor, that descriptor will be effective on visual object recognition and classification task. Therefore, the color descriptor is to be used in combination with a shape descriptor it does not need to contain any spatial information, which leads us to use local histograms. This local color histogram method is remain reliable under variation of photometric changes, geometrical changes and variation of image quality. So, coloring local feature extraction methods are used to extract features, and also the Scale Invariant Feature Transform (SIFT) descriptor used to for shape description in the proposed method. After the combination of these descriptors, the state-of-art descriptor named by Color-SIFT will be used in this study. Finally, the image feature vectors obtained from quantization algorithm are fed to classifiers such as Nearest Neighbor (KNN), Naive Bayes or Support Vector Machines (SVM) to determine label(s) of the visual object category or matching. In this study, SVM are used as classifiers for color determination and shade matching. Finally, experimental results of this method will be compared with other recent studies. It is concluded from the study that the proposed method is remarkable development on computer aided tooth shade determination system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classifiers" title="classifiers">classifiers</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20determination" title=" color determination"> color determination</a>, <a href="https://publications.waset.org/abstracts/search?q=computer-aided%20system" title=" computer-aided system"> computer-aided system</a>, <a href="https://publications.waset.org/abstracts/search?q=tooth%20shade%20matching" title=" tooth shade matching"> tooth shade matching</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a> </p> <a href="https://publications.waset.org/abstracts/51113/a-computer-aided-system-for-tooth-shade-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51113.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">444</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2004</span> Computing Maximum Uniquely Restricted Matchings in Restricted Interval Graphs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Swapnil%20Gupta">Swapnil Gupta</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pandu%20Rangan"> C. Pandu Rangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A uniquely restricted matching is defined to be a matching M whose matched vertices induces a sub-graph which has only one perfect matching. In this paper, we make progress on the open question of the status of this problem on interval graphs (graphs obtained as the intersection graph of intervals on a line). We give an algorithm to compute maximum cardinality uniquely restricted matchings on certain sub-classes of interval graphs. We consider two sub-classes of interval graphs, the former contained in the latter, and give O(|E|^2) time algorithms for both of them. It is to be noted that both sub-classes are incomparable to proper interval graphs (graphs obtained as the intersection graph of intervals in which no interval completely contains another interval), on which the problem can be solved in polynomial time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=uniquely%20restricted%20matching" title="uniquely restricted matching">uniquely restricted matching</a>, <a href="https://publications.waset.org/abstracts/search?q=interval%20graph" title=" interval graph"> interval graph</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=induced%20matching" title=" induced matching"> induced matching</a>, <a href="https://publications.waset.org/abstracts/search?q=witness%20counting" title=" witness counting"> witness counting</a> </p> <a href="https://publications.waset.org/abstracts/45203/computing-maximum-uniquely-restricted-matchings-in-restricted-interval-graphs" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45203.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">389</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2003</span> Retina Registration for Biometrics Based on Characterization of Retinal Feature Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nougrara%20Zineb">Nougrara Zineb</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fovea" title="fovea">fovea</a>, <a href="https://publications.waset.org/abstracts/search?q=optic%20disc" title=" optic disc"> optic disc</a>, <a href="https://publications.waset.org/abstracts/search?q=registration" title=" registration"> registration</a>, <a href="https://publications.waset.org/abstracts/search?q=retinal%20images" title=" retinal images"> retinal images</a> </p> <a href="https://publications.waset.org/abstracts/72438/retina-registration-for-biometrics-based-on-characterization-of-retinal-feature-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72438.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">266</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2002</span> A Developmental Survey of Local Stereo Matching Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andr%C3%A9%20Smith">André Smith</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Abdel-Dayem"> Amr Abdel-Dayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents an overview of the history and development of stereo matching algorithms. Details from its inception, up to relatively recent techniques are described, noting challenges that have been surmounted across these past decades. Different components of these are explored, though focus is directed towards the local matching techniques. While global approaches have existed for some time, and demonstrated greater accuracy than their counterparts, they are generally quite slow. Many strides have been made more recently, allowing local methods to catch up in terms of accuracy, without sacrificing the overall performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=developmental%20survey" title="developmental survey">developmental survey</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20stereo%20matching" title=" local stereo matching"> local stereo matching</a>, <a href="https://publications.waset.org/abstracts/search?q=rectification" title=" rectification"> rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20correspondence" title=" stereo correspondence"> stereo correspondence</a> </p> <a href="https://publications.waset.org/abstracts/49461/a-developmental-survey-of-local-stereo-matching-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/49461.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">293</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2001</span> Nazca: A Context-Based Matching Method for Searching Heterogeneous Structures</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Karine%20B.%20de%20Oliveira">Karine B. de Oliveira</a>, <a href="https://publications.waset.org/abstracts/search?q=Carina%20F.%20Dorneles"> Carina F. Dorneles</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The structure level matching is the problem of combining elements of a structure, which can be represented as entities, classes, XML elements, web forms, and so on. This is a challenge due to large number of distinct representations of semantically similar structures. This paper describes a structure-based matching method applied to search for different representations in data sources, considering the similarity between elements of two structures and the data source context. Using real data sources, we have conducted an experimental study comparing our approach with our baseline implementation and with another important schema matching approach. We demonstrate that our proposal reaches higher precision than the baseline. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=context" title="context">context</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20source" title=" data source"> data source</a>, <a href="https://publications.waset.org/abstracts/search?q=index" title=" index"> index</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=search" title=" search"> search</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=structure" title=" structure"> structure</a> </p> <a href="https://publications.waset.org/abstracts/4417/nazca-a-context-based-matching-method-for-searching-heterogeneous-structures" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4417.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">364</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2000</span> Seashore Debris Detection System Using Deep Learning and Histogram of Gradients-Extractor Based Instance Segmentation Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Anshika%20Kankane">Anshika Kankane</a>, <a href="https://publications.waset.org/abstracts/search?q=Dongshik%20Kang"> Dongshik Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Marine debris has a significant influence on coastal environments, damaging biodiversity, and causing loss and damage to marine and ocean sector. A functional cost-effective and automatic approach has been used to look up at this problem. Computer vision combined with a deep learning-based model is being proposed to identify and categorize marine debris of seven kinds on different beach locations of Japan. This research compares state-of-the-art deep learning models with a suggested model architecture that is utilized as a feature extractor for debris categorization. The model is being proposed to detect seven categories of litter using a manually constructed debris dataset, with the help of Mask R-CNN for instance segmentation and a shape matching network called HOGShape, which can then be cleaned on time by clean-up organizations using warning notifications of the system. The manually constructed dataset for this system is created by annotating the images taken by fixed KaKaXi camera using CVAT annotation tool with seven kinds of category labels. A pre-trained HOG feature extractor on LIBSVM is being used along with multiple templates matching on HOG maps of images and HOG maps of templates to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the warning notifications using live recorded beach debris data. The suggested network results in the improvement of misclassified debris masks of debris objects with different illuminations, shapes, viewpoints and litter with occlusions which have vague visibility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=debris" title=" debris"> debris</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20live%20camera%20images" title=" fixed live camera images"> fixed live camera images</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20of%20gradients%20feature%20extractor" title=" histogram of gradients feature extractor"> histogram of gradients feature extractor</a>, <a href="https://publications.waset.org/abstracts/search?q=instance%20segmentation" title=" instance segmentation"> instance segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=manually%20annotated%20dataset" title=" manually annotated dataset"> manually annotated dataset</a>, <a href="https://publications.waset.org/abstracts/search?q=multiple%20template%20matching" title=" multiple template matching"> multiple template matching</a> </p> <a href="https://publications.waset.org/abstracts/150676/seashore-debris-detection-system-using-deep-learning-and-histogram-of-gradients-extractor-based-instance-segmentation-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/150676.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">106</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1999</span> The Hospitals Residents Problem with Bounded Length Preference List under Social Stability</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashish%20Shrivastava">Ashish Shrivastava</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Pandu%20Rangan"> C. Pandu Rangan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we consider The Hospitals Residents problem with Social Stability (HRSS), where hospitals and residents can communicate only through the underlying social network. Those residents and hospitals which don not have any social connection between them can not communicate and hence they cannot be a social blocking pair with respect to a socially stable matching in an instance of hospitals residents problem with social stability. In large scale matching like NRMP or Scottish medical matching scheme etc. where set of agents, as well as length of preference lists, are very large, social stability is a useful notion in which members of a blocking pair could block a matching if and only if they know the existence of each other. Thus the notion of social stability in hospitals residents problem allows us to increase the cardinality of the matching without taking care of those blocking pairs which are not socially connected to each other. We know that finding a maximum cardinality socially stable matching, in an instance, of HRSS is NP-hard. This motivates us to solve this problem with bounded length preference lists on one side. In this paper, we have presented a polynomial time algorithm to compute maximum cardinality socially stable matching in a HRSS instance where residents can give at most two length and hospitals can give unbounded length preference list. Preference lists of residents and hospitals will be strict in nature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=matching%20under%20preference" title="matching under preference">matching under preference</a>, <a href="https://publications.waset.org/abstracts/search?q=socially%20stable%20matching" title=" socially stable matching"> socially stable matching</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20hospital%20residents%20problem" title=" the hospital residents problem"> the hospital residents problem</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20stable%20marriage%20problem" title=" the stable marriage problem"> the stable marriage problem</a> </p> <a href="https://publications.waset.org/abstracts/57888/the-hospitals-residents-problem-with-bounded-length-preference-list-under-social-stability" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57888.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">277</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1998</span> A Study of ZY3 Satellite Digital Elevation Model Verification and Refinement with Shuttle Radar Topography Mission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wang">Bo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ZY-3%20satellite%20imagery" title="ZY-3 satellite imagery">ZY-3 satellite imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=DEM" title=" DEM"> DEM</a>, <a href="https://publications.waset.org/abstracts/search?q=SRTM" title=" SRTM"> SRTM</a>, <a href="https://publications.waset.org/abstracts/search?q=refinement" title=" refinement"> refinement</a> </p> <a href="https://publications.waset.org/abstracts/76112/a-study-of-zy3-satellite-digital-elevation-model-verification-and-refinement-with-shuttle-radar-topography-mission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1997</span> Evaluation of the Matching Optimization of Human-Machine Interface Matching in the Cab</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanhua%20Ma">Yanhua Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhai"> Lu Zhai</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinchen%20Wang"> Xinchen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Liang"> Hongyu Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20hierarchy%20process" title="analytic hierarchy process">analytic hierarchy process</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20comprehension%20evaluation%20method" title=" fuzzy comprehension evaluation method"> fuzzy comprehension evaluation method</a>, <a href="https://publications.waset.org/abstracts/search?q=human-machine%20interface" title=" human-machine interface"> human-machine interface</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20optimization" title=" matching optimization"> matching optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20human%20factor%20analysis" title=" software human factor analysis"> software human factor analysis</a> </p> <a href="https://publications.waset.org/abstracts/131104/evaluation-of-the-matching-optimization-of-human-machine-interface-matching-in-the-cab" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131104.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1996</span> Hybrid Approximate Structural-Semantic Frequent Subgraph Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Montaceur%20Zaghdoud">Montaceur Zaghdoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Moussaoui"> Mohamed Moussaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20graph%20matching" title="approximate graph matching">approximate graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20frequent%20subgraph%20mining" title=" hybrid frequent subgraph mining"> hybrid frequent subgraph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20mining" title=" graph mining"> graph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/34195/hybrid-approximate-structural-semantic-frequent-subgraph-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">402</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1995</span> A Practical and Efficient Evaluation Function for 3D Model Based Vehicle Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan%20Zheng">Yuan Zheng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> 3D model-based vehicle matching provides a new way for vehicle recognition, localization and tracking. Its key is to construct an evaluation function, also called fitness function, to measure the degree of vehicle matching. The existing fitness functions often poorly perform when the clutter and occlusion exist in traffic scenarios. In this paper, we present a practical and efficient fitness function. Unlike the existing evaluation functions, the proposed fitness function is to study the vehicle matching problem from both local and global perspectives, which exploits the pixel gradient information as well as the silhouette information. In view of the discrepancy between 3D vehicle model and real vehicle, a weighting strategy is introduced to differently treat the fitting of the model’s wireframes. Additionally, a normalization operation for the model’s projection is performed to improve the accuracy of the matching. Experimental results on real traffic videos reveal that the proposed fitness function is efficient and robust to the cluttered background and partial occlusion. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D-2D%20matching" title="3D-2D matching">3D-2D matching</a>, <a href="https://publications.waset.org/abstracts/search?q=fitness%20function" title=" fitness function"> fitness function</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20vehicle%20model" title=" 3D vehicle model"> 3D vehicle model</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20image%20gradient" title=" local image gradient"> local image gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=silhouette%20information" title=" silhouette information"> silhouette information</a> </p> <a href="https://publications.waset.org/abstracts/45357/a-practical-and-efficient-evaluation-function-for-3d-model-based-vehicle-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45357.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">399</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1994</span> On Phase Based Stereo Matching and Its Related Issues</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andr%C3%A1s%20R%C3%B6vid">András Rövid</a>, <a href="https://publications.waset.org/abstracts/search?q=Takeshi%20Hashimoto"> Takeshi Hashimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=stereo%20matching" title="stereo matching">stereo matching</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-pixel%20accuracy" title=" sub-pixel accuracy"> sub-pixel accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=phase%20correlation" title=" phase correlation"> phase correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=SVD" title=" SVD"> SVD</a>, <a href="https://publications.waset.org/abstracts/search?q=NSSD" title=" NSSD"> NSSD</a> </p> <a href="https://publications.waset.org/abstracts/8549/on-phase-based-stereo-matching-and-its-related-issues" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8549.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">468</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1993</span> Chinese Event Detection Technique Based on Dependency Parsing and Rule Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weitao%20Lin">Weitao Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese%20event%20detection" title=" Chinese event detection"> Chinese event detection</a>, <a href="https://publications.waset.org/abstracts/search?q=rules%20matching" title=" rules matching"> rules matching</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20parsing" title=" dependency parsing"> dependency parsing</a> </p> <a href="https://publications.waset.org/abstracts/158129/chinese-event-detection-technique-based-on-dependency-parsing-and-rule-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158129.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1992</span> Design and Implementation of Partial Denoising Boundary Image Matching Using Indexing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bum-Soo%20Kim">Bum-Soo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jin-Uk%20Kim"> Jin-Uk Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we design and implement a partial denoising boundary image matching system using indexing techniques. Converting boundary images to time-series makes it feasible to perform fast search using indexes even on a very large image database. Thus, using this converting method we develop a client-server system based on the previous partial denoising research in the GUI (graphical user interface) environment. The client first converts a query image given by a user to a time-series and sends denoising parameters and the tolerance with this time-series to the server. The server identifies similar images from the index by evaluating a range query, which is constructed using inputs given from the client, and sends the resulting images to the client. Experimental results show that our system provides much intuitive and accurate matching result. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=boundary%20image%20matching" title="boundary image matching">boundary image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=indexing" title=" indexing"> indexing</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20denoising" title=" partial denoising"> partial denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=time-series%20matching" title=" time-series matching"> time-series matching</a> </p> <a href="https://publications.waset.org/abstracts/97170/design-and-implementation-of-partial-denoising-boundary-image-matching-using-indexing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97170.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">137</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1991</span> The Effect of Feature Selection on Pattern Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chih-Fong%20Tsai">Chih-Fong Tsai</a>, <a href="https://publications.waset.org/abstracts/search?q=Ya-Han%20Hu"> Ya-Han Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=data%20mining" title="data mining">data mining</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=pattern%20classification" title=" pattern classification"> pattern classification</a>, <a href="https://publications.waset.org/abstracts/search?q=dimensionality%20reduction" title=" dimensionality reduction"> dimensionality reduction</a> </p> <a href="https://publications.waset.org/abstracts/5047/the-effect-of-feature-selection-on-pattern-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5047.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">669</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feature%20matching&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feature%20matching&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feature%20matching&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feature%20matching&page=5">5</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=feature%20matching&page=6">6</a></li> <li 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