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style="font-size:.9rem"><span class="badge badge-info">2308</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">2307</span> Comparative Analysis of Dissimilarity Detection between Binary Images Based on Equivalency and Non-Equivalency of Image Inversion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20A.%20Y.%20Mustafa">Adnan A. Y. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20image" title="binary image">binary image</a>, <a href="https://publications.waset.org/abstracts/search?q=dissimilarity%20detection" title=" dissimilarity detection"> dissimilarity detection</a>, <a href="https://publications.waset.org/abstracts/search?q=probabilistic%20matching%20model%20for%20binary%20images" title=" probabilistic matching model for binary images"> probabilistic matching model for binary images</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20mapping" title=" image mapping"> image mapping</a> </p> <a href="https://publications.waset.org/abstracts/113778/comparative-analysis-of-dissimilarity-detection-between-binary-images-based-on-equivalency-and-non-equivalency-of-image-inversion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/113778.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">153</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">2306</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">2305</span> Size Reduction of Images Using Constraint Optimization Approach for Machine Communications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chee%20Sun%20Won">Chee Sun Won</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents the size reduction of images for machine-to-machine communications. Here, the salient image regions to be preserved include the image patches of the key-points such as corners and blobs. Based on a saliency image map from the key-points and their image patches, an axis-aligned grid-size optimization is proposed for the reduction of image size. To increase the size-reduction efficiency the aspect ratio constraint is relaxed in the constraint optimization framework. The proposed method yields higher matching accuracy after the size reduction than the conventional content-aware image size-reduction methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20compression" title="image compression">image compression</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=key-point%20detection%20and%20description" title=" key-point detection and description"> key-point detection and description</a>, <a href="https://publications.waset.org/abstracts/search?q=machine-to-machine%20communication" title=" machine-to-machine communication"> machine-to-machine communication</a> </p> <a href="https://publications.waset.org/abstracts/67605/size-reduction-of-images-using-constraint-optimization-approach-for-machine-communications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67605.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">418</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">2304</span> Quick Similarity Measurement of Binary Images via Probabilistic Pixel Mapping</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adnan%20A.%20Y.%20Mustafa">Adnan A. Y. Mustafa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a quick technique to measure the similarity between binary images. The technique is based on a probabilistic mapping approach and is fast because only a minute percentage of the image pixels need to be compared to measure the similarity, and not the whole image. We exploit the power of the Probabilistic Matching Model for Binary Images (PMMBI) to arrive at an estimate of the similarity. We show that the estimate is a good approximation of the actual value, and the quality of the estimate can be improved further with increased image mappings. Furthermore, the technique is image size invariant; the similarity between big images can be measured as fast as that for small images. Examples of trials conducted on real images are presented. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20images" title="big images">big images</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20images" title=" binary images"> binary images</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=image%20similarity" title=" image similarity"> image similarity</a> </p> <a href="https://publications.waset.org/abstracts/89963/quick-similarity-measurement-of-binary-images-via-probabilistic-pixel-mapping" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/89963.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">196</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">2303</span> A Comparison of Image Data Representations for Local Stereo Matching</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> The stereo matching problem, while having been present for several decades, continues to be an active area of research. The goal of this research is to find correspondences between elements found in a set of stereoscopic images. With these pairings, it is possible to infer the distance of objects within a scene, relative to the observer. Advancements in this field have led to experimentations with various techniques, from graph-cut energy minimization to artificial neural networks. At the basis of these techniques is a cost function, which is used to evaluate the likelihood of a particular match between points in each image. While at its core, the cost is based on comparing the image pixel data; there is a general lack of consistency as to what image data representation to use. This paper presents an experimental analysis to compare the effectiveness of more common image data representations. The goal is to determine the effectiveness of these data representations to reduce the cost for the correct correspondence relative to other possible matches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colour%20data" title="colour data">colour data</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=stereo%20correspondence" title=" stereo correspondence"> stereo correspondence</a>, <a href="https://publications.waset.org/abstracts/search?q=disparity%20map" title=" disparity map"> disparity map</a> </p> <a href="https://publications.waset.org/abstracts/68197/a-comparison-of-image-data-representations-for-local-stereo-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/68197.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">370</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">2302</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">2301</span> Biimodal Biometrics System Using Fusion of Iris and Fingerprint</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Attallah%20Bilal">Attallah Bilal</a>, <a href="https://publications.waset.org/abstracts/search?q=Hendel%20Fatiha"> Hendel Fatiha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper proposes the bimodal biometrics system for identity verification iris and fingerprint, at matching score level architecture using weighted sum of score technique. The features are extracted from the pre processed images of iris and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on CASIA database and gives an overall accuracy of 91.04% with FAR of 2.58% and FRR of 8.34%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris" title="iris">iris</a>, <a href="https://publications.waset.org/abstracts/search?q=fingerprint" title=" fingerprint"> fingerprint</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20rule" title=" sum rule"> sum rule</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion" title=" fusion"> fusion</a> </p> <a href="https://publications.waset.org/abstracts/18556/biimodal-biometrics-system-using-fusion-of-iris-and-fingerprint" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18556.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">368</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">2300</span> Optimizing Machine Learning Through Python Based Image Processing Techniques</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Srinidhi.%20A">Srinidhi. A</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Twinkle%20Hareendran"> Twinkle Hareendran</a>, <a href="https://publications.waset.org/abstracts/search?q=Vriksha%20Prakash"> Vriksha Prakash</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work reviews some of the advanced image processing techniques for deep learning applications. Object detection by template matching, image denoising, edge detection, and super-resolution modelling are but a few of the tasks. The paper looks in into great detail, given that such tasks are crucial preprocessing steps that increase the quality and usability of image datasets in subsequent deep learning tasks. We review some of the methods for the assessment of image quality, more specifically sharpness, which is crucial to ensure a robust performance of models. Further, we will discuss the development of deep learning models specific to facial emotion detection, age classification, and gender classification, which essentially includes the preprocessing techniques interrelated with model performance. Conclusions from this study pinpoint the best practices in the preparation of image datasets, targeting the best trade-off between computational efficiency and retaining important image features critical for effective training of deep learning models. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning%20applications" title=" machine learning applications"> machine learning applications</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a>, <a href="https://publications.waset.org/abstracts/search?q=emotion%20detection" title=" emotion detection"> emotion detection</a> </p> <a href="https://publications.waset.org/abstracts/193107/optimizing-machine-learning-through-python-based-image-processing-techniques" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/193107.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">13</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">2299</span> Wavelet Coefficients Based on Orthogonal Matching Pursuit (OMP) Based Filtering for Remotely Sensed Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ramandeep%20Kaur">Ramandeep Kaur</a>, <a href="https://publications.waset.org/abstracts/search?q=Kamaljit%20Kaur"> Kamaljit Kaur</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, the technology of the remote sensing is growing rapidly. Image enhancement is one of most commonly used of image processing operations. Noise reduction plays very important role in digital image processing and various technologies have been located ahead to reduce the noise of the remote sensing images. The noise reduction using wavelet coefficients based on Orthogonal Matching Pursuit (OMP) has less consequences on the edges than available methods but this is not as establish in edge preservation techniques. So in this paper we provide a new technique minimum patch based noise reduction OMP which reduce the noise from an image and used edge preservation patch which preserve the edges of the image and presents the superior results than existing OMP technique. Experimental results show that the proposed minimum patch approach outperforms over existing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20denoising" title="image denoising">image denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20patch" title=" minimum patch"> minimum patch</a>, <a href="https://publications.waset.org/abstracts/search?q=OMP" title=" OMP"> OMP</a>, <a href="https://publications.waset.org/abstracts/search?q=WCOMP" title=" WCOMP"> WCOMP</a> </p> <a href="https://publications.waset.org/abstracts/59831/wavelet-coefficients-based-on-orthogonal-matching-pursuit-omp-based-filtering-for-remotely-sensed-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59831.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">2298</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">2297</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">2296</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">2295</span> Speeding-up Gray-Scale FIC by Moments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Eman%20A.%20Al-Hilo">Eman A. Al-Hilo</a>, <a href="https://publications.waset.org/abstracts/search?q=Hawraa%20H.%20Al-Waelly"> Hawraa H. Al-Waelly</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, fractal compression (FIC) technique is introduced based on using moment features to block indexing the zero-mean range-domain blocks. The moment features have been used to speed up the IFS-matching stage. Its moments ratio descriptor is used to filter the domain blocks and keep only the blocks that are suitable to be IFS matched with tested range block. The results of tests conducted on Lena picture and Cat picture (256 pixels, resolution 24 bits/pixel) image showed a minimum encoding time (0.89 sec for Lena image and 0.78 of Cat image) with appropriate PSNR (30.01dB for Lena image and 29.8 of Cat image). The reduction in ET is about 12% for Lena and 67% for Cat image. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=fractal%20gray%20level%20image" title="fractal gray level image">fractal gray level image</a>, <a href="https://publications.waset.org/abstracts/search?q=fractal%20compression%20technique" title=" fractal compression technique"> fractal compression technique</a>, <a href="https://publications.waset.org/abstracts/search?q=iterated%20function%20system" title=" iterated function system"> iterated function system</a>, <a href="https://publications.waset.org/abstracts/search?q=moments%20feature" title=" moments feature"> moments feature</a>, <a href="https://publications.waset.org/abstracts/search?q=zero-mean%20range-domain%20block" title=" zero-mean range-domain block"> zero-mean range-domain block</a> </p> <a href="https://publications.waset.org/abstracts/19903/speeding-up-gray-scale-fic-by-moments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19903.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">492</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">2294</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">2293</span> Application of a Universal Distortion Correction Method in Stereo-Based Digital Image Correlation Measurement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hu%20Zhenxing">Hu Zhenxing</a>, <a href="https://publications.waset.org/abstracts/search?q=Gao%20Jianxin"> Gao Jianxin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=distortion" title="distortion">distortion</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo-based%20digital%20image%20correlation" title=" stereo-based digital image correlation"> stereo-based digital image correlation</a>, <a href="https://publications.waset.org/abstracts/search?q=b-spline" title=" b-spline"> b-spline</a>, <a href="https://publications.waset.org/abstracts/search?q=3D" title=" 3D"> 3D</a>, <a href="https://publications.waset.org/abstracts/search?q=2D" title=" 2D "> 2D </a> </p> <a href="https://publications.waset.org/abstracts/20547/application-of-a-universal-distortion-correction-method-in-stereo-based-digital-image-correlation-measurement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20547.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">498</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">2292</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">2291</span> New Features for Copy-Move Image Forgery Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Michael%20Zimba">Michael Zimba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A novel set of features for copy-move image forgery, CMIF, detection method is proposed. The proposed set presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilised to extract robust features. The extracted features are shown to be invariant to additive noise, JPEG compression, and affine transformation. The proposed features can also be used in general object matching. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=virtual%20electrostatic%20field" title="virtual electrostatic field">virtual electrostatic field</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=affine%20transformation" title=" affine transformation"> affine transformation</a>, <a href="https://publications.waset.org/abstracts/search?q=copy-move%20image%20forgery" title=" copy-move image forgery"> copy-move image forgery</a> </p> <a href="https://publications.waset.org/abstracts/29604/new-features-for-copy-move-image-forgery-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29604.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">543</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">2290</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">2289</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">2288</span> Spatial Object-Oriented Template Matching Algorithm Using Normalized Cross-Correlation Criterion for Tracking Aerial Image Scene</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jigg%20Pelayo">Jigg Pelayo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Villar"> Ricardo Villar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithm" title="algorithm">algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=LiDAR" title=" LiDAR"> LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=OBIA" title=" OBIA"> OBIA</a> </p> <a href="https://publications.waset.org/abstracts/58677/spatial-object-oriented-template-matching-algorithm-using-normalized-cross-correlation-criterion-for-tracking-aerial-image-scene" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58677.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">244</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">2287</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">2286</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">2285</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">2284</span> An Approach for Reducing Morphological Operator Dataset and Recognize Optical Character Based on Significant Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ashis%20Pradhan">Ashis Pradhan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohan%20P.%20Pradhan"> Mohan P. Pradhan </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pattern Matching is useful for recognizing character in a digital image. OCR is one such technique which reads character from a digital image and recognizes them. Line segmentation is initially used for identifying character in an image and later refined by morphological operations like binarization, erosion, thinning, etc. The work discusses a recognition technique that defines a set of morphological operators based on its orientation in a character. These operators are further categorized into groups having similar shape but different orientation for efficient utilization of memory. Finally the characters are recognized in accordance with the occurrence of frequency in hierarchy of significant pattern of those morphological operators and by comparing them with the existing database of each character. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binary%20image" title="binary image">binary image</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20patterns" title=" morphological patterns"> morphological patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20count" title=" frequency count"> frequency count</a>, <a href="https://publications.waset.org/abstracts/search?q=priority" title=" priority"> priority</a>, <a href="https://publications.waset.org/abstracts/search?q=reduction%20data%20set%20and%20recognition" title=" reduction data set and recognition"> reduction data set and recognition</a> </p> <a href="https://publications.waset.org/abstracts/30867/an-approach-for-reducing-morphological-operator-dataset-and-recognize-optical-character-based-on-significant-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/30867.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">414</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">2283</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">2282</span> Image Features Comparison-Based Position Estimation Method Using a Camera Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinseon%20Song">Jinseon Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongwan%20Park"> Yongwan Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=positioning" title="positioning">positioning</a>, <a href="https://publications.waset.org/abstracts/search?q=distance" title=" distance"> distance</a>, <a href="https://publications.waset.org/abstracts/search?q=camera" title=" camera"> camera</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF%28Speed-Up%20Robust%20Features%29" title=" SURF(Speed-Up Robust Features)"> SURF(Speed-Up Robust Features)</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a> </p> <a href="https://publications.waset.org/abstracts/11844/image-features-comparison-based-position-estimation-method-using-a-camera-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11844.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">349</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">2281</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">2280</span> A Trends Analysis of Yatch Simulator</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Neung%20Lee">Jae-Neung Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Keun-Chang%20Kwak"> Keun-Chang Kwak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=yacht%20simulator" title="yacht simulator">yacht simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=simulator" title=" simulator"> simulator</a>, <a href="https://publications.waset.org/abstracts/search?q=trends%20analysis" title=" trends analysis"> trends analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a> </p> <a href="https://publications.waset.org/abstracts/23888/a-trends-analysis-of-yatch-simulator" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23888.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">432</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">2279</span> A Deep Learning Based Approach for Dynamically Selecting Pre-processing Technique for Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Revoti%20Prasad%20Bora">Revoti Prasad Bora</a>, <a href="https://publications.waset.org/abstracts/search?q=Nikita%20Katyal"> Nikita Katyal</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabh%20Yadav"> Saurabh Yadav</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pre-processing plays an important role in various image processing applications. Most of the time due to the similar nature of images, a particular pre-processing or a set of pre-processing steps are sufficient to produce the desired results. However, in the education domain, there is a wide variety of images in various aspects like images with line-based diagrams, chemical formulas, mathematical equations, etc. Hence a single pre-processing or a set of pre-processing steps may not yield good results. Therefore, a Deep Learning based approach for dynamically selecting a relevant pre-processing technique for each image is proposed. The proposed method works as a classifier to detect hidden patterns in the images and predicts the relevant pre-processing technique needed for the image. This approach experimented for an image similarity matching problem but it can be adapted to other use cases too. Experimental results showed significant improvement in average similarity ranking with the proposed method as opposed to static pre-processing techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep-learning" title="deep-learning">deep-learning</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a>, <a href="https://publications.waset.org/abstracts/search?q=pre-processing" title=" pre-processing"> pre-processing</a>, <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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=educational%20data%20mining" title=" educational data mining"> educational data mining</a> </p> <a 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