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Aljutaili</a>, <a href="https://publications.waset.org/abstracts/search?q=Redna%20A.%20Almutlaq"> Redna A. Almutlaq</a>, <a href="https://publications.waset.org/abstracts/search?q=Suha%20A.%20Alharbi"> Suha A. Alharbi</a>, <a href="https://publications.waset.org/abstracts/search?q=Dina%20M.%20Ibrahim"> Dina M. Ibrahim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=currency%20recognition" title="currency recognition">currency recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20detection%20and%20description" title=" feature detection and description"> feature detection and description</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT%20algorithm" title=" SIFT algorithm"> SIFT algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF%20algorithm" title=" SURF algorithm"> SURF algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=speeded%20up%20and%20robust%20features" title=" speeded up and robust features"> speeded up and robust features</a> </p> <a href="https://publications.waset.org/abstracts/94315/a-speeded-up-robust-scale-invariant-feature-transform-currency-recognition-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/94315.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">239</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">3652</span> Fast and Scale-Adaptive Target Tracking via PCA-SIFT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yawen%20Wang">Yawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongchang%20Chen"> Hongchang Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Shaomei%20Li"> Shaomei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Gao"> Chao Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangpeng%20Zhang"> Jiangpeng Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=target%20tracking" title="target tracking">target tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA-SIFT" title=" PCA-SIFT"> PCA-SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=mean-shift" title=" mean-shift"> mean-shift</a>, <a href="https://publications.waset.org/abstracts/search?q=scale-adaptive" title=" scale-adaptive"> scale-adaptive</a> </p> <a href="https://publications.waset.org/abstracts/19009/fast-and-scale-adaptive-target-tracking-via-pca-sift" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19009.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">436</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">3651</span> Using Scale Invariant Feature Transform Features to Recognize Characters in Natural Scene Images </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Belaynesh%20Chekol">Belaynesh Chekol</a>, <a href="https://publications.waset.org/abstracts/search?q=Numan%20%C3%87elebi"> Numan Çelebi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN. <p class="card-text"><strong>Keywords:</strong> <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=KNN" title=" KNN"> KNN</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20scene%20image" title=" natural scene image"> natural scene image</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a> </p> <a href="https://publications.waset.org/abstracts/58580/using-scale-invariant-feature-transform-features-to-recognize-characters-in-natural-scene-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58580.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">285</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">3650</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">477</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">3649</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">439</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">3648</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">318</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">3647</span> Classifications of Images for the Recognition of People’s Behaviors by SIFT and SVM</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Henni%20Sid%20Ahmed">Henni Sid Ahmed</a>, <a href="https://publications.waset.org/abstracts/search?q=Belbachir%20Mohamed%20Faouzi"> Belbachir Mohamed Faouzi</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean%20Caelen"> Jean Caelen </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20analysis" title="video analysis">video analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=people%20behavior" title=" people behavior"> people behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20building" title=" intelligent building"> intelligent building</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification "> classification </a> </p> <a href="https://publications.waset.org/abstracts/24738/classifications-of-images-for-the-recognition-of-peoples-behaviors-by-sift-and-svm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24738.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">380</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">3646</span> Implementation of a Multimodal Biometrics Recognition System with Combined Palm Print and Iris Features</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rabab%20M.%20Ramadan">Rabab M. Ramadan</a>, <a href="https://publications.waset.org/abstracts/search?q=Elaraby%20A.%20Elgallad"> Elaraby A. Elgallad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=iris%20recognition" title="iris recognition">iris recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=particle%20swarm%20optimization" title=" particle swarm optimization"> particle swarm optimization</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=feature%20selection" title=" feature selection"> feature selection</a>, <a href="https://publications.waset.org/abstracts/search?q=palm%20print" title=" palm print"> palm print</a>, <a href="https://publications.waset.org/abstracts/search?q=the%20Scale%20Invariant%20Feature%20Transform%20%28SIFT%29" title=" the Scale Invariant Feature Transform (SIFT)"> the Scale Invariant Feature Transform (SIFT)</a> </p> <a href="https://publications.waset.org/abstracts/90535/implementation-of-a-multimodal-biometrics-recognition-system-with-combined-palm-print-and-iris-features" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90535.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">241</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">3645</span> Change Detection Method Based on Scale-Invariant Feature Transformation Keypoints and Segmentation for Synthetic Aperture Radar Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lan%20Du">Lan Du</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Wang"> Yan Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hui%20Dai"> Hui Dai</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=change%20detection" title="change detection">change detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Synthetic%20Aperture%20Radar%20%28SAR%29" title=" Synthetic Aperture Radar (SAR)"> Synthetic Aperture Radar (SAR)</a>, <a href="https://publications.waset.org/abstracts/search?q=Scale-Invariant%20Feature%20Transformation%20%28SIFT%29" title=" Scale-Invariant Feature Transformation (SIFT)"> Scale-Invariant Feature Transformation (SIFT)</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/66992/change-detection-method-based-on-scale-invariant-feature-transformation-keypoints-and-segmentation-for-synthetic-aperture-radar-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66992.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">394</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">3644</span> Adaptive Online Object Tracking via Positive and Negative Models Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaomei%20Li">Shaomei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yawen%20Wang"> Yawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Gao"> Chao Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title="object tracking">object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking%20drift" title=" tracking drift"> tracking drift</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20analysis" title=" partial least squares analysis"> partial least squares analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20models%20matching" title=" positive and negative models matching"> positive and negative models matching</a> </p> <a href="https://publications.waset.org/abstracts/19382/adaptive-online-object-tracking-via-positive-and-negative-models-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19382.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">538</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">3643</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">362</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">3642</span> An Experiment of Three-Dimensional Point Clouds Using GoPro</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jong-Hwa%20Kim">Jong-Hwa Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Mu-Wook%20Pyeon"> Mu-Wook Pyeon</a>, <a href="https://publications.waset.org/abstracts/search?q=Yang-dam%20Eo"> Yang-dam Eo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ill-Woong%20Jang"> Ill-Woong Jang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Construction of geo-spatial information recently tends to develop as multi-dimensional geo-spatial information. People constructing spatial information is also expanding its area to the general public from some experts. As well as, studies are in progress using a variety of devices, with the aim of near real-time update. In this paper, getting the stereo images using GoPro device used widely also to the general public as well as experts. And correcting the distortion of the images, then by using SIFT, DLT, is acquired the point clouds. It presented a possibility that on the basis of this experiment, using a video device that is readily available in real life, to create a real-time digital map. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GoPro" title="GoPro">GoPro</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=DLT" title=" DLT"> DLT</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20clouds" title=" point clouds"> point clouds</a> </p> <a href="https://publications.waset.org/abstracts/5342/an-experiment-of-three-dimensional-point-clouds-using-gopro" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/5342.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">474</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">3641</span> Modern Detection and Description Methods for Natural Plants Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Masoud%20Fathi%20Kazerouni">Masoud Fathi Kazerouni</a>, <a href="https://publications.waset.org/abstracts/search?q=Jens%20Schlemper"> Jens Schlemper</a>, <a href="https://publications.waset.org/abstracts/search?q=Klaus-Dieter%20Kuhnert"> Klaus-Dieter Kuhnert</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SIFT%20combination" title="SIFT combination">SIFT combination</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=feature%20detection" title=" feature detection"> feature detection</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20images" title=" natural images"> natural images</a>, <a href="https://publications.waset.org/abstracts/search?q=natural%20plant%20recognition" title=" natural plant recognition"> natural plant recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=HARRIS-SIFT" title=" HARRIS-SIFT"> HARRIS-SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=FAST-SIFT" title=" FAST-SIFT"> FAST-SIFT</a> </p> <a href="https://publications.waset.org/abstracts/64236/modern-detection-and-description-methods-for-natural-plants-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/64236.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">285</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">3640</span> Bag of Words Representation Based on Weighting Useful Visual Words</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The most effective and efficient methods in image categorization are almost based on bag-of-words (BOW) which presents image by a histogram of occurrence of visual words. In this paper, we propose a novel extension to this method. Firstly, we extract features in multi-scales by applying a color local descriptor named opponent-SIFT. Secondly, in order to represent image we use Spatial Pyramid Representation (SPR) and an extension to the BOW method which based on weighting visual words. Typically, the visual words are weighted during histogram assignment by computing the ratio of their occurrences in the image to the occurrences in the background. Finally, according to classical BOW retrieval framework, only a few words of the vocabulary is useful for image representation. Therefore, we select the useful weighted visual words that respect the threshold value. Experimentally, the algorithm is tested by using different image classes of PASCAL VOC 2007 and is compared against the classical bag-of-visual-words algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BOW" title="BOW">BOW</a>, <a href="https://publications.waset.org/abstracts/search?q=useful%20visual%20words" title=" useful visual words"> useful visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=weighted%20visual%20words" title=" weighted visual words"> weighted visual words</a>, <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20visual%20words" title=" bag of visual words"> bag of visual words</a> </p> <a href="https://publications.waset.org/abstracts/14009/bag-of-words-representation-based-on-weighting-useful-visual-words" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14009.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">440</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">3639</span> Face Sketch Recognition in Forensic Application Using Scale Invariant Feature Transform and Multiscale Local Binary Patterns Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gargi%20Phadke">Gargi Phadke</a>, <a href="https://publications.waset.org/abstracts/search?q=Mugdha%20Joshi"> Mugdha Joshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Shamal%20Salunkhe"> Shamal Salunkhe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Facial sketches are used as a crucial clue by criminal investigators for identification of suspects when the description of eyewitness or victims are only available as evidence. A forensic artist develops a sketch as per the verbal description is given by an eyewitness that shows the facial look of the culprit. In this paper, the fusion of Scale Invariant Feature Transform (SIFT) and multiscale local binary patterns (MLBP) are proposed as a feature to recognize a forensic face sketch images from a gallery of mugshot photos. This work focuses on comparative analysis of proposed scheme with existing algorithms in different challenges like illumination change and rotation condition. Experimental results show that proposed scheme can lead to better performance for the defined problem. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SIFT%20feature" title="SIFT feature">SIFT feature</a>, <a href="https://publications.waset.org/abstracts/search?q=MLBP" title=" MLBP"> MLBP</a>, <a href="https://publications.waset.org/abstracts/search?q=PCA" title=" PCA"> PCA</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20sketch" title=" face sketch"> face sketch</a> </p> <a href="https://publications.waset.org/abstracts/85747/face-sketch-recognition-in-forensic-application-using-scale-invariant-feature-transform-and-multiscale-local-binary-patterns-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85747.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">344</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">3638</span> Co-Evolutionary Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Unconstrained Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20M.%20Rizk-Allah">R. M. Rizk-Allah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fruit%20fly%20optimization%20algorithm" title=" fruit fly optimization algorithm"> fruit fly optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=unconstrained%20optimization%20problems" title=" unconstrained optimization problems"> unconstrained optimization problems</a> </p> <a href="https://publications.waset.org/abstracts/15923/co-evolutionary-fruit-fly-optimization-algorithm-and-firefly-algorithm-for-solving-unconstrained-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/15923.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">542</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">3637</span> Evaluation of Robust Feature Descriptors for Texture Classification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jia-Hong%20Lee">Jia-Hong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Mei-Yi%20Wu"> Mei-Yi Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsien-Tsung%20Kuo"> Hsien-Tsung Kuo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=texture%20classification" title="texture classification">texture classification</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptor" title=" texture descriptor"> texture descriptor</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF" title=" SURF"> SURF</a>, <a href="https://publications.waset.org/abstracts/search?q=ORB" title=" ORB"> ORB</a> </p> <a href="https://publications.waset.org/abstracts/11046/evaluation-of-robust-feature-descriptors-for-texture-classification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11046.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">375</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">3636</span> A Hybrid Multi-Objective Firefly-Sine Cosine Algorithm for Multi-Objective Optimization Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gaohuizi%20Guo">Gaohuizi Guo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ning%20Zhang"> Ning Zhang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Firefly algorithm (FA) and Sine Cosine algorithm (SCA) are two very popular and advanced metaheuristic algorithms. However, these algorithms applied to multi-objective optimization problems have some shortcomings, respectively, such as premature convergence and limited exploration capability. Combining the privileges of FA and SCA while avoiding their deficiencies may improve the accuracy and efficiency of the algorithm. This paper proposes a hybridization of FA and SCA algorithms, named multi-objective firefly-sine cosine algorithm (MFA-SCA), to develop a more efficient meta-heuristic algorithm than FA and SCA. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=firefly%20algorithm" title="firefly algorithm">firefly algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20algorithm" title=" hybrid algorithm"> hybrid algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=sine%20cosine%20algorithm" title=" sine cosine algorithm"> sine cosine algorithm</a> </p> <a href="https://publications.waset.org/abstracts/129731/a-hybrid-multi-objective-firefly-sine-cosine-algorithm-for-multi-objective-optimization-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129731.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">173</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">3635</span> Bag of Words Representation Based on Fusing Two Color Local Descriptors and Building Multiple Dictionaries </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fatma%20Abdedayem">Fatma Abdedayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bag%20of%20words%20%28BOW%29" title="bag of words (BOW)">bag of words (BOW)</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20descriptors" title=" color descriptors"> color descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-dictionaries" title=" multi-dictionaries"> multi-dictionaries</a>, <a href="https://publications.waset.org/abstracts/search?q=MDBoW" title=" MDBoW"> MDBoW</a> </p> <a href="https://publications.waset.org/abstracts/14637/bag-of-words-representation-based-on-fusing-two-color-local-descriptors-and-building-multiple-dictionaries" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14637.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">301</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">3634</span> Approximating Fixed Points by a Two-Step Iterative Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safeer%20Hussain%20Khan">Safeer Hussain Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a two-step iterative algorithm to prove a strong convergence result for approximating common fixed points of three contractive-like operators. Our algorithm basically generalizes an existing algorithm..Our iterative algorithm also contains two famous iterative algorithms: Mann iterative algorithm and Ishikawa iterative algorithm. Thus our result generalizes the corresponding results proved for the above three iterative algorithms to a class of more general operators. At the end, we remark that nothing prevents us to extend our result to the case of the iterative algorithm with error terms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=contractive-like%20operator" title="contractive-like operator">contractive-like operator</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=fixed%20point" title=" fixed point"> fixed point</a>, <a href="https://publications.waset.org/abstracts/search?q=strong%20convergence" title=" strong convergence"> strong convergence</a> </p> <a href="https://publications.waset.org/abstracts/10341/approximating-fixed-points-by-a-two-step-iterative-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/10341.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">555</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">3633</span> Object Detection Based on Plane Segmentation and Features Matching for a Service Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ant%C3%B3nio%20J.%20R.%20Neves">António J. R. Neves</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Garcia"> Rui Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Dias"> Paulo Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Alina%20Trifan"> Alina Trifan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature" title=" feature"> feature</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptors" title=" descriptors"> descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF" title=" SURF"> SURF</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20images" title=" depth images"> depth images</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20robots" title=" service robots"> service robots</a> </p> <a href="https://publications.waset.org/abstracts/39840/object-detection-based-on-plane-segmentation-and-features-matching-for-a-service-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39840.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">550</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">3632</span> An Algorithm to Compute the State Estimation of a Bilinear Dynamical Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Eqal%20Al%20Mazrooei">Abdullah Eqal Al Mazrooei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce a mathematical algorithm which is used for estimating the states in the bilinear systems. This algorithm uses a special linearization of the second-order term by using the best available information about the state of the system. This technique makes our algorithm generalizes the well-known Kalman estimators. The system which is used here is of the bilinear class, the evolution of this model is linear-bilinear in the state of the system. Our algorithm can be used with linear and bilinear systems. We also here introduced a real application for the new algorithm to prove the feasibility and the efficiency for it. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=estimation%20algorithm" title="estimation algorithm">estimation algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bilinear%20systems" title=" bilinear systems"> bilinear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=Kakman%20filter" title=" Kakman filter"> Kakman filter</a>, <a href="https://publications.waset.org/abstracts/search?q=second%20order%20linearization" title=" second order linearization"> second order linearization</a> </p> <a href="https://publications.waset.org/abstracts/51466/an-algorithm-to-compute-the-state-estimation-of-a-bilinear-dynamical-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51466.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">489</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">3631</span> Handshake Algorithm for Minimum Spanning Tree Construction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nassiri%20Khalid">Nassiri Khalid</a>, <a href="https://publications.waset.org/abstracts/search?q=El%20Hibaoui%20Abdelaaziz%20et%20Hajar%20Moha"> El Hibaoui Abdelaaziz et Hajar Moha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we introduce and analyse a probabilistic distributed algorithm for a construction of a minimum spanning tree on network. This algorithm is based on the handshake concept. Firstly, each network node is considered as a sub-spanning tree. And at each round of the execution of our algorithm, a sub-spanning trees are merged. The execution continues until all sub-spanning trees are merged into one. We analyze this algorithm by a stochastic process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Spanning%20tree" title="Spanning tree">Spanning tree</a>, <a href="https://publications.waset.org/abstracts/search?q=Distributed%20Algorithm" title=" Distributed Algorithm"> Distributed Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Handshake%20Algorithm" title=" Handshake Algorithm"> Handshake Algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=Matching" title=" Matching"> Matching</a>, <a href="https://publications.waset.org/abstracts/search?q=Probabilistic%20Analysis" title=" Probabilistic Analysis"> Probabilistic Analysis</a> </p> <a href="https://publications.waset.org/abstracts/17743/handshake-algorithm-for-minimum-spanning-tree-construction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17743.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">665</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">3630</span> Digestion Optimization Algorithm: A Novel Bio-Inspired Intelligence for Global Optimization Problems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Akintayo%20E.%20Akinsunmade">Akintayo E. Akinsunmade</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The digestion optimization algorithm is a novel biological-inspired metaheuristic method for solving complex optimization problems. The algorithm development was inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to eﬀectively and eﬃciently search for optimal solutions. This algorithm was tested for optimal solutions on seven diﬀerent types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solution of the test functions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bio-inspired%20algorithm" title="bio-inspired algorithm">bio-inspired algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=benchmark%20optimization%20functions" title=" benchmark optimization functions"> benchmark optimization functions</a>, <a href="https://publications.waset.org/abstracts/search?q=digestive%20system%20in%20human" title=" digestive system in human"> digestive system in human</a>, <a href="https://publications.waset.org/abstracts/search?q=algorithm%20development" title=" algorithm development"> algorithm development</a> </p> <a href="https://publications.waset.org/abstracts/194133/digestion-optimization-algorithm-a-novel-bio-inspired-intelligence-for-global-optimization-problems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/194133.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">25</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">3629</span> Improving the Performance of Back-Propagation Training Algorithm by Using ANN</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vishnu%20Pratap%20Singh%20Kirar">Vishnu Pratap Singh Kirar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Artificial Neural Network (ANN) can be trained using backpropagation (BP). It is the most widely used algorithm for supervised learning with multi-layered feed-forward networks. Efficient learning by the BP algorithm is required for many practical applications. The BP algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a two-term algorithm consisting of a learning rate (LR) and a momentum factor (MF). The major drawbacks of the two-term BP learning algorithm are the problems of local minima and slow convergence speeds, which limit the scope for real-time applications. Recently the addition of an extra term, called a proportional factor (PF), to the two-term BP algorithm was proposed. The third increases the speed of the BP algorithm. However, the PF term also reduces the convergence of the BP algorithm, and criteria for evaluating convergence are required to facilitate the application of the three terms BP algorithm. Although these two seem to be closely related, as described later, we summarize various improvements to overcome the drawbacks. Here we compare the different methods of convergence of the new three-term BP algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title="neural network">neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=backpropagation" title=" backpropagation"> backpropagation</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20minima" title=" local minima"> local minima</a>, <a href="https://publications.waset.org/abstracts/search?q=fast%20convergence%20rate" title=" fast convergence rate"> fast convergence rate</a> </p> <a href="https://publications.waset.org/abstracts/22746/improving-the-performance-of-back-propagation-training-algorithm-by-using-ann" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/22746.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">505</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">3628</span> Tabu Random Algorithm for Guiding Mobile Robots</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kevin%20Worrall">Kevin Worrall</a>, <a href="https://publications.waset.org/abstracts/search?q=Euan%20McGookin"> Euan McGookin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of optimization algorithms is common across a large number of diverse fields. This work presents the use of a hybrid optimization algorithm applied to a mobile robot tasked with carrying out a search of an unknown environment. The algorithm is then applied to the multiple robots case, which results in a reduction in the time taken to carry out the search. The hybrid algorithm is a Random Search Algorithm fused with a Tabu mechanism. The work shows that the algorithm locates the desired points in a quicker time than a brute force search. The Tabu Random algorithm is shown to work within a simulated environment using a validated mathematical model. The simulation was run using three different environments with varying numbers of targets. As an algorithm, the Tabu Random is small, clear and can be implemented with minimal resources. The power of the algorithm is the speed at which it locates points of interest and the robustness to the number of robots involved. The number of robots can vary with no changes to the algorithm resulting in a flexible algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=algorithms" title="algorithms">algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-agent" title=" multi-agent"> multi-agent</a>, <a href="https://publications.waset.org/abstracts/search?q=search%20and%20rescue" title=" search and rescue"> search and rescue</a> </p> <a href="https://publications.waset.org/abstracts/92647/tabu-random-algorithm-for-guiding-mobile-robots" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92647.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">242</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">3627</span> Hybrid Bee Ant Colony Algorithm for Effective Load Balancing and Job Scheduling in Cloud Computing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Yeboah">Thomas Yeboah</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Cloud Computing is newly paradigm in computing that promises a delivery of computing as a service rather than a product, whereby shared resources, software, and information are provided to computers and other devices as a utility (like the electricity grid) over a network (typically the Internet). As Cloud Computing is a newly style of computing on the internet. It has many merits along with some crucial issues that need to be resolved in order to improve reliability of cloud environment. These issues are related with the load balancing, fault tolerance and different security issues in cloud environment.In this paper the main concern is to develop an effective load balancing algorithm that gives satisfactory performance to both, cloud users and providers. This proposed algorithm (hybrid Bee Ant Colony algorithm) is a combination of two dynamic algorithms: Ant Colony Optimization and Bees Life algorithm. Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues whiles the Bees Life algorithm is used for optimization of job scheduling in cloud environment. The results of the proposed algorithm shows that the hybrid Bee Ant Colony algorithm outperforms the performances of both Ant Colony algorithm and Bees Life algorithm when evaluated the proposed algorithm performances in terms of Waiting time and Response time on a simulator called CloudSim. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ant%20colony%20optimization%20algorithm" title="ant colony optimization algorithm">ant colony optimization algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=bees%20life%20algorithm" title=" bees life algorithm"> bees life algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=scheduling%20algorithm" title=" scheduling algorithm"> scheduling algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=performance" title=" performance"> performance</a>, <a href="https://publications.waset.org/abstracts/search?q=cloud%20computing" title=" cloud computing"> cloud computing</a>, <a href="https://publications.waset.org/abstracts/search?q=load%20balancing" title=" load balancing"> load balancing</a> </p> <a href="https://publications.waset.org/abstracts/27139/hybrid-bee-ant-colony-algorithm-for-effective-load-balancing-and-job-scheduling-in-cloud-computing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27139.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">636</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">3626</span> Evolution of Multimodulus Algorithm Blind Equalization Based on Recursive Least Square Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sardar%20Ameer%20Akram%20Khan">Sardar Ameer Akram Khan</a>, <a href="https://publications.waset.org/abstracts/search?q=Shahzad%20Amin%20Sheikh"> Shahzad Amin Sheikh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Blind equalization is an important technique amongst equalization family. Multimodulus algorithms based on blind equalization removes the undesirable effects of ISI and cater ups the phase issues, saving the cost of rotator at the receiver end. In this paper a new algorithm combination of recursive least square and Multimodulus algorithm named as RLSMMA is proposed by providing few assumption, fast convergence and minimum Mean Square Error (MSE) is achieved. The excellence of this technique is shown in the simulations presenting MSE plots and the resulting filter results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=blind%20equalizations" title="blind equalizations">blind equalizations</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20modulus%20algorithm" title=" constant modulus algorithm"> constant modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-modulus%20algorithm" title=" multi-modulus algorithm"> multi-modulus algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=recursive%20%20least%20square%20algorithm" title=" recursive least square algorithm"> recursive least square algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=quadrature%20amplitude%20modulation%20%28QAM%29" title=" quadrature amplitude modulation (QAM)"> quadrature amplitude modulation (QAM)</a> </p> <a href="https://publications.waset.org/abstracts/24704/evolution-of-multimodulus-algorithm-blind-equalization-based-on-recursive-least-square-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24704.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">651</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">3625</span> A Comparative Study of GTC and PSP Algorithms for Mining Sequential Patterns Embedded in Database with Time Constraints</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Safa%20Adi">Safa Adi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper will consider the problem of sequential mining patterns embedded in a database by handling the time constraints as defined in the GSP algorithm (level wise algorithms). We will compare two previous approaches GTC and PSP, that resumes the general principles of GSP. Furthermore this paper will discuss PG-hybrid algorithm, that using PSP and GTC. The results show that PSP and GTC are more efficient than GSP. On the other hand, the GTC algorithm performs better than PSP. The PG-hybrid algorithm use PSP algorithm for the two first passes on the database, and GTC approach for the following scans. Experiments show that the hybrid approach is very efficient for short, frequent sequences. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=database" title="database">database</a>, <a href="https://publications.waset.org/abstracts/search?q=GTC%20algorithm" title=" GTC algorithm"> GTC algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=PSP%20algorithm" title=" PSP algorithm"> PSP algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20patterns" title=" sequential patterns"> sequential patterns</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20constraints" title=" time constraints"> time constraints</a> </p> <a href="https://publications.waset.org/abstracts/97812/a-comparative-study-of-gtc-and-psp-algorithms-for-mining-sequential-patterns-embedded-in-database-with-time-constraints" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97812.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">395</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">3624</span> A Genetic Based Algorithm to Generate Random Simple Polygons Using a New Polygon Merge Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Nourollah">Ali Nourollah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohsen%20Movahedinejad"> Mohsen Movahedinejad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The merge algorithm has the time complexity of O ((r+s) *l) where r and s are the size of merging polygons and l shows the number of intersecting edges removed from the polygonal chain. It will be shown that 1 < l < r+s. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Divide%20and%20conquer" title="Divide and conquer">Divide and conquer</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=merge%20polygons" title=" merge polygons"> merge polygons</a>, <a href="https://publications.waset.org/abstracts/search?q=Random%20simple%20polygon%20generation." title=" Random simple polygon generation. "> Random simple polygon generation. </a> </p> <a href="https://publications.waset.org/abstracts/21488/a-genetic-based-algorithm-to-generate-random-simple-polygons-using-a-new-polygon-merge-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21488.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">538</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=SIFT%20algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=SIFT%20algorithm&page=3">3</a></li> <li class="page-item"><a class="page-link" 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