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style="font-size:.9rem"><span class="badge badge-info">10463</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">10462</span> BART Matching Method: Using Bayesian Additive Regression Tree for Data Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gianna%20Zou">Gianna Zou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=BART" title="BART">BART</a>, <a href="https://publications.waset.org/abstracts/search?q=Bayesian" title=" Bayesian"> Bayesian</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=regression" title=" regression"> regression</a> </p> <a href="https://publications.waset.org/abstracts/149989/bart-matching-method-using-bayesian-additive-regression-tree-for-data-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/149989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">147</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10461</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">10460</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">10459</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">10458</span> Tool for Fast Detection of Java Code Snippets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tom%C3%A1%C5%A1%20Bubl%C3%ADk">Tomáš Bublík</a>, <a href="https://publications.waset.org/abstracts/search?q=Miroslav%20Virius"> Miroslav Virius</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents general results on the Java source code snippet detection problem. We propose the tool which uses graph and sub graph isomorphism detection. A number of solutions for all of these tasks have been proposed in the literature. However, although that all these solutions are really fast, they compare just the constant static trees. Our solution offers to enter an input sample dynamically with the Scripthon language while preserving an acceptable speed. We used several optimizations to achieve very low number of comparisons during the matching algorithm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=AST" title="AST">AST</a>, <a href="https://publications.waset.org/abstracts/search?q=Java" title=" Java"> Java</a>, <a href="https://publications.waset.org/abstracts/search?q=tree%20matching" title=" tree matching"> tree matching</a>, <a href="https://publications.waset.org/abstracts/search?q=scripthon%20source%20code%20recognition" title=" scripthon source code recognition"> scripthon source code recognition</a> </p> <a href="https://publications.waset.org/abstracts/8474/tool-for-fast-detection-of-java-code-snippets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8474.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">425</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">10457</span> Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP) for Recovering Signal</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> Given a large sparse signal, great wishes are to reconstruct the signal precisely and accurately from lease number of measurements as possible as it could. Although this seems possible by theory, the difficulty is in built an algorithm to perform the accuracy and efficiency of reconstructing. This paper proposes a new proved method to reconstruct sparse signal depend on using new method called Least Support Matching Pursuit (LS-OMP) merge it with the theory of Partial Knowing Support (PSK) given new method called Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP). The new methods depend on the greedy algorithm to compute the support which depends on the number of iterations. So to make it faster, the PKLS-OMP adds the idea of partial knowing support of its algorithm. It shows the efficiency, simplicity, and accuracy to get back the original signal if the sampling matrix satisfies the Restricted Isometry Property (RIP). Simulation results also show that it outperforms many algorithms especially for compressible signals. <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=lest%20support%20orthogonal%20matching%20pursuit" title=" lest support orthogonal matching pursuit"> lest support orthogonal matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20knowing%20support" title=" partial knowing support"> partial knowing support</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> </p> <a href="https://publications.waset.org/abstracts/16008/partially-knowing-of-least-support-orthogonal-matching-pursuit-pkls-omp-for-recovering-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16008.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">10456</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">10455</span> Management of Empty Containers by Consignees in the Hinterland</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Legros">Benjamin Legros</a>, <a href="https://publications.waset.org/abstracts/search?q=Jan%20Fransoo"> Jan Fransoo</a>, <a href="https://publications.waset.org/abstracts/search?q=Oualid%20Jouini"> Oualid Jouini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to evaluate street-turn strategies for empty container repositioning in the hinterland. Containers arrive over time at the (importer) consignee, while the demand for containers arises from the (exporter) shipper. A match can be operated between an empty container from the consignee and the load from the shipper. Therefore, we model the system as a double-ended queue with non-zero matching time and a limited number of resources in order to optimize the reposition- ing decisions. We determine the performance measures when the consignee operates using a fixed withholding threshold policy. We show that the matching time mainly plays a role in the matching proportion, while under a certain duration, it only marginally impacts the consignee’s inventory policy and cost per container. Also, the withholding level is mainly determined by the shipper’s production rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=container" title="container">container</a>, <a href="https://publications.waset.org/abstracts/search?q=double-ended%20queue" title=" double-ended queue"> double-ended queue</a>, <a href="https://publications.waset.org/abstracts/search?q=inventory" title=" inventory"> inventory</a>, <a href="https://publications.waset.org/abstracts/search?q=Markov%20decision%20process" title=" Markov decision process"> Markov decision process</a>, <a href="https://publications.waset.org/abstracts/search?q=non-zero%20matching%20time" title=" non-zero matching time"> non-zero matching time</a>, <a href="https://publications.waset.org/abstracts/search?q=street-turn" title=" street-turn"> street-turn</a> </p> <a href="https://publications.waset.org/abstracts/159800/management-of-empty-containers-by-consignees-in-the-hinterland" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/159800.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">142</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">10454</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">10453</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">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">10452</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">10451</span> A Study of ZY3 Satellite Digital Elevation Model Verification and Refinement with Shuttle Radar Topography Mission</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bo%20Wang">Bo Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ZY-3%20satellite%20imagery" title="ZY-3 satellite imagery">ZY-3 satellite imagery</a>, <a href="https://publications.waset.org/abstracts/search?q=DEM" title=" DEM"> DEM</a>, <a href="https://publications.waset.org/abstracts/search?q=SRTM" title=" SRTM"> SRTM</a>, <a href="https://publications.waset.org/abstracts/search?q=refinement" title=" refinement"> refinement</a> </p> <a href="https://publications.waset.org/abstracts/76112/a-study-of-zy3-satellite-digital-elevation-model-verification-and-refinement-with-shuttle-radar-topography-mission" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76112.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">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">10450</span> Clustering-Based Computational Workload Minimization in Ontology Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mansir%20Abubakar">Mansir Abubakar</a>, <a href="https://publications.waset.org/abstracts/search?q=Hazlina%20Hamdan"> Hazlina Hamdan</a>, <a href="https://publications.waset.org/abstracts/search?q=Norwati%20Mustapha"> Norwati Mustapha</a>, <a href="https://publications.waset.org/abstracts/search?q=Teh%20Noranis%20Mohd%20Aris"> Teh Noranis Mohd Aris</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=attribute%20correspondence" title="attribute correspondence">attribute correspondence</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=computational%20workload" title=" computational workload"> computational workload</a>, <a href="https://publications.waset.org/abstracts/search?q=k-medoids%20clustering" title=" k-medoids clustering"> k-medoids clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=ontology%20matching" title=" ontology matching"> ontology matching</a> </p> <a href="https://publications.waset.org/abstracts/78369/clustering-based-computational-workload-minimization-in-ontology-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78369.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">248</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">10449</span> Model Order Reduction for Frequency Response and Effect of Order of Method for Matching Condition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aref%20Ghafouri">Aref Ghafouri</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20javad%20Mollakazemi"> Mohammad javad Mollakazemi</a>, <a href="https://publications.waset.org/abstracts/search?q=Farhad%20Asadi"> Farhad Asadi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20response" title="frequency response">frequency response</a>, <a href="https://publications.waset.org/abstracts/search?q=order%20of%20model%20reduction" title=" order of model reduction"> order of model reduction</a>, <a href="https://publications.waset.org/abstracts/search?q=frequency%20matching%20condition" title=" frequency matching condition"> frequency matching condition</a>, <a href="https://publications.waset.org/abstracts/search?q=nonlinear%20experimental%20data" title=" nonlinear experimental data"> nonlinear experimental data</a> </p> <a href="https://publications.waset.org/abstracts/17631/model-order-reduction-for-frequency-response-and-effect-of-order-of-method-for-matching-condition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/17631.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">403</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">10448</span> Evaluation of the Matching Optimization of Human-Machine Interface Matching in the Cab</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yanhua%20Ma">Yanhua Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Lu%20Zhai"> Lu Zhai</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinchen%20Wang"> Xinchen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hongyu%20Liang"> Hongyu Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=analytic%20hierarchy%20process" title="analytic hierarchy process">analytic hierarchy process</a>, <a href="https://publications.waset.org/abstracts/search?q=fuzzy%20comprehension%20evaluation%20method" title=" fuzzy comprehension evaluation method"> fuzzy comprehension evaluation method</a>, <a href="https://publications.waset.org/abstracts/search?q=human-machine%20interface" title=" human-machine interface"> human-machine interface</a>, <a href="https://publications.waset.org/abstracts/search?q=matching%20optimization" title=" matching optimization"> matching optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=software%20human%20factor%20analysis" title=" software human factor analysis"> software human factor analysis</a> </p> <a href="https://publications.waset.org/abstracts/131104/evaluation-of-the-matching-optimization-of-human-machine-interface-matching-in-the-cab" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131104.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">156</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10447</span> Hybrid Approximate Structural-Semantic Frequent Subgraph Mining</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Montaceur%20Zaghdoud">Montaceur Zaghdoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Moussaoui"> Mohamed Moussaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=Jalel%20Akaichi"> Jalel Akaichi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=approximate%20graph%20matching" title="approximate graph matching">approximate graph matching</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20frequent%20subgraph%20mining" title=" hybrid frequent subgraph mining"> hybrid frequent subgraph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=graph%20mining" title=" graph mining"> graph mining</a>, <a href="https://publications.waset.org/abstracts/search?q=possibility%20theory" title=" possibility theory"> possibility theory</a> </p> <a href="https://publications.waset.org/abstracts/34195/hybrid-approximate-structural-semantic-frequent-subgraph-mining" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/34195.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">403</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">10446</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">10445</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">10444</span> Chinese Event Detection Technique Based on Dependency Parsing and Rule Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Weitao%20Lin">Weitao Lin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To quickly extract adequate information from large-scale unstructured text data, this paper studies the representation of events in Chinese scenarios and performs the regularized abstraction. It proposes a Chinese event detection technique based on dependency parsing and rule matching. The method first performs dependency parsing on the original utterance, then performs pattern matching at the word or phrase granularity based on the results of dependent syntactic analysis, filters out the utterances with prominent non-event characteristics, and obtains the final results. The experimental results show the effectiveness of the method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=natural%20language%20processing" title="natural language processing">natural language processing</a>, <a href="https://publications.waset.org/abstracts/search?q=Chinese%20event%20detection" title=" Chinese event detection"> Chinese event detection</a>, <a href="https://publications.waset.org/abstracts/search?q=rules%20matching" title=" rules matching"> rules matching</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20parsing" title=" dependency parsing"> dependency parsing</a> </p> <a href="https://publications.waset.org/abstracts/158129/chinese-event-detection-technique-based-on-dependency-parsing-and-rule-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/158129.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">141</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10443</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">10442</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">10441</span> AIPM：An Integrator and Pull Request Matching Model in Github</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zhifang%20Liao">Zhifang Liao</a>, <a href="https://publications.waset.org/abstracts/search?q=Yanbing%20Li"> Yanbing Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Xu"> Li Xu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yan%20Zhang"> Yan Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaoping%20Fan"> Xiaoping Fan</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinsong%20Wu"> Jinsong Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Pull Request (PR) is the primary method for code contributions from the external contributors in Github. PR review is an essential part of open source software developments for maintaining the quality of software. Matching a new PR of an appropriate integrator will make the PR review more effective. However, PR and integrator matching are now organized manually in Github. To reduce this cost, we presented an AIPM model to predict highly relevant integrator of incoming PRs. AIPM uses topic model to extract topics from the PRs, and builds a one-to-one correspondence between topics and integrators. Then, AIPM finds the most suitable integrator according to the maximum entry of the topic-document distribution. On average, AIPM can reach a precision of 60%, and even in some projects, can reach a precision of 80%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=pull%20Request" title="pull Request">pull Request</a>, <a href="https://publications.waset.org/abstracts/search?q=integrator%20matching" title=" integrator matching"> integrator matching</a>, <a href="https://publications.waset.org/abstracts/search?q=Github" title=" Github"> Github</a>, <a href="https://publications.waset.org/abstracts/search?q=open%20source%20project" title=" open source project"> open source project</a>, <a href="https://publications.waset.org/abstracts/search?q=topic%20model" title=" topic model"> topic model</a> </p> <a href="https://publications.waset.org/abstracts/63126/aipman-integrator-and-pull-request-matching-model-in-github" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63126.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">299</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">10440</span> Registration of Multi-Temporal Unmanned Aerial Vehicle Images for Facility Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dongyeob%20Han">Dongyeob Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Jungwon%20Huh"> Jungwon Huh</a>, <a href="https://publications.waset.org/abstracts/search?q=Quang%20Huy%20Tran"> Quang Huy Tran</a>, <a href="https://publications.waset.org/abstracts/search?q=Choonghyun%20Kang"> Choonghyun Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned Aerial Vehicles (UAVs) have been used for surveillance, monitoring, inspection, and mapping. In this paper, we present a systematic approach for automatic registration of UAV images for monitoring facilities such as building, green house, and civil structures. The two-step process is applied; 1) an image matching technique based on SURF (Speeded up Robust Feature) and RANSAC (Random Sample Consensus), 2) bundle adjustment of multi-temporal images. Image matching to find corresponding points is one of the most important steps for the precise registration of multi-temporal images. We used the SURF algorithm to find a quick and effective matching points. RANSAC algorithm was used in the process of finding matching points between images and in the bundle adjustment process. Experimental results from UAV images showed that our approach has a good accuracy to be applied to the change detection of facility. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=building" title="building">building</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20matching" title=" image matching"> image matching</a>, <a href="https://publications.waset.org/abstracts/search?q=temperature" title=" temperature"> temperature</a>, <a href="https://publications.waset.org/abstracts/search?q=unmanned%20aerial%20vehicle" title=" unmanned aerial vehicle"> unmanned aerial vehicle</a> </p> <a href="https://publications.waset.org/abstracts/85064/registration-of-multi-temporal-unmanned-aerial-vehicle-images-for-facility-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/85064.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">292</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">10439</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">529</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">10438</span> Study on Dynamic Stiffness Matching and Optimization Design Method of a Machine Tool</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lu%20Xi">Lu Xi</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Pan"> Li Pan</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen%20Mengmeng"> Wen Mengmeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The stiffness of each component has different influences on the stiffness of the machine tool. Taking the five-axis gantry machining center as an example, we made the modal analysis of the machine tool, followed by raising and lowering the stiffness of the pillar, slide plate, beam, ram and saddle so as to study the stiffness matching among these components on the standard of whether the stiffness of the modified machine tool changes more than 50% relative to the stiffness of the original machine tool. The structural optimization of the machine tool can be realized by changing the stiffness of the components whose stiffness is mismatched. For example, the stiffness of the beam is mismatching. The natural frequencies of the first six orders of the beam increased by 7.70%, 0.38%, 6.82%, 7.96%, 18.72% and 23.13%, with the weight increased by 28Kg, leading to the natural frequencies of several orders which had a great influence on the dynamic performance of the whole machine increased by 1.44%, 0.43%, 0.065%, which verified the correctness of the optimization method based on stiffness matching proposed in this paper. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=machine%20tool" title="machine tool">machine tool</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=modal%20analysis" title=" modal analysis"> modal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=stiffness%20matching" title=" stiffness matching"> stiffness matching</a> </p> <a href="https://publications.waset.org/abstracts/169087/study-on-dynamic-stiffness-matching-and-optimization-design-method-of-a-machine-tool" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/169087.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">102</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">10437</span> The Amount of Conformity of Persian Subject Headlines with Users' Social Tagging</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amir%20Reza%20Asnafi">Amir Reza Asnafi</a>, <a href="https://publications.waset.org/abstracts/search?q=Masoumeh%20Kazemizadeh"> Masoumeh Kazemizadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Najmeh%20Salemi"> Najmeh Salemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Due to the diversity of information resources in the web0.2 environment, which is increasing in number from time to time, the social tagging system should be used to discuss Internet resources. Studying the relevance of social tags to thematic headings can help enrich resources and make them more accessible to resources. The present research is of applied-theoretical type and research method of content analysis. In this study, using the listing method and content analysis, the level of accurate, approximate, relative, and non-conformity of social labels of books available in the field of information science and bibliography of Kitabrah website with Persian subject headings was determined. The exact matching of subject headings with social tags averaged 22 items, the approximate matching of subject headings with social tags averaged 36 items, the relative matching of thematic headings with social tags averaged 36 social items, and the average matching titles did not match the title. The average is 116. According to the findings, the exact matching of subject headings with social labels is the lowest and the most inconsistent. This study showed that the average non-compliance of subject headings with social labels is even higher than the sum of the three types of exact, relative, and approximate matching. As a result, the relevance of thematic titles to social labels is low. Due to the fact that the subject headings are in the form of static text and users are not allowed to interact and insert new selected words and topics, and on the other hand, in websites based on Web 2 and based on the social classification system, this possibility is available for users. An important point of the present study and the studies that have matched the syntactic and semantic matching of social labels with thematic headings is that the degree of conformity of thematic headings with social labels is low. Therefore, these two methods can complement each other and create a hybrid cataloging that includes subject headings and social tags. The low level of conformity of thematic headings with social tags confirms the results of backgrounds and writings that have compared the social tags of books with the thematic headings of the Library of Congress. It is not enough to match social labels with thematic headings. It can be said that these two methods can be complementary. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Web%202%2F0" title="Web 2/0">Web 2/0</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20tags" title=" social tags"> social tags</a>, <a href="https://publications.waset.org/abstracts/search?q=subject%20headings" title=" subject headings"> subject headings</a>, <a href="https://publications.waset.org/abstracts/search?q=hybrid%20cataloging" title=" hybrid cataloging"> hybrid cataloging</a> </p> <a href="https://publications.waset.org/abstracts/129206/the-amount-of-conformity-of-persian-subject-headlines-with-users-social-tagging" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/129206.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">159</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">10436</span> Counting People Utilizing Space-Time Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Elmarhomy">Ahmed Elmarhomy</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Terada"> K. Terada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An automated method for counting passerby has been proposed using virtual-vertical measurement lines. Space-time image is representing the human regions which are treated using the segmentation process. Different color space has been used to perform the template matching. A proper template matching has been achieved to determine direction and speed of passing people. Distinguish one or two passersby has been investigated using a correlation between passerby speed and the human-pixel area. Finally, the effectiveness of the presented method has been experimentally verified. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=counting%20people" title="counting people">counting people</a>, <a href="https://publications.waset.org/abstracts/search?q=measurement%20line" title=" measurement line"> measurement line</a>, <a href="https://publications.waset.org/abstracts/search?q=space-time%20image" title=" space-time image"> space-time image</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=template%20matching" title=" template matching"> template matching</a> </p> <a href="https://publications.waset.org/abstracts/46877/counting-people-utilizing-space-time-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/46877.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">452</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">10435</span> Factor Study Affecting Visual Awareness on Dynamic Object Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Terry%20Liang%20Khin%20Teo">Terry Liang Khin Teo</a>, <a href="https://publications.waset.org/abstracts/search?q=Sun%20Woh%20Lye"> Sun Woh Lye</a>, <a href="https://publications.waset.org/abstracts/search?q=Kai%20Lun%20Brendon%20Goh"> Kai Lun Brendon Goh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As applied to dynamic monitoring situations, the prevailing approach to situation awareness (SA) assumes that the relevant areas of interest (AOI) be perceived before that information can be processed further to affect decision-making and, thereafter, action. It is not entirely clear whether this is the case. This study seeks to investigate the monitoring of dynamic objects through matching eye fixations with the relevant AOIs in boundary-crossing scenarios. By this definition, a match is where a fixation is registered on the AOI. While many factors may affect monitoring characteristics, traffic simulations were designed in this study to explore two factors, namely: the number of inbounds/outbound traffic transfers and the number of entry and/or exit points in a radar monitoring sector. These two factors were graded into five levels of difficulty ranging from low to high traffic flow numbers. Combined permutation in terms of levels of difficulty of these two factors yielded a total of thirty scenarios. Through this, results showed that changes in the traffic flow numbers on transfer resulted in greater variations having match limits ranging from 29%-100%, as compared to the number of sector entry/exit points of range limit from 80%-100%. The subsequent analysis is able to determine the type and combination of traffic scenarios where imperfect matching is likely to occur. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=air%20traffic%20simulation" title="air traffic simulation">air traffic simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=eye-tracking" title=" eye-tracking"> eye-tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=visual%20monitoring" title=" visual monitoring"> visual monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=focus%20attention" title=" focus attention"> focus attention</a> </p> <a href="https://publications.waset.org/abstracts/155140/factor-study-affecting-visual-awareness-on-dynamic-object-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155140.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">57</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">10434</span> Estimating Estimators: An Empirical Comparison of Non-Invasive Analysis Methods</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yan%20Torres">Yan Torres</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernanda%20Simoes"> Fernanda Simoes</a>, <a href="https://publications.waset.org/abstracts/search?q=Francisco%20Petrucci-Fonseca"> Francisco Petrucci-Fonseca</a>, <a href="https://publications.waset.org/abstracts/search?q=Freddie-Jeanne%20Richard"> Freddie-Jeanne Richard</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The non-invasive samples are an alternative of collecting genetic samples directly. Non-invasive samples are collected without the manipulation of the animal (e.g., scats, feathers and hairs). Nevertheless, the use of non-invasive samples has some limitations. The main issue is degraded DNA, leading to poorer extraction efficiency and genotyping. Those errors delayed for some years a widespread use of non-invasive genetic information. Possibilities to limit genotyping errors can be done using analysis methods that can assimilate the errors and singularities of non-invasive samples. Genotype matching and population estimation algorithms can be highlighted as important analysis tools that have been adapted to deal with those errors. Although, this recent development of analysis methods there is still a lack of empirical performance comparison of them. A comparison of methods with dataset different in size and structure can be useful for future studies since non-invasive samples are a powerful tool for getting information specially for endangered and rare populations. To compare the analysis methods, four different datasets used were obtained from the Dryad digital repository were used. Three different matching algorithms (Cervus, Colony and Error Tolerant Likelihood Matching - ETLM) are used for matching genotypes and two different ones for population estimation (Capwire and BayesN). The three matching algorithms showed different patterns of results. The ETLM produced less number of unique individuals and recaptures. A similarity in the matched genotypes between Colony and Cervus was observed. That is not a surprise since the similarity between those methods on the likelihood pairwise and clustering algorithms. The matching of ETLM showed almost no similarity with the genotypes that were matched with the other methods. The different cluster algorithm system and error model of ETLM seems to lead to a more criterious selection, although the processing time and interface friendly of ETLM were the worst between the compared methods. The population estimators performed differently regarding the datasets. There was a consensus between the different estimators only for the one dataset. The BayesN showed higher and lower estimations when compared with Capwire. The BayesN does not consider the total number of recaptures like Capwire only the recapture events. So, this makes the estimator sensitive to data heterogeneity. Heterogeneity in the sense means different capture rates between individuals. In those examples, the tolerance for homogeneity seems to be crucial for BayesN work properly. Both methods are user-friendly and have reasonable processing time. An amplified analysis with simulated genotype data can clarify the sensibility of the algorithms. The present comparison of the matching methods indicates that Colony seems to be more appropriated for general use considering a time/interface/robustness balance. The heterogeneity of the recaptures affected strongly the BayesN estimations, leading to over and underestimations population numbers. Capwire is then advisable to general use since it performs better in a wide range of situations. <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=genetics" title=" genetics"> genetics</a>, <a href="https://publications.waset.org/abstracts/search?q=matching" title=" matching"> matching</a>, <a href="https://publications.waset.org/abstracts/search?q=population" title=" population"> population</a> </p> <a href="https://publications.waset.org/abstracts/99825/estimating-estimators-an-empirical-comparison-of-non-invasive-analysis-methods" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/99825.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">143</span> </span> </div> 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