Relevant bibliographies by topics / Object matching

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Object matching'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Object matching"

1

Li, Stan Z. "Bayesian object matching." Journal of Applied Statistics 25, no. 3 (1998): 425–43. http://dx.doi.org/10.1080/02664769823142.

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Leme, Luiz André P., Daniela F. Brauner, Karin K. Breitman, Marco A. Casanova, and Alexandre Gazola. "Matching object catalogues." Innovations in Systems and Software Engineering 4, no. 4 (2008): 315–28. http://dx.doi.org/10.1007/s11334-008-0070-3.

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Klami, Arto. "Bayesian object matching." Machine Learning 92, no. 2-3 (2013): 225–50. http://dx.doi.org/10.1007/s10994-013-5357-4.

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Koning, Arno, and Johan Wagemans. "Detection of Symmetry and Repetition in One and Two Objects." Experimental Psychology 56, no. 1 (2009): 5–17. http://dx.doi.org/10.1027/1618-3169.56.1.5.

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Symmetry is usually easier to detect within a single object than in two objects (one-object advantage), while the reverse is true for repetition (two-objects advantage). This interaction between regularity and number of objects could reflect an intrinsic property of encoding spatial relations within and across objects or it could reflect a matching strategy. To test this, regularities between two contours (belonging to a single object or two objects) had to be detected in two experiments. Projected three-dimensional (3-D) objects rotated in depth were used to disambiguate figure-ground segment
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Auclair-Ouellet, Noémie, Marion Fossard, Joël Macoir, and Robert Laforce. "The Nonverbal Processing of Actions Is an Area of Relative Strength in the Semantic Variant of Primary Progressive Aphasia." Journal of Speech, Language, and Hearing Research 63, no. 2 (2020): 569–84. http://dx.doi.org/10.1044/2019_jslhr-19-00271.

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Purpose Better performance for actions compared to objects has been reported in the semantic variant of primary progressive aphasia (svPPA). This study investigated the influence of the assessment task (naming, semantic picture matching) over the dissociation between objects and actions. Method Ten individuals with svPPA and 17 matched controls completed object and action naming tests, and object and action semantic picture matching tests. Performance was compared between the svPPA and control groups, within the svPPA group, and for each participant with svPPA versus the control group individu
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Guo, Lei, Ningdong Song, Jindong Hu, Huiyan Han, Xie Han, and Fengguang Xiong. "Focusing 3D Small Objects with Object Matching Set Abstraction." Applied Sciences 15, no. 8 (2025): 4121. https://doi.org/10.3390/app15084121.

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Currently, 3D object detection methods fail to detect small objects due to the fewer effective points of small objects. It is a significant challenge to reduce the loss of information of points in representation learning. To this end, we propose an effective 3D detection method with object matching set abstraction (OMSA). We observe that key points are lost during feature learning with multiple set abstraction layers, especially for downsampling and queries. Therefore, we present a novel sampling module named focus-based sampling, which raises the sampling probability of small objects. In addi
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Ulrich, Markus, Patrick Follmann, and Jan-Hendrik Neudeck. "A comparison of shape-based matching with deep-learning-based object detection." tm - Technisches Messen 86, no. 11 (2019): 685–98. http://dx.doi.org/10.1515/teme-2019-0076.

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AbstractMatching, i. e. determining the exact 2D pose (e. g., position and orientation) of objects, is still one of the key tasks in machine vision applications like robot navigation, measuring, or grasping an object. There are many classic approaches for matching, based on edges or on the pure gray values of the template. In recent years, deep learning has been utilized mainly for more difficult tasks where the objects of interest are from many different categories with high intra-class variations and classic algorithms are failing. In this work, we compare one of the latest deep-learning-bas
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Zhu, Daoye, Chengqi Cheng, Weixin Zhai, Yihang Li, Shizhong Li, and Bo Chen. "Multiscale Spatial Polygonal Object Granularity Factor Matching Method Based on BPNN." ISPRS International Journal of Geo-Information 10, no. 2 (2021): 75. http://dx.doi.org/10.3390/ijgi10020075.

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Spatial object matching is one of the fundamental technologies used for updating and merging spatial data. This study focused mainly on the matching optimization of multiscale spatial polygonal objects. We proposed a granularity factor evaluation index that was developed to promote the recognition ability of complex matches in multiscale spatial polygonal object matching. Moreover, we designed the granularity factor matching model based on a backpropagation neural network (BPNN) and designed a multistage matching workflow. Our approach was validated experimentally using two topographical datas
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Flusser, Jan. "Object matching by means of matching likelihood coefficients." Pattern Recognition Letters 16, no. 9 (1995): 893–900. http://dx.doi.org/10.1016/0167-8655(95)00032-c.

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BHANDARKAR, SUCHENDRA M. "A SURFACE FEATURE ATTRIBUTED HYPERGRAPH REPRESENTATION FOR 3-D OBJECT RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 06 (1995): 869–909. http://dx.doi.org/10.1142/s0218001495000365.

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A surface feature hypergraph (SFAHG) representation is proposed for the recognition and localization of three-dimensional objects. The hypergraph representation is shown to be viewpoint independent thus resulting in substantial savings in terms of memory for the object model database. The resulting hypergraph matching algorithm integrates both, relational and the rigid pose constraint in a consistent unified manner. The matching algorithm is also shown to have a polynomial order of complexity even in multiple-object scenes with instances of objects partially occluding each other. An algorithm
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Dissertations / Theses on the topic "Object matching"

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Lennartsson, Mattias. "Object Recognition with Cluster Matching." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51494.

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<p>Within this thesis an algorithm for object recognition called Cluster Matching has been developed, implemented and evaluated. The image information is sampled at arbitrary sample points, instead of interest points, and local image features are extracted. These sample points are used as a compact representation of the image data and can quickly be searched for prior known objects. The algorithm is evaluated on a test set of images and the result is surprisingly reliable and time efficient.</p>
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Ahn, Yushin. "Object space matching and reconstruction using multiple images." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1213375997.

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Anderson, R. "Phase-based object matching using complex wavelets." Thesis, University of Cambridge, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595514.

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This thesis investigates the use of phase information from the Dual-Tree Complex Wavelet Transform (DT CWT) for the purpose of image matching. We review methods for image matching with a particular emphasis on matching with local features. We review the meaning and current uses of local phase, and introduce the DT CWT. We highlight the shortcomings of decimated wavelets for typical local phase analysis, and introduce two new functions that can extract useful information from the phases of decimated complex wavelets. The first, the InterLevel Product (ILP), is a pyramidal representation where t
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Kwon, Ohkyu. "Similarity measures for object matching in computer vision." Thesis, University of Bolton, 2016. http://ubir.bolton.ac.uk/890/.

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The similarity measures for object matching and their applications have been important topics in many fields of computer vision such as those of image recognition, image fusion, image analysis, video sequence matching, and so on. This critical commentary presents the efficiency of new metric methods such as the robust Hausdorff distance (RHD), the accurate M-Hausdorff distance (AMHD), and the fast sum of absolute differences (FSAD). The RHD measure computes the similarity distance of the occluded/noisy image pair and evaluates the performances of the multi-modal registration algorithms. The AM
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Tieu, Kinh H. (Kinh Han) 1976. "Statistical dependence estimation for object interaction and matching." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38316.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (p. 97-103).<br>This dissertation shows how statistical dependence estimation underlies two key problems in visual surveillance and wide-area tracking. The first problem is to detect and describe interactions between moving objects. The goal is to measure the influence objects exert on one another. The second problem is to match objects between non-overlapping cameras. There, the goal is to pair the departures in one camera with the arrivals
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Ko, Kwang Hee 1971. "Algorithms for three-dimensional free-form object matching." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29751.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 2003.<br>Includes bibliographical references (leaves 117-126).<br>This thesis addresses problems of free-form object matching for the point vs. NURBS surface and the NURBS surface vs. NURBS surface cases, and its application to copyright protection. Two new methods are developed to solve a global and partial matching problem with no a priori information on correspondence or initial transformation and no scaling effects, namely the KH and the umbilic method. The KH method establishes a correspondence between two
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Sangi, P. (Pekka). "Object motion estimation using block matching with uncertainty analysis." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200774.

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Abstract Estimation of 2-D motion is one of the fundamental problems in video processing and computer vision. This thesis addresses two general tasks in estimating projected motions of background and foreground objects in a scene: global motion estimation and motion based segmentation. The work concentrates on the study of the block matching method, and especially on those cases where the matching measure is based on the sum of squared or absolute displaced frame differences. Related techniques for performing the confidence analysis of local displacement are considered and used to improve the
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Staniaszek, Michal. "Feature-Feature Matching For Object Retrieval in Point Clouds." Thesis, KTH, Datorseende och robotik, CVAP, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170475.

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In this project, we implement a system for retrieving instances of objects from point clouds using feature based matching techniques. The target dataset of point clouds consists of approximately 80 full scans of office rooms over a period of one month. The raw clouds are reprocessed to remove regions which are unlikely to contain objects. Using locations determined by one of several possible interest point selection methods, one of a number of descriptors is extracted from the processed clouds. Descriptors from a target cloud are compared to those from a query object using a nearest neighbour
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Sim, Hak Chuah. "Invariant object matching with a modified dynamic link network." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256269/.

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Krupnik, Amnon. "Multiple-patch matching in the object space for aerotriangulation /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487857546386844.

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Books on the topic "Object matching"

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Lamdan, Yehezkel. Object recognition by affine invariant matching. Courant Institute of Mathematical Sciences, New York University, 1988.

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Bastuscheck, C. Marc. Object recognition by 3-dimensional curve matching. Courant Institute of Mathematical Sciences, New York University, 1986.

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Dawson, K. M. Implicit model matching as an approach to three-dimensional object recognition. Trinity College, Department of Computer Science, 1991.

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Lee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. IOS Press, 2003.

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Kao, Chang-Lung. Affine invariant matching of noisy objects. Naval Postgraduate School, 1989.

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Abdelmalek, Nabih N. A computer vision system for matching 3-D range data objects. National Research Council Canada, Division of Electrical Engineering, 1987.

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Schwartz, Jacob T. Identification of partially obscured objects in two dimensions by matching of noisy 'characteristic curves,'. Courant Institute of Mathematical Sciences, New York University, 1985.

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Yang, Cong. Object Shape Generation, Representation and Matching. Logos Verlag Berlin, 2016.

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Schonberg, Edith, C. Marc Bastuscheck, and Jacob T. Schwartz. Object Recognition by 3-Dimensional Curve Matching. Creative Media Partners, LLC, 2015.

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Finding and Matching Topographic Features in 3-D Object Meshes. Storming Media, 1999.

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Book chapters on the topic "Object matching"

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Bennamoun, M., and G. J. Mamic. "Object Representation and Feature Matching." In Object Recognition. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_4.

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Treiber, Marco. "Flexible Shape Matching." In An Introduction to Object Recognition. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-235-3_6.

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Tiebe, Oliver, Cong Yang, Muhammad Hassan Khan, Marcin Grzegorzek, and Dominik Scarpin. "Stripes-Based Object Matching." In Computer and Information Science. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40171-3_5.

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Bennamoun, M., and G. J. Mamic. "Stereo Matching and Reconstruction of a Depth Map." In Object Recognition. Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_2.

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Emir, Burak, Martin Odersky, and John Williams. "Matching Objects with Patterns." In ECOOP 2007 – Object-Oriented Programming. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73589-2_14.

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Berg, Alexander C., and Jitendra Malik. "Shape Matching and Object Recognition." In Toward Category-Level Object Recognition. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_25.

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Lammari, Nadira, Jacky Akoka, and Isabelle Comyn-Wattiau. "Supporting Database Evolution: Using Ontologies Matching." In Object-Oriented Information Systems. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45242-3_27.

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Cootes, Tim F. "Deformable Object Modelling and Matching." In Computer Vision – ACCV 2010. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_1.

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Cho, Tai-Hoon. "Object Matching Using Generalized Hough Transform and Chamfer Matching." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_171.

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Shan, Y., H. S. Sawhney, B. Matei, and R. Kumar. "Partial Object Matching with Shapeme Histograms." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_35.

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Conference papers on the topic "Object matching"

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Balikai, Anupriya, and Peter Hall. "Depiction Inviariant Object Matching." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.56.

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Zardetto, Diego, Monica Scannapieco, and Tiziana Catarci. "Effective automated Object Matching." In 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icde.2010.5447904.

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Choe, Jaesung, Kyungdon Joo, Francois Rameau, and In So Kweon. "Stereo Object Matching Network." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9562027.

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Huang, Aiming, Zheng Gao, Bin Dai, and Li Luo. "Real-time object matching." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew G. Tescher. SPIE, 1998. http://dx.doi.org/10.1117/12.323233.

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Ma, Thomas, Vijay Menon, and Kate Larson. "Improving Welfare in One-Sided Matchings using Simple Threshold Queries." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/45.

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We study one-sided matching problems where each agent must be assigned at most one object. In this classic problem it is often assumed that agents specify only ordinal preferences over objects and the goal is to return a matching that satisfies some desirable property such as Pareto optimality or rank-maximality. However, agents may have cardinal utilities describing their preference intensities and ignoring this can result in welfare loss. We investigate how to elicit additional cardinal information from agents using simple threshold queries and use it in turn to design algorithms that return
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Pon, Alex D., Jason Ku, Chengyao Li, and Steven L. Waslander. "Object-Centric Stereo Matching for 3D Object Detection." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9196660.

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Hu, Nan, Qixing Huang, Boris Thibert, and Leonidas Guibas. "Distributable Consistent Multi-object Matching." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00261.

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Grove, T. D., and R. B. Fisher. "Attention in Iconic Object Matching." In British Machine Vision Conference 1996. British Machine Vision Association, 1996. http://dx.doi.org/10.5244/c.10.19.

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Arandjelovi, Ognjen. "Object Matching Using Boundary Descriptors." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.85.

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Celenk, Mehmet. "Three-dimensional object surface matching." In SPIE's 1995 Symposium on OE/Aerospace Sensing and Dual Use Photonics, edited by Friedrich O. Huck and Richard D. Juday. SPIE, 1995. http://dx.doi.org/10.1117/12.211999.

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Reports on the topic "Object matching"

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Gyaourova, A., C. Kamath, and S. Cheung. Block Matching for Object Tracking. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15009731.

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Smith, David. Parallel approximate string matching applied to occluded object recognition. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.5608.

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Cass, Todd A. Feature Matching for Object Localization in the Presence of Uncertainty. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada231405.

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Luo, Ming, Daniel DeMenthon, Xiaodong Yu, and David Doermann. SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada448477.

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Asari, Vijayan, Paheding Sidike, Binu Nair, Saibabu Arigela, Varun Santhaseelan, and Chen Cui. PR-433-133700-R01 Pipeline Right-of-Way Automated Threat Detection by Advanced Image Analysis. Pipeline Research Council International, Inc. (PRCI), 2015. http://dx.doi.org/10.55274/r0010891.

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A novel algorithmic framework for the robust detection and classification of machinery threats and other potentially harmful objects intruding onto a pipeline right-of-way (ROW) is designed from three perspectives: visibility improvement, context-based segmentation, and object recognition/classification. In the first part of the framework, an adaptive image enhancement algorithm is utilized to improve the visibility of aerial imagery to aid in threat detection. In this technique, a nonlinear transfer function is developed to enhance the processing of aerial imagery with extremely non-uniform l
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Love, N., and C. Kamath. An Empirical Study of Block Matching Techniques for the Detection of Moving Objects. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/898460.

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