Academic literature on the topic 'Object matching'
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Journal articles on the topic "Object matching"
Li, Stan Z. "Bayesian object matching." Journal of Applied Statistics 25, no. 3 (June 1998): 425–43. http://dx.doi.org/10.1080/02664769823142.
Full textLeme, 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 (October 31, 2008): 315–28. http://dx.doi.org/10.1007/s11334-008-0070-3.
Full textKlami, Arto. "Bayesian object matching." Machine Learning 92, no. 2-3 (April 30, 2013): 225–50. http://dx.doi.org/10.1007/s10994-013-5357-4.
Full textNewell, F. N. "Searching for Objects in the Visual Periphery: Effects of Orientation." Perception 25, no. 1_suppl (August 1996): 110. http://dx.doi.org/10.1068/v96l1111.
Full textKoning, Arno, and Johan Wagemans. "Detection of Symmetry and Repetition in One and Two Objects." Experimental Psychology 56, no. 1 (January 2009): 5–17. http://dx.doi.org/10.1027/1618-3169.56.1.5.
Full textFlusser, Jan. "Object matching by means of matching likelihood coefficients." Pattern Recognition Letters 16, no. 9 (September 1995): 893–900. http://dx.doi.org/10.1016/0167-8655(95)00032-c.
Full textAuclair-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 (February 26, 2020): 569–84. http://dx.doi.org/10.1044/2019_jslhr-19-00271.
Full textUlrich, 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 (November 26, 2019): 685–98. http://dx.doi.org/10.1515/teme-2019-0076.
Full textYang, Yingpeng. "A Stereo Matching System." International Journal of Advanced Pervasive and Ubiquitous Computing 5, no. 2 (April 2013): 1–8. http://dx.doi.org/10.4018/japuc.2013040101.
Full textZhu, 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 (February 13, 2021): 75. http://dx.doi.org/10.3390/ijgi10020075.
Full textDissertations / Theses on the topic "Object matching"
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.
Full textWithin 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.
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.
Full textAhn, Yushin. "Object space matching and reconstruction using multiple images." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1213375997.
Full textKwon, Ohkyu. "Similarity measures for object matching in computer vision." Thesis, University of Bolton, 2016. http://ubir.bolton.ac.uk/890/.
Full textTieu, 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.
Full textIncludes bibliographical references (p. 97-103).
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 in a different camera so that the resulting distribution of relationships best models the data. Both problems have become important for scaling up surveillance systems to larger areas and expanding the monitoring to more interesting behaviors. We show how statistical dependence estimation generalizes previous work and may have applications in other areas. The two problems represent different applications of our thesis that statistical dependence estimation underlies the learning of the structure of probabilistic models. First, we analyze the relationship between Bayesian, information-theoretic, and classical statistical methods for statistical dependence estimation. Then, we apply these ideas to formulate object interaction in terms of dependency structure model selection.
(cont.) We describe experiments on simulated and real interaction data to validate our approach. Second, we formulate the matching problem in terms of maximizing statistical dependence. This allows us to generalize previous work on matching, and we show improved results on simulated and real data for non-overlapping cameras. We also prove an intractability result on exact maximally dependent matching.
by Kinh Tieu.
Ph.D.
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.
Full textIncludes bibliographical references (leaves 117-126).
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 objects by utilizing the Gaussian and mean curvatures. The umbilic method uses the qualitative properties of umbilical points to find correspondence information between two objects. These two methods are extended to deal with uniform scaling effects. The umbilic method is enhanced with an algorithm for scaling factor estimation using the quantitative properties of umbilical points. The KH method is used as a building block of an optimization scheme based on the golden section search which recovers iteratively an optimum scaling factor. Since the golden section search only requires an initial interval for the scaling factor, the solution process is simplified compared to iterative optimization algorithms, which require good initial estimates of the scaling factor and the rigid body transformation. The matching algorithms are applied to problems of copyright protection.
(cont.) A suspect model is aligned to an original model through matching methods so that similarity between two geometric models can be assessed to determine if the suspect model contains part(s) of the original model. Three types of tests, the weak, intermediate and strong tests, are proposed for similarity assessment between two objects. The weak and intermediate tests are performed at node points obtained through shape intrinsic wireframing. The strong test relies on isolated umbilical points which can be used as fingerprints of an object for supporting an ownership claim to the original model. The three tests are organized in two decision algorithms so that they produce systematic and statistical measures for a similarity decision between two objects in a hierarchical manner. Based on the systematic statistical evaluation of similarity, a decision can be reached whether the suspect model is a copy of the original model.
by Kwang Hee Ko.
Ph.D.
Sangi, P. (Pekka). "Object motion estimation using block matching with uncertainty analysis." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526200774.
Full textTiivistelmä Tässä väitöskirjassa tutkitaan yhtä videonkäsittelyn ja konenäön perusongelmaa, kaksiulotteisen liikkeen estimointia. Työ käsittelee kahta yleistä tehtävää taustan ja etualan kohteiden liikkeiden määrittämisessä: hallitsevan liikkeen estimointia ja liikepohjaista kuvan segmentointia. Tutkituissa ratkaisuissa lähtökohtana käytetään lohkosovitukseen perustuvaa paikallisen liikkeen määritystä, jossa sovituksen kriteerinä käytetään poikkeutettujen kehysten pikseliarvojen erotusta. Tähän liittyen tarkastellaan estimoinnin luotettavuuden analyysin tekniikoita ja näiden hyödyntämistä edellä mainittujen tehtävien ratkaisuissa. Yleensä ottaen paikallisen liikkeen estimointia vaikeuttaa apertuuriongelma. Tämän vuoksi tarvitaan analyysitekniikoita, jotka kykenevät antamaan täydentävää tietoa liike-estimaattien luotettavuudesta. Työn ensimmäisessä osassa kehitetty analyysimenetelmä käyttää lähtötietona lohkosovituksen kriteerin arvoja, jotka on saatu eri liikekandidaateille. Erotuksena aiempiin menetelmiin kehitetty ratkaisu ottaa huomioon kuvagradientin vaikutuksen. Työn toisessa osassa tutkitaan nelivaiheista piirrepohjaista ratkaisua hallitsevan liikkeen estimoimiseksi. Perushavaintoina mallissa käytetään liikepiirteitä, jotka koostuvat valittujen kuvapisteiden koordinaateista, näissä pisteissä lasketuista liike-estimaateista ja estimaattien epävarmuuden esityksestä. Jälkimmäinen esitetään parametrisessa muodossa käyttäen laskentaan työn ensimmäisessä osassa esitettyä menetelmää. Tätä epävarmuustietoa käytetään piirteiden painottamiseen hallitsevan liikkeen estimoinnissa. Lisäksi tutkitaan gradienttipohjaista piirteiden valintaa. Kokeellisessa osassa erilaisia suunnitteluvalintoja verrataan toisiinsa käyttäen synteettisiä ja todellisia kuvasekvenssejä. Väitöstyön kolmannessa osassa esitetään piirrepohjainen menetelmä taustan ja etualan kohteen liikkeiden erottamiseksi toisistaan. Algoritmi tekee analyysin kahta liikettä sisältävälle näkymälle käyttäen sekä spatiaalista että ajallista segmentointitiedon välittämistä. Piirteiden painotus hyödyntää epävarmuustietoa tässä yhteydessä, jonka osoitetaan kokeellisesti parantavan liike-estimoinnin suorituskykyä. Viimeisessä osassa kehitetään viitekehys liikepohjaisen kohteen ilmaisun, segmentoinnin ja seurannan toteutukselle. Se perustuu lohkopohjaiseen esitystapaan ja näytteistyksen soveltamiseen liikkeen estimoinnissa. Analyysitekniikka segmentoinnin määrittämiseksi esitellään. Lopuksi ratkaisu integroidaan työn kolmannessa osassa esitetyn menetelmän kanssa, ja menetelmien yhdistelmän osoitetaan kokeellisesti parantavan sekä näytteistyksen tehokkuutta että segmentoinnin tarkkuutta
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.
Full textSim, Hak Chuah. "Invariant object matching with a modified dynamic link network." Thesis, University of Southampton, 1999. https://eprints.soton.ac.uk/256269/.
Full textKrupnik, Amnon. "Multiple-patch matching in the object space for aerotriangulation /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487857546386844.
Full textBooks on the topic "Object matching"
Lamdan, Yehezkel. Object recognition by affine invariant matching. New York: Courant Institute of Mathematical Sciences, New York University, 1988.
Find full textBastuscheck, C. Marc. Object recognition by 3-dimensional curve matching. New York: Courant Institute of Mathematical Sciences, New York University, 1986.
Find full textDawson, K. M. Implicit model matching as an approach to three-dimensional object recognition. Dublin: Trinity College, Department of Computer Science, 1991.
Find full textLee, Raymond Shu Tak. Invariant object recognition based on elastic graph matching: Theory and applications. Amsterdam: IOS Press, 2003.
Find full textInformation routing, correspondence finding, and object recognition in the brain. Berlin: Springer-Verlag, 2010.
Find full textKao, Chang-Lung. Affine invariant matching of noisy objects. Monterey, Calif: Naval Postgraduate School, 1989.
Find full textAbdelmalek, Nabih N. A computer vision system for matching 3-D range data objects. Ottawa, Ont: National Research Council Canada, Division of Electrical Engineering, 1987.
Find full textSchwartz, Jacob T. Identification of partially obscured objects in two dimensions by matching of noisy 'characteristic curves,'. New York: Courant Institute of Mathematical Sciences, New York University, 1985.
Find full textYang, Cong. Object Shape Generation, Representation and Matching. Logos Verlag Berlin, 2016.
Find full textSchonberg, Edith, C. Marc Bastuscheck, and Jacob T. Schwartz. Object Recognition by 3-Dimensional Curve Matching. Creative Media Partners, LLC, 2015.
Find full textBook chapters on the topic "Object matching"
Bennamoun, M., and G. J. Mamic. "Object Representation and Feature Matching." In Object Recognition, 161–94. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_4.
Full textTreiber, Marco. "Flexible Shape Matching." In An Introduction to Object Recognition, 117–43. London: Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-235-3_6.
Full textTiebe, Oliver, Cong Yang, Muhammad Hassan Khan, Marcin Grzegorzek, and Dominik Scarpin. "Stripes-Based Object Matching." In Computer and Information Science, 59–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40171-3_5.
Full textBennamoun, M., and G. J. Mamic. "Stereo Matching and Reconstruction of a Depth Map." In Object Recognition, 29–96. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_2.
Full textEmir, Burak, Martin Odersky, and John Williams. "Matching Objects with Patterns." In ECOOP 2007 – Object-Oriented Programming, 273–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73589-2_14.
Full textBerg, Alexander C., and Jitendra Malik. "Shape Matching and Object Recognition." In Toward Category-Level Object Recognition, 483–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11957959_25.
Full textLammari, Nadira, Jacky Akoka, and Isabelle Comyn-Wattiau. "Supporting Database Evolution: Using Ontologies Matching." In Object-Oriented Information Systems, 284–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45242-3_27.
Full textCootes, Tim F. "Deformable Object Modelling and Matching." In Computer Vision – ACCV 2010, 1–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19315-6_1.
Full textCho, Tai-Hoon. "Object Matching Using Generalized Hough Transform and Chamfer Matching." In Lecture Notes in Computer Science, 1253–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_171.
Full textShan, Y., H. S. Sawhney, B. Matei, and R. Kumar. "Partial Object Matching with Shapeme Histograms." In Lecture Notes in Computer Science, 442–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24672-5_35.
Full textConference papers on the topic "Object matching"
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.
Full textZardetto, 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.
Full textChoe, 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.
Full textHuang, 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.
Full textPon, 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.
Full textHu, 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.
Full textGrove, 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.
Full textArandjelovi, 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.
Full textCelenk, 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.
Full textMa, 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}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/45.
Full textReports on the topic "Object matching"
Gyaourova, A., C. Kamath, and S. Cheung. Block Matching for Object Tracking. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/15009731.
Full textSmith, David. Parallel approximate string matching applied to occluded object recognition. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5608.
Full textCass, Todd A. Feature Matching for Object Localization in the Presence of Uncertainty. Fort Belvoir, VA: Defense Technical Information Center, May 1990. http://dx.doi.org/10.21236/ada231405.
Full textLuo, Ming, Daniel DeMenthon, Xiaodong Yu, and David Doermann. SOFTCBIR: Object Searching in Videos Combining Keypoint Matching and Graduated Assignment. Fort Belvoir, VA: Defense Technical Information Center, May 2006. http://dx.doi.org/10.21236/ada448477.
Full textAsari, 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. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), December 2015. http://dx.doi.org/10.55274/r0010891.
Full textLove, N., and C. Kamath. An Empirical Study of Block Matching Techniques for the Detection of Moving Objects. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/898460.
Full text