Computer Vision
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Computer Vision: Algorithms and Applications
By Richard SzeliskiAgarwala, A., Agrawala, M., Cohen, M., Salesin, D., and Szeliski, R. (2006). Photographing long scenes with multi-viewpoint panoramas. ACM Transactions on Graphics (Proc. SIGGRAPH 2006), 25(3):853–861. Agarwala, A., Dontcheva, M., ...
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Computer Vision: Principles, Algorithms, Applications, Learning
By E. R. DaviesComputer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory ...
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Computer Vision: Algorithms and Applications
By Richard SzeliskiThis text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford.
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Computer Vision: Models, Learning, and Inference
By Simon J. D. PrinceNotes Regression methods: Rasmussen and Williams (2006) is a comprehensive resource on Gaussian processes. The relevance vector machine was first introduced by Tipping (2001). Several innovations within the vision community have ...
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Computer Vision: Models, Learning, and Inference
By Simon J. D. PrinceA modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
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Computer Vision: A Modern Approach
By David A. Forsyth, Jean PonceAppropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering.
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Computer Vision: Aav 2010
By Ron Kimmel, Reinhard Klette, Akihiro SugimotoThe four-volume set LNCS 6492-6495 constitutes the thoroughly refereed post-proceedings of the 10th Asian Conference on Computer Vision, ACCV 2009, held in Queenstown, New Zealand in November 2010.
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Computer Vision
By Linda G. Shapiro, George C. StockmanUsing a progressive intuitive/mathematical approach, this introduction to computer vision provides necessary theory and examples for practitioners who work in fields where significant information must be extracted...
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Computer Vision: Algorithms and Applications
By Richard SzeliskiHe is currently an Affiliate Professor at the University of Washington where he co-developed (with Steve Seitz) the widely adopted computer vision curriculum on which this book is based.
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Computer Vision: Principles, Algorithms, Applications, Learning
By E. R. DaviesComputer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory ...
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Computer Vision: From Surfaces to 3D Objects
By Christopher W. TylerFor complex object structures, however, such a planar approach cannot determine object shape; the structural edges have to be encoded in terms of their full 3D spatial configuration. Computer Vision: From Surfaces to 3D Objects i
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Computer Vision: Concepts, Methodologies, Tools, and Applications
By Management Association, Information ResourcesPrinciples of Appearance Acquisition and Representation. Foundations and Trends in Computer Graphics and Vision, 4(2), 75–191. doi:10.1561/0600000022 Wilkie, A., Weidlich, A., Magnor, M., & Chalmers, A. (2009). Predictive Rendering.
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Computer Vision: A Reference Guide
An A-Z format of over 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of Computer Vision. Over 200 Authors from both industry and academia contributed to this volume.
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Computer Vision: Algorithms and Applications
By Richard SzeliskiThis text draws on that experience, as well as on computer vision courses he has taught at the University of Washington and Stanford.
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Computer Vision
By Christopher M. Brown, Dana Harry BallardComputer Vision
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Computer Vision: A Modernl Approach
By David A. Forsyth, Jean PonceComputer Vision: A Modernl Approach
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Computer Vision: Second CCF Chinese Conference, CCCV 2017, Tianjin, China, October 11–14, 2017, Proceedings, Part III
By Liang Wang, Qingshan Liu, Jinfeng YangMeng Jian, Shijie Zhang, Xiangdong Wang, Yudi He, and Lifang Wu Human Action Recognition Based on Sub-data Learning . . . . . . . . . . . . . . . Yang Chen, Tian Wang, Jiakun Li, Xiaowei Lv, and Hichem Snoussi Hashing Based State ...
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Computer Vision
By Zhihui Xiong... landmark are connected. Utilizing the depressions obtained from images and demarcated parameters of landmarks, the physical space position of the AGV is confirmed. We tag the landmarks as A and B. In order to set up the physical ...
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Computer Vision: A Modern Approach
By David Forsyth, Jean PonceAppropriate for upper-division undergraduate and graduate level courses in computer vision found in departments of computer science, computer engineering and electrical engineering, this book offers a treatment of modern computer vision ...