Книги з теми "Robot vision Mathematical models"

Ji suan zhi neng ji qi zai san wei biao mian sao miao ji qi ren xi tong zhong de ying yong. Dalian Shi: Dalian hai shi da xue chu ban she, 2012.

Bochsler, Daniel C. Robotic space simulation: Integration of vision algorithms into an orbital operations simulation. [Houston, Tex.]: Research Institute for Computing and Information Systems, University of Houston--Clear Lake, 1987.

Jaklič, Aleš. Segmentation and recovery of superquadrics. Dordrecht: Kluwer Academic Publishers, 2000.

International Workshop on Visuomotor Coordination in Amphibians: Experiments, Comparisons, Models, and Robots (1987 Kassel, Germany). Visuomotor coordination: Amphibians, comparisons, models, and robots. New York: Plenum Press, 1989.

Paragios, Nikos, Yunmei Chen, and Olivier Faugeras, eds. Handbook of Mathematical Models in Computer Vision. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-28831-7.

Megahed, Saïd M. Principles of robot modelling and simulation. Chichester: Wiley, 1993.

Megahed, Saïd M. Principles of robot modelling and simulation. Chichester: J. Wiley, 1993.

Stefan, Türk, ed. The DFVLR models 1 and 2 of the Manutec r3 robot. Oberpfaffenhofen: DFVLR, Institut für Dynamik der Flugsysteme, 1988.

Jepson, Allan D. Mixture models for optical flow computation. Toronto: University of Toronto, Dept. of Computer Science, 1993.

Jepson, Allan D. What is a percept? Toronto: University of Toronto, Dept. of Computer Science, 1993.

Cheung, Catherine Kwok-Wai. Eigenpaxels in low-level vision: A theory of cell development, function and organisation. [Downsview, Ont.]: University of Toronto, Institute for Aerospace Studies, 2002.

Symposium on Noise and Disorder in Vision (1987 Toronto, Ont.). Symposium on Noise and Disorder in Vision: 1987 lectures in vision, University of Toronto. Toronto: University of Toronto, Dept. of Computer Science, 1987.

Chen, Ke, Carola-Bibiane Schönlieb, Xue-Cheng Tai, and Laurent Younces, eds. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03009-4.

Li, S. Z. Markov random field modeling in computer vision. New York: Springer-Verlag, 1995.

Xiao, Bai, Jian Cheng, and Edwin R. Hancock. Graph-based methods in computer vision: Developments and applications. Hershey: Information Science Reference, 2012.

Barker, L. Keith. Theoretical three- and four-axis gimbal robot wrists. [s.l.]: National Aeronautics and Space Administration Scientific and Technical Information Branch, 1986.

Amini, Amir A. Measurement of Cardiac Deformations from MRI: Physical and Mathematical Models. Dordrecht: Springer Netherlands, 2002.

B, Fisher R., and Institute of Mathemtics and Its Applications., eds. Design and application of curves and surfaces: Mathematics of surfaces V : based on the proceedings of the fifth Conference on the Mathematics of Surfaces organized by the Institute of Mathematics and its Applications, held in Edinburgh in September 1992. Oxford: Clarendon Press, 1994.

1931-, Rosenfeld Azriel, ed. A pyramid framework for early vision: Multiresolutional computer vision. Dordrecht: Kluwer Academic Publishers, 1994.

Petitot, Jean. Neurogéométrie de la vision: Modèles mathématiques et physiques des architectures fonctionnelles. [Palaiseau]: Les Éditions de l'École Polytechnique, 2008.

K, Hung George, and Ciuffreda Kenneth J. 1947-, eds. Models of the visual system. New York: Kluwer Academic/Plenum Publishers, 2002.

N, Wilson Joseph, ed. Handbook of computer vision algorithms in image algebra. 2nd ed. Boca Raton: CRC Press, 2001.

N, Wilson Joseph, ed. Handbook of computer vision algorithms in image algebra. Boca Raton: CRC Press, 1996.

G, Gettinby, ed. Experimental design techniques in statistical practice: A practical software-based approach. Chichester, W. Sussex, England: Horwood Pub., 1998.

Ming-Chih, Chien, ed. Adaptive control of robot manipulators: A unified regressor-free approach. New Jersey: World Scientific Publishing, 2010.

Physics-based deformable models: Applications to computer vision, graphics, and medical imaging. Boston: Kluwer Academic, 1997.

Symposium of Mathematical Aspects of Image Processing and Computer Vision (10th 2010 Sapporo-shi, Japan). Mathematical aspects of image processing and computer vision 2010: Research Center for Integrative Mathematics symposium : the 10th symposium of MAIPCV. Sapporo: Research Center for Integrative Mathematics, Hokkaido University, 2010.

CIE Technical Committee TC-8-01. A colour appearance model for colour management systems: CIEAMO2. Vienna, Austria: CIE Central Bureau, 2004.

Postaire, Jack-Gérard. De l'image à la décision: Analyse des images numériques et théorie de la décision. [Paris]: Dunod, 1987.

Informat Łsionnai Ła teorii Ła identifikat Łsii. Moskva: Nauka, Fizmatlit, 1995.

Han, Ding, and Xiong You-Lun, eds. Fundamentals of robotic grasping and fixturing. Singapore: World Scientific, 2007.

Marr, David. Vision: A computational investigation into the human representation and processing of visual information. Cambridge, Mass: MIT Press, 2010.

Vision: A computational investigation into the human representation and processing of visual information. Cambridge, MA: MIT Press, 2010.

A computational perspective on visual attention. Cambridge, Mass: MIT Press, 2011.

Xing, Jing. Reexamination of color vision standards. Washington, D.C: Federal Aviation Administration, Office of Aerospace Medicine, 2006.

Perus, Mitja. Biological and quantum computing for human vision: Holonomic models and applications. Hershey, PA: Medical Information Science Reference, 2011.

Metaxas, Dimitris N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging. Boston, MA: Springer US, 1997.

Barron, John Leonard. Computing motion and structure from noisy, time-varying image velocity information. Toronto: University of Toronto, Dept. of Computer Science, 1988.

Hyvärinen, Aapo. Natural Image Statistics: A Probabilistic Approach to Early Computational Vision. London: Springer London, 2009.

Szeliski, Richard. Bayesian modeling of uncertainty in low-level vision. Boston: Kluwer Academic Publishers, 1989.

Kiong, Loo Chu. Biological and quantum computing for human vision: Holonomic models and applications. Hershey, PA: Medical Information Science Reference, 2011.

Zhao, Jingshan. Ji qi ren ji gou zi you du fen xi li lun =: Analytical theory of degrees of freedom for robot mechanisms. Bei jing: Ke xue chu ban she, 2009.

Bart M. ter Haar Romeny. Front-end vision and multi-scale image analysis: Multi-scale computer vision theory and applications, written in Mathematica. Dordrecht: Kluwer Academic, 2003.

Romeny, Bart M. ter Haar. Front-end vision and multi-scale image analysis: Multi-scale computer vision theory and applications, written in Mathematica. [Dordrecht]: Springer, 2008.

Bart M. ter Haar Romeny. Front-end vision and multi-scale image analysis: Multi-scale computer vision theory and applications, written in Mathematica. [Dordrecht]: Springer, 2008.

1959-, Valentine Tim, British Psychological Society. Welsh Branch., and International Conference on Face Processing (1993 : Cardiff, Wales), eds. Cognitive and computational aspects of face recognition: Explorations in face space. London: Routledge, 1995.

1965-, Zhang Zhengyou, ed. Boosting-based face detection and adaptation. San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA): Morgan & Claypool, 2010.

Ali, Mohamad-Djafari, ed. Inverse problems in vision and 3D tomography. London: ISTE, 2009.

Choudhary, Alok N. NETRA, a parallel architecture for integrated vision systems. [Urbana, Ill.]: Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1989.

Choudhary, Alok N. NETRA, a parallel architecture for integrated vision systems. [Urbana, Ill.]: Coordinated Science Laboratory, College of Engineering, University of Illinois at Urbana-Champaign, 1989.