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Comparison of view-based object recognition algorithms using realistic 3D models

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83815

Blanz,  V
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83839

Bülthoff,  HH
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84280

Vetter,  T
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Blanz, V., Schölkopf, B., Bülthoff, H., Burges C, Vapnik, V., & Vetter, T. (1996). Comparison of view-based object recognition algorithms using realistic 3D models. Artificial Neural Networks, Proceedings of the International Conference on Artificial Neural Networks, (Eds.) C. von der Malsburg, W. von Seelen, J.-C. Vorbrüggen, B. Sendhoff. Springer Lecture Notes in Computer Science, Bochum 1996, 251-256.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-EB4C-5
Abstract
Two view-based object recognition algorithms are compared: (1) a heuristic algorithm based on oriented filters, and (2) a support vector learning machine trained on low-resolution images of the objects. Classification performance is assessed using a high number of images generated by a computer graphics system under precisely controlled conditions. Training- and test-images show a set of 25 realistic three-dimensional models of chairs from viewing directions spread over the upper half of the viewing sphere. The percentage of correct identification of all 25 objects is measured.