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 mlmber 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.
Cite
CITATION STYLE
Blanz, V., Schölkopf, B., Bülthoff, H., Burges, C., Vapnik, V., & Vetter, T. (1996). Comparison of view-based object recognition algorithms using realistic 3D models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1112 LNCS, pp. 251–256). Springer Verlag. https://doi.org/10.1007/3-540-61510-5_45
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