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.
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© 1996 Springer-Verlag Berlin Heidelberg
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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: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_45
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DOI: https://doi.org/10.1007/3-540-61510-5_45
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