Comparison of view-based object recognition algorithms using realistic 3D models
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.
KeywordsSupport Vector Machine Structural Risk Minimization Support Vector Learn Machine Handwritten Digit Recognition High Spatial Frequency Information
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