{'fraction_non_alphanumeric': 0.016159695817490494, 'fraction_numerical': 0.0038022813688212928, 'mean_word_length': 5.157894736842105, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Support vector machines and Gabor kernels for object recognition on a humanoid with active foveated vision

Object recognition requires a robot to perform a number of nontrivial tasks such as finding objects of interest, directing its eyes towards the objects, pursuing them, and identifying the objects once they appear in the robot's central vision. We have recently developed a recognition system on a humanoid robot, which makes use of foveated vision to accomplish these tasks (A Ude, et al., 2003). In this paper we present several substantial improvements to this system. We present a biologically motivated object representation scheme based on Gabor kernel functions and show how to employ support vector machines to identify known objects in foveal images based on this representation. A mechanism for visual search is integrated into the system to find objects of interest in peripheral images. The framework also includes a control scheme for eye movements, which are directed using the results of attentive processing in peripheral images.