Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning - Special issue on learning in autonomous robots
Spatial Color Indexing and Applications
International Journal of Computer Vision
Natural basis functions and topographic memory for face recognition
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Learning-based robot vision: principles and applications
Learning-based robot vision: principles and applications
Shaping receptive fields for affine invariance
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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A general-purpose object indexing technique is described that combines the virtues of principal component analysis with the favorable matching properties of high-dimensional spaces to achieve high-precision recognition. An object is represented by a set of high-dimensional iconic feature vectors comprised of the responses of derivatives of Gaussian filters at a range of orientations and scales. Since these filters can be shown to form the eigenvectors of arbitrary images containing both natural and man-made structures, they are well-suited for indexing in disparate domains. The indexing algorithm uses an active vision system in conjunction with a modified form of Kanerva's (1988, 1993) sparse distributed memory which facilitates interpolation between views and provides a convenient platform for learning the association between an object's appearance and its identity. The robustness of the indexing method was experimentally confirmed by subjecting the method to a range of viewing conditions and the accuracy was verified using a well-known model database containing a number of complex 3D objects under varying pose.