Stand-Alone Vision Sensor Design Based on Fuzzy Associative Database
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Stand-alone embedded vision system based on fuzzy associative database
Image and Vision Computing
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Local Image Descriptors Using Supervised Kernel ICA
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Uncorrelated multilinear principal component analysis for unsupervised multilinear subspace learning
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A survey of multilinear subspace learning for tensor data
Pattern Recognition
Vehicle classification from traffic surveillance videos at a finer granularity
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Object recognition based on three-dimensional model
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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This paper presents results on appearance-based three-dimensional (3-D) object recognition (3DOR) accomplished by utilizing a neural-network architecture developed based on independent component analysis (ICA). ICA has already been applied for face recognition in the literature with encouraging results. In this paper, we are exploring the possibility of utilizing the redundant information in the visual data to enhance the view based object recognition. The underlying premise here is that since ICA uses high-order statistics, it should in principle outperform principle component analysis (PCA), which does not utilize statistics higher than two, in the recognition task. Two databases of images captured by a CCD camera are used. It is demonstrated that ICA did perform better than PCA in one of the databases, but interestingly its performance was no better than PCA in the case of the second database. Thus, suggesting that the use of ICA may not necessarily always give better results than PCA, and that the application of ICA is highly data dependent. Various factors affecting the differences in the recognition performance using both methods are also discussed.