Chandra Shekhar

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We propose a framework for the development of vision systems that incorporate, along with the executable computer algorithms, the problem-solving knowledge required to obtain optimal performance from them. In this approach, the user provides the input data, specifies the vision task to be performed, and then provides feedback in the form of qualitative(More)
We describe a framework for aligning images without needing to establish explicit feature correspondences. We assume that the geometry between the two images can be adequately described by an aane transformation and develop a framework that uses the statistical distribution of geometric properties of image contours to estimate the relevant transformation(More)
In this paper we present the results of a comparative study of linear and kernel-based methods for face recognition. The methods used for dimensionality reduction are Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA), Linear Discriminant Analysis (LDA) and Kernel Discriminant Analysis (KDA). The methods used for classification(More)