Kalaivani Sundararajan

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In this paper, we present a head pose estimation method for unconstrained images using feature-based manifold embedding. The main challenge of manifold embedding methods is to learn a similarity kernel that is reflective of variations only due to head pose and ignore other sources of variation. To address this challenge, we have used the feature(More)
Feature tracking algorithms have conventionally tracked 'corner' features or windows with high spatial frequency content. However, this conventional point feature representation of scenes would be inappropriate for poorly textured image sequences like indoor image sequences. To overcome this problem, we propose a feature tracking algorithm which tracks(More)
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