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Democratic Integration of Texture and Edge Planar Trackers with Local Quality Metric

This research presents a new probabilistic tracking framework that integrates visual cues by selecting and evaluating 'good' features for texture and edge cues. Two particle filter trackers run independently, and the master tracker output is computed through democratic integration of important factors.

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Democratic Integration of Texture and Edge Planar Trackers with Local Quality Metric

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  1. Combining Texture and Edge Planar Trackers based on a local Quality Metric A.H. Abdul Hafez Computer Science & Engg. Dept., Osmania University, Hyderabad, India Visesh Chari and C.V. JawaharCVIT, International Institute of Information Technology, Hyderabad, India • A new probabilistic tracking framework for integrating visual cues is presented here. • The framework allows selection of “good’’ features for each cue, along with factors of their important “goodness” factors. • Two particle filter trackers run independently. Using either texture or edge cues. • The output of the master tracker is computed by democratic integration using the important factors.

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