#134: Feature fusion and label propagation for textured object video segmentation

V. B. S. Prasath, R. Pelapur, K. Palaniappan, and G. Seetharaman

Proc. SPIE Conf. Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II (Defense, Security and Sensing), Volume 9089, 2014

segmentation, active contours, video, globally convex

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We study an efficient texture segmentation model for multichannel videos using a local feature fitting based active contours scheme. We propose a flexible motion segmentation approach using features computed from texture, intensity components in a globally convex continuous optimization and fusion framework. A fast numerical implementation is described using an efficient dual minimization formulation and experimental results on synthetic and real color videos indicate the superior performance of the proposed method compared to related approaches. The novel contributions include the use of local feature density functions in the context of a luminance-chromaticity decomposition combined with a globally convex active contour variational method to capture texture variations for video object segmentation.