3 July 2014 Feature fusion and label propagation for textured object video segmentation
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We study an efficient texture segmentation model for multichannel videos using a local feature fitting based active contour scheme. We propose a flexible motion segmentation approach using fused features computed from texture and intensity components in a globally convex continuous optimization and fusion framework. A fast numerical implementation is demonstrated using an efficient dual minimization formulation. The novel contributions include the fusion of local feature density functions including luminance-chromaticity and local texture in a globally convex active contour variational method, combined with label propagation in scale space using noisy sparse object labels initialized from long term optical flow-based point trajectories. We provide a proof-of-concept demonstration of this novel multi-scale label propagation approach to video object segmentation using synthetic textured video objects embedded in a noisy background and starting with sparse label set trajectories for each object.
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V. B. Surya Prasath, V. B. Surya Prasath, Rengarajan Pelapur, Rengarajan Pelapur, Kannappan Palaniappan, Kannappan Palaniappan, Gunasekaran Seetharaman, Gunasekaran Seetharaman, "Feature fusion and label propagation for textured object video segmentation", Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 908904 (3 July 2014); doi: 10.1117/12.2052983; https://doi.org/10.1117/12.2052983


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