3 July 2014 Feature fusion and label propagation for textured object video segmentation
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Abstract
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, Rengarajan Pelapur, Kannappan Palaniappan, 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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