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Target Image Mask Correction Based on Skeleton Divergence

Pattern Recognition and Computer Vision Lab, Zhejiang Sci-Tech University, Hangzhou 310000, China
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Algorithms 2019, 12(12), 251; https://doi.org/10.3390/a12120251
Received: 25 October 2019 / Revised: 17 November 2019 / Accepted: 23 November 2019 / Published: 25 November 2019
Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms. View Full-Text
Keywords: skeleton divergence; edge merging; TOPSIS; simultaneous optimization binary; mask correction skeleton divergence; edge merging; TOPSIS; simultaneous optimization binary; mask correction
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Wang, Y.; Xu, Z.; Huang, W.; Han, Y.; Jiang, M. Target Image Mask Correction Based on Skeleton Divergence. Algorithms 2019, 12, 251.

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