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Xingwei Yang, Ph.D.

Affiliations: 
2011 Computer and Information Science Temple University, Philadelphia, PA, United States 
Area:
Computer Science
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"Xingwei Yang"
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Longin J. Latecki grad student 2011 Temple University
 (Shape Based Object Detection and Recognition in Silhouettes and Real Images.)

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Publications

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Adluru N, Yang X, Latecki LJ. (2015) Sequential Monte Carlo for Maximum Weight Subgraphs with Application to Solving Image Jigsaw Puzzles. International Journal of Computer Vision. 112: 319-341
Yang X, Prasad L, Latecki LJ. (2013) Affinity learning with diffusion on tensor product graph. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 28-38
Shen W, Bai X, Yang XW, et al. (2013) Skeleton pruning as trade-off between skeleton simplicity and reconstruction error Science China Information Sciences. 56: 1-14
Yang X, Bai X, Köknar-Tezel S, et al. (2013) Densifying distance spaces for shape and image retrieval Journal of Mathematical Imaging and Vision. 46: 12-28
Bai X, Li C, Yang X, et al. (2012) Shape retrieval and classification based on geodesic paths in skeleton graphs Graph-Based Methods in Computer Vision: Developments and Applications. 190-215
Yang X, Liu H, Latecki LJ. (2012) Contour-based object detection as dominant set computation Pattern Recognition. 45: 1927-1936
Liu H, Yang X, Latecki LJ, et al. (2012) Dense neighborhoods on affinity graph International Journal of Computer Vision. 98: 65-82
Yang X, Adluru N, Latecki LJ. (2011) Particle Filter with State Permutations for Solving Image Jigsaw Puzzles. Proceedings. Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2011: 2873-2880
Yang X, Adluru N, Latecki LJ. (2011) Particle filter with state permutations for solving image jigsaw puzzles Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2873-2880
Yang X, Latecki LJ. (2011) Affinity learning on a tensor product graph with applications to shape and image retrieval Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. 2369-2376
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