Range Image Segmentation Using Multiple Markov Random Fields

スポンサーリンク

概要

• 論文の詳細を見る

• A method of range image segmentation using four Markov random field (MRF)s is described in this paper. MRFs are used in depth smoothing, gradient smoothing, edge detection and surface type labeling stage. First, range and its gradient images are smoothed preserving jump and roof edges respectively using line process concept one after another. Then jump and roof edges are extracted, combined and refined using penalizing undesirable edge patterns. Finally, curvatures are computed and the surface types are labeled according to the signs of principal curvatures. The surface type labels are refined using winner-takes-all layers in the last stage. The final output is a set of regions with its exact surface type. The energy function is used in order to represent constraints of each stage and the minimum energy state is found using iterative method. Several experimental results show the generality of our approach and the execution speed of the proposed method is faster than that of a typical region merging method. This promises practical applications of our method.

• 社団法人電子情報通信学会の論文
• 1994-03-25

著者

• PARK Kyu

  Computer Engineering Research Laboratory, Department of Electrical Engineering and Computer Science

• Park Kyu

  Computer Engineering And Research Laboratory Dept. Of Electrical Engineering Korea Advanced Institut

• Chun Gook

  Computer Engineering and Research Laboratory, dept. of Electrical Engineering, Korea Advanced Instit

• Chun Gook

  Computer Engineering And Research Laboratory Dept. Of Electrical Engineering Korea Advanced Institut

関連論文

• A Scalable Multi-Host RAID-5 with Parity Consistency
• A Direct Hashing Directory for Fast Inode Lookup
• Range Image Segmentation Using Multiple Markov Random Fields
• A Method of 3D Object Reconstruction from a Series of Cross-Sectional Images (Special Issue on 3D Image Processing)

スポンサーリンク