Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in range images based on scan line approximation
Computer Vision and Image Understanding
An Adaptive Contour Closure Algorithm and Its Experimental Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Range Images through the Integration of Different Strate Gies
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Range Image Segmentation: Adaptive Grouping of Edges into Regions
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
Machine Vision and Applications
Search-Based Contour Closure in Range Images
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Improvement in Range Segmentation Parameters Tuning
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Optimal Range Segmentation Parameters through Genetic Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Comparing Curved-Surface Range Image Segmenters
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Automated performance evaluation of range image segmentation algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
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We propose a new approach to range image segmentation of planar and curved surface scenes. Our method is mainly an extended design of an existing algorithm, which was guided by a framework of performance evaluation. We choose the range segmentation algorithm developed by Jiang and Bunke as our baseline algorithm, which is last and has shown relatively high performance in several experimental performance evaluation studies. We analyze the types of errors made by the algorithm, propose design modifications to decrease the error rate, and experimentally verify that the new approach achieves statistically significant performance improvement. Whereas the baseline algorithm applies the edge-linking uniformly to all edge pixels to segment a region, the modified algorithm selects high potential edge areas in the region by analyzing the surface fit pattern and gives priority of edge-linking to those areas. The contributions of this work are (1) an improved algorithm for segmentation of range images of both planar and curved surface scenes, and (2) a demonstration of using empirical performance evaluation to guide algorithm design and modification to achieve better performance.