An Adaptive Contour Closure Algorithm and Its Experimental Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
A New Technique for the Extraction and Tracking of Surfaces in Range Image Sequences
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
Markov random field modeled range image segmentation
Pattern Recognition Letters
Range Image Segmentation by an Effective Jump-Diffusion Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved range image segmentation by analyzing surface fit patterns
Computer Vision and Image Understanding
Efficient search and verification for function based classification from real range images
Computer Vision and Image Understanding
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Distance measures for image segmentation evaluation
EURASIP Journal on Applied Signal Processing
Improved range image segmentation by analyzing surface fit patterns
Computer Vision and Image Understanding
Extraction and tracking of surfaces in range image sequences
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
Image segmentation evaluation by techniques of comparing clusterings
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
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This work focuses on creating a framework for objectively evaluating the performance of range image segmentation algorithms. The algorithms are evaluated in terms of correct segmentation, over- and under- segmentation, missed and noise regions. A set of images with ground truth was created for this work. The images were captured using a structured light scanner. Images used in the evaluation contain planar, spherical, cylindrical, toroidal andconical surface patches. The different surface patches in each image were manually identified to establish ground truth for performance evaluation. Two segmentation algorithms from the literature are compared.