Improved range image segmentation by analyzing surface fit patterns
Computer Vision and Image Understanding
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EURASIP Journal on Applied Signal Processing
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Proceedings of the 30th DAGM symposium on Pattern Recognition
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SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Improved range image segmentation by analyzing surface fit patterns
Computer Vision and Image Understanding
Revisiting the evaluation of segmentation results: introducing confidence maps
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
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MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
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SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
k nearest neighbor using ensemble clustering
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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International Journal of Business Intelligence and Data Mining
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Previous performance evaluation of range image segmentation algorithms has depended on manual tuning of algorithm parameters, and has lacked a basis for a test of the significance of differences between algorithms. We present an automated framework for evaluating the performance of range image segmentation algorithms. Automated tuning of algorithm parameters in this framework results in performance as good as that previously obtained with careful manual tuning by the algorithm developers. Use of multiple training and test sets of images provides the basis for a test of the significance of performance differences between algorithms. The framework implementation includes range images, ground truth overlays, program source code, and shell scripts. This framework should make it possible to objectively and reliably compare the performance of range image segmentation algorithms; allow informed experimental feedback for the design of improved segmentation algorithms. The framework is demonstrated using range images, but in principle it could be used to evaluate region segmentation algorithms for any type of image.