An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Extraction to Surface Primitives from Range Images
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Damped Newton Algorithms for Matrix Factorization with Missing Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Clustering Appearance for Scene Analysis
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Photometric Stereo with General, Unknown Lighting
International Journal of Computer Vision
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Appearance Derivatives for Isonormal Clustering of Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-based perceptual segmentation of stereo vision 3D images at multiple abstraction levels
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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This paper describes a new algorithm for segmenting 2D images by taking into account 3D shape information. The proposed approach consists of two stages. In the first stage, the 3D surface normals of the objects present in the scene are estimated through robust photometric stereo. Then, the image is segmented by grouping its pixels according to their estimated normals through graph-based clustering. One of the advantages of the proposed approach is that, although the segmentation is based on the 3D shape of the objects, the photometric stereo stage used to estimate the 3D normals only requires a set of 2D images. This paper provides an extensive validation of the proposed approach by comparing it with several image segmentation algorithms. Particularly, it is compared with both appearance-based image segmentation algorithms and shape-based ones. Experimental results confirm that the latter are more suitable when the objective is to segment the objects or surfaces present in the scene. Moreover, results show that the proposed approach yields the best image segmentation in most of the cases.