Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for Graphics and Imag
Algorithms for Graphics and Imag
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of Planar Surfaces Behind Occlusions in Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency sequential surface organization for free-form object recognition
Computer Vision and Image Understanding
Parallel Edge-Region-Based Segmentation Algorithm Targeted at Reconfigurable MultiRing Network
The Journal of Supercomputing
Machine Vision and Applications
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
Integrated image and graphics technologies
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Automatic segmentation of unorganized noisy point clouds based on the Gaussian map
Computer-Aided Design
Object Detection and Localization in Clutter Range Images Using Edge Features
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Gradient operators for feature extraction and characterisation in range images
Pattern Recognition Letters
Segmenting free-form 3d objects by a function representation in spherical coordinates
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
Multimedia Tools and Applications
Point cloud segmentation for cultural heritage sites
VAST'11 Proceedings of the 12th International conference on Virtual Reality, Archaeology and Cultural Heritage
International Journal of Computer Vision
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In this correspondence, we present a new computationally efficient three-dimensional (3-D) object segmentation technique. The technique is based on the detection of edges in the image. The edges can be classified as belonging to one of the three categories: fold edges, semistep edges (defined here), and secondary edges. The 3-D image is sliced to create equidepth contours (EDCs). Three types of critical points are extracted from the EDCs. A subset of the edge pixels is extracted first using these critical points. The edges are grown from these pixels through the application of some masks proposed in this correspondence. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort is small since the masks are applied only over a small neighborhood of critical points (edge regions). Furthermore, the algorithm can be implemented in parallel, as edge growing from different regions can be carried out independently of each other.