Surfaces in range image understanding
Surfaces in range image understanding
Adaptive multiscale feature extraction from range data
Computer Vision, Graphics, and Image Processing
Computer Vision and Image Understanding
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
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
International Journal of Computer Vision
A Systematic Design Procedure for Scalable Near-Circular Laplacian of Gaussian Operators
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Range image segmentation based on randomized Hough transform
Pattern Recognition Letters
Architectural Modeling from Sparsely Scanned Range Data
International Journal of Computer Vision
Coarse-to-fine vision-based localization by indexing scale-Invariant features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Quantitative error measures for edge detection
Pattern Recognition
Object segmentation and classification using 3-D range camera
Journal of Visual Communication and Image Representation
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Feature extraction in image data has been investigated for many years, and more recently the problem of processing images containing irregularly distributed data has become prominent. Range data are now commonly used in the areas of image processing and computer vision. However, due to the data irregularity found in range images that occurs with a variety of image sensors, direct image processing, in particular edge detection, is a nontrivial problem. Typically, irregular range data would require to be interpolated to a regular grid prior to processing. One example of an edge detection technique that can be directly applied to range images is the scan-line approximation, but this does not employ exact data locations. Therefore, we present novel Laplacian operators that can be applied directly to irregularly distributed data, and in particular we focus on application to irregularly distributed 3-D range data for the purpose of edge detection. Within the data distribution framework commonly occurring in range data acquisition devices, our results illustrate that the approach works well over a range of levels of irregularity of data distribution. The use of Laplacian operators on range data is also found to be much less susceptible to noise than the traditional use of Laplacian operators on intensity images.