Invariant surface characteristics for 3D object recognition in range images
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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BONSAI: 3D Object Recognition Using Constrained Search
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Characterization of Signals from Multiscale Edges
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast segmentation of range images into planar regions by scan line grouping
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Experiments in Curvature-Based Segmentation of Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge detection in range images based on scan line approximation
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3D object recognition: Representation and matching
Statistics and Computing
Extracting Objects from Range and Radiance Images
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Results on Range Image Segmentation for Service Robots
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Noise versus Facial Expression on 3D Face Recognition
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Stereo-based 3D Face Modeling using Annealing in Local Energy Minimization
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Face^3 a 2D+3D Robust Face Recognition System
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
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The volume of raw range image data that is required to represent just a single scene can be extensive; hence direct interpretation of range images can incur a very high computational cost. Range image feature extraction has been identified as a mechanism to produce a more compact scene representation, in particular using features such as edges and surfaces, and hence enables less costly scene interpretation for applications such as object recognition and robot navigation. We present an approach to edge detection in range images that can be used directly with any range data, regardless of whether the data have regular or irregular spatial distribution. The approach is evaluated with respect to accuracy of both edge location and visual results are also provided.