Adaptive surface data compression
Signal Processing
Accurate and simple geometric calibration of multi-camera systems
Signal Processing
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometrical cloning of 3D objects via simultaneous registration of multiple range images
SMA '97 Proceedings of the 1997 International Conference on Shape Modeling and Applications (SMA '97)
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Laser Stripe Peak Detector for 3D Scanners. A FIR Filter Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Boundary Identification and Triangulation of STL Model
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01
A new approach for face recognition by sketches in photos
Signal Processing
Entropy controlled Laplacian regularization for least square regression
Signal Processing
Fast and robust laser stripe extraction for 3D reconstruction in industrial environments
Machine Vision and Applications
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
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It is very important to acquire accurate depth information of target object or scene for many applications in machine learning. The use of 3D reconstruction based on active laser triangulation technology is very complex in real application. One main problem is that most of these technologies detect light stripes by considering each column or row of the image as independent signals causing lack of robustness. In real application, variable illumination, uneven surface and imaging noise will make stripe detection fail. In this paper, by considering laser stripe distortion assumption, we adopt efficient belief propagation algorithm to extract center of laser stripe, which proves superior to existing peak detection approaches. Because of occlusion and low reflectivity, laser stripe captured by the sensor will be cut into several parts at some points, which are referred to as outliers. As for non-outlier, the SNR of that point is high and the disparity difference between left and right neighbor is slight. First, determine whether a point is an outlier or not by computing the weighted SNR and disparity difference. Then efficient belief propagation algorithm is adopted to infer the outlier map which is called labels in machine learning. Experimental results demonstrate the feasibility of our proposed approach.