Surfaces from Stereo: Integrating Feature Matching, Disparity Estimation, and Contour Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence by Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV 90 Proceedings of the first european conference on Computer vision
International Journal of Computer Vision - Special issue on computer vision research at NEC Research Institute
Image-based modeling and photo editing
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Face Recognition Using Line Edge Map
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Simple Stereo Algorithm to Recover Precise Object Boundaries and Smooth Surfaces
International Journal of Computer Vision
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-Time Visual Tracking of Complex Structures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Global Solution to Sparse Correspondence Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation of Multiple Salient Closed Contours from Real Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion from 3D line correspondences: linear and non-linear solutions
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Disparity/segmentation analysis: matching with an adaptive window and depth-driven segmentation
IEEE Transactions on Circuits and Systems for Video Technology
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Two objectives of 3D computer vision are high processing speed and precise recovery of object boundaries. This paper addresses these issues by presenting an algorithm that combines feature-based 3D matching with Compressed Image Correlation. The algorithm uses an image compression scheme that retains pixel values in high intensity gradient areas while eliminating pixels with little correlation information in smooth surface regions. The remaining pixels are stored in sparse format along with their relative locations encoded into 32-bit words. The result is a highly reduced image data set containing distinct features at object boundaries. Consequently, far fewer memory calls and data entry comparisons are required to accurately determine edge movement. In addition, by utilizing an error correlation function, pixel comparisons are made through single integer calculations eliminating time consuming multiplication and floating point arithmetic. Thus, this algorithm typically results in much higher correlation speeds than spectral correlation and SSD algorithms. Unlike the traditional fixed window sorting scheme, adaptive correlation window positioning is implemented by dynamically placing object boundaries at the center of each correlation window. Processing speed is further improved by compressing and correlating the images in only the direction of disparity motion between frames. Test results on both simulated disparities and real motion image pair are presented.