A fast and robust feature-based 3D algorithm using compressed image correlation Export

@article{citeulike:2719114, abstract = {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.}, author = {Tan, Sheng S. and Hart, Douglas P.}, citeulike-article-id = {2719114}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.patrec.2005.01.009}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V15-4G05M3W-2/1/410872f502c331fb080e6be71492a642}, doi = {10.1016/j.patrec.2005.01.009}, journal = {Pattern Recognition Letters}, keywords = {pattern, spatial}, month = {August}, number = {11}, pages = {1620--1631}, posted-at = {2008-04-25 18:37:13}, priority = {3}, title = {A fast and robust feature-based 3D algorithm using compressed image correlation}, url = {http://dx.doi.org/10.1016/j.patrec.2005.01.009}, volume = {26}, year = {2005} }

TY - JOUR ID - citeulike:2719114 L3 - citeulike-article-id:2719114 N2 - 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. IS - 11 JF - Pattern Recognition Letters EP - 1631 TI - A fast and robust feature-based 3D algorithm using compressed image correlation VL - 26 SP - 1620 KW - pattern KW - spatial AU - Tan, Sheng AU - Hart, Douglas PY - 2005/08// UR - http://dx.doi.org/10.1016/j.patrec.2005.01.009 ER -