TY - GEN
T1 - On optimizing template matching via performance characterization
AU - Han, Tony X.
AU - Ramesh, Visvanathan
AU - Zhu, Ying
AU - Huang, Thomas S
PY - 2005/12/1
Y1 - 2005/12/1
N2 - Template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. Under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the SSD between the target and a warped template. The performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model. In this paper, we assume a discretization of the warp parameter space and derive the theoretical expression for the probability mass function (PMF) of the parameter estimate. As the PMF is also a function of the template size, we can optimize the choice of the template or block size by determining the template/block size that gives the estimate with minimum entropy. Experimental results illustrate the correctness of the theory. An experiment involving feature point tracking in face video is shown to illustrate the robustness of the algorithm in a real-world problem.
AB - Template matching is a fundamental operator in computer vision and is widely used in feature tracking, motion estimation, image alignment, and mosaicing. Under a certain parameterized warping model, the traditional template matching algorithm estimates the geometric warp parameters that minimize the SSD between the target and a warped template. The performance of the template matching can be characterized by deriving the distribution of warp parameter estimate as a function of the ideal template, the ideal warp parameters, and a given noise or perturbation model. In this paper, we assume a discretization of the warp parameter space and derive the theoretical expression for the probability mass function (PMF) of the parameter estimate. As the PMF is also a function of the template size, we can optimize the choice of the template or block size by determining the template/block size that gives the estimate with minimum entropy. Experimental results illustrate the correctness of the theory. An experiment involving feature point tracking in face video is shown to illustrate the robustness of the algorithm in a real-world problem.
UR - http://www.scopus.com/inward/record.url?scp=33745962057&partnerID=8YFLogxK
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U2 - 10.1109/ICCV.2005.178
DO - 10.1109/ICCV.2005.178
M3 - Conference contribution
AN - SCOPUS:33745962057
SN - 076952334X
SN - 9780769523347
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 182
EP - 189
BT - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
T2 - Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Y2 - 17 October 2005 through 20 October 2005
ER -