This study presents an alternative method to estimate motion parameters to the gradient-based method, which is known as Lucas-Kanade algorithm. The proposed method is a faster version of a hyperplane-intersection method. The hyperplane-intersection method estimates the motion parameters between images as an intersection position of estimated hyperplanes in a parameter space. The hyperplanes approximate the zero positions of partial derivatives of a continuous similarity measure with respect to each parameter. The method employs a straightforward computation to estimate the parameters, instead of using an iterative framework. The noniterative method is suitable for hardware implementation. The method is the region-based and intensity-based technique that is capable of using any dissimilarity or similarity measure such as sum of squared differences (SSD) or zero-mean normalized cross correlation (ZNCC), which can be selected adequately in consideration of a property of input image sequence and a required computation time. The faster version of the method is realized with pre-computed warped images of the template, which reduce the computational cost for each input frame. This study also compares the computational cost and the accuracy of the estimated parameters of the proposed algorithm with those of the gradient descent method. Experiments using synthesized-motion sequences and real image sequences are performed to confirm the comparisons. The faster version of the hyperplane-intersection method using ZNCC demonstrates robustness to a non-uniform illumination change in the image sequences.

Date Published: 19 January 2006
PDF: 12 pages
Proc. SPIE 6077, Visual Communications and Image Processing 2006, 607713 (19 January 2006); doi: 10.1117/12.641550

Published in SPIE Proceedings Vol. 6077:
Visual Communications and Image Processing 2006
John G. Apostolopoulos; Amir Said, Editor(s)