An annealed Hopfield neural network has been shown to solve an image segmentation problem and good image segmentation was successfully achieved. In this paper, a new motion estimation algorithm using an annealed Hopfield neural network is developed. Motion estimation process can simply be described as finding the corresponding pixels in the consecutive images. Optimization function E_me1 equals E_g to achieve this simple process is defined first. This optimization function finds the motion vector for a given pixel in a frame by finding a corresponding pixel in the next frame. However, the image sequence usually contains the noise. In this case, only finding the corresponding pixels does not work well in estimating the correct vector field. To make the motion vectors smooth within a moving object and to make the motion vectors different between the objects moving in different directions, weak continuity constraints terms, E_me2 equals E_d + E_s + E_p, are added to the previously defined optimization function E_me1, resulting in E_me equals E_me1 + E_me2. E_me2 controls the smoothness of the detected motion vectors within objects as well as maintains the motion vector boundaries between the objects moving to the different directions. Simulation are done for the synthetic image sequence and the real image sequence.

Date Published: 16 September 1994
PDF: 11 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994);

Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)