Parallel motion estimation using an annealed Hopfield neural network

Yungsik Kim

doi:10.1117/12.186008

16 September 1994 Parallel motion estimation using an annealed Hopfield neural network

Yungsik Kim

Proceedings Volume 2308, Visual Communications and Image Processing '94; (1994) https://doi.org/10.1117/12.186008
Event: Visual Communications and Image Processing '94, 1994, Chicago, IL, United States

Abstract

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.

Citation Download Citation

Yungsik Kim "Parallel motion estimation using an annealed Hopfield neural network", Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); https://doi.org/10.1117/12.186008

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KEYWORDS

Motion estimation

Neural networks

Neurons

Motion measurement

Algorithm development

Lithium

Annealing

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