New training method for linear separable threshold Boolean filters Conference Paper uri icon

abstract

  • The key point of the LS-TBF (Linear Separable Threshold Boolean Filter) design is the training of the Linear Separable Boolean Function (LSBF). The standard LS-TBF design method approximates the LSBF with a linear function. This procedure leads to a closed form expression of the filter weights but it does not provide the optimal solution. Other LSBF training algorithms are not really applicable in filter design because they either require too many iterations or do not offer a reasonable stability. This paper introduces a new gradient- type method applicable for LS-TBF design. The proposed algorithm is able to reach the optimal solution in very few iterations. In order to provide high convergence rate together with stability the method uses multiple gain factors at the same time. This way the proposed algorithm simulates a continuous-time implementation of the steepest-descent method. While the known training methods use many iterations the proposed one minimizes the number of iterations but increases the amount of calculations at each step. Consequently the computational effort spent for additional operations like disk access, windowing and thresholding becomes negligible and also the overall effort is very much reduced. Among other advantages the proposed training algorithm is very suitable for parallel implementation. 2004 Copyright SPIE - The International Society for Optical Engineering.

name of conference

  • Statistical and Stochastic Methods in Image Processing II

published proceedings

  • Proceedings of SPIE

author list (cited authors)

  • Sarca, O. V., Astola, J. T., & Dougherty, E. R.

citation count

  • 1

complete list of authors

  • Sarca, Octavian V||Astola, Jaakko T||Dougherty, Edward R

editor list (cited editors)

  • Preteux, F. J., Davidson, J. L., & Dougherty, E. R.

publication date

  • October 1997