Analog models for early vision

Citation

Harris, John G. (1991) Analog models for early vision. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-06202007-093347

Abstract

Analog models provide a novel framework for understanding and developing algorithms for computer vision. This thesis introduces several extensions to well-known resistive network techniques for solving early vision problems. First, constraint boxes are developed as a general methodology for mapping regularization-based algorithms onto stable analog hardware. These multiterminal resistor systems solve low-level vision problems by minimizing a global Lyapunov energy. Second, a circuit element called the resistive fuse is introduced to extend these networks for discontinuity detection. This is the first hardware circuit that explicitly implements line-process discontinuities. Since resistive fuse networks must minimize a non-convex energy function that may contain local minima, complex annealing or continuation methods are necessary for adequate solutions of the problem. Third, the tiny-tanh network is proposed as a new mechanism for discontinuity detection that is not plagued by problems with local minima. A piece-wise constant segmentation is performed through minimization of a convex Lyapunov energy.

Thesis Files