Harris, John G. (1991) Analog models for early vision. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechETD:etd-06202007-093347
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.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Degree Grantor:||California Institute of Technology|
|Major Option:||Computation and Neural Systems|
|Thesis Availability:||Public (worldwide access)|
|Defense Date:||30 May 1991|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Imported from ETD-db|
|Deposited On:||13 Jul 2007|
|Last Modified:||26 Dec 2012 02:53|
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