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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.

Item Type:Thesis (Dissertation (Ph.D.))
Degree Grantor:California Institute of Technology
Division:Biology
Major Option:Computation and Neural Systems
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Koch, Christof
Thesis Committee:
  • Koch, Christof (chair)
  • Barr, Alan H.
  • Mead, Carver
  • Van Essen, David
  • Goodman, Rodney M.
Defense Date:30 May 1991
Record Number:CaltechETD:etd-06202007-093347
Persistent URL:http://resolver.caltech.edu/CaltechETD:etd-06202007-093347
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:2659
Collection:CaltechTHESIS
Deposited By: Imported from ETD-db
Deposited On:13 Jul 2007
Last Modified:26 Dec 2012 02:53

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