Computation in the higher visual cortices: map-seeking circuit theory and application to machine vision

  title={Computation in the higher visual cortices: map-seeking circuit theory and application to machine vision},
  author={David Arathorn},
  journal={33rd Applied Imagery Pattern Recognition Workshop (AIPR'04)},
  • D. Arathorn
  • Published 13 October 2004
  • Computer Science
  • 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04)
Map-seeking circuit theory is a biologically based computational theory of vision applicable to difficult machine vision problems such as recognition of 3D objects in arbitrary poses amid distractors and clutter, as well as to non-recognition problems such as terrain interpretation. It provides a general computational mechanism for tractable discovery of correspondences in massive transformation spaces by exploiting an ordering property of superpositions. The latter allows a set of… 
Analysis of Constrained Optimization Variants of the Map-Seeking Circuit Algorithm
Several numerical schemes to compute local solutions are presented and compared on a pair of test problems: an image matching problem and the challenging problem of automatically solving a Rubik’s cube.
Convergence of Map Seeking Circuits
This algorithm is formulated as discrete dynamical system on a set Δ=Πℓ=1LΔ(�), where each Δ(�¬) is a compact subset of a nonnegative orthant of ℝn, and it is shown that for an open and dense set of initial conditions in Δ the corresponding solutions converge to either a vector with unique nonzero element in each Δ (™) or to a zero vector.
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Terrain discovery and navigation of a multi-articulated linear robot using map-seeking circuits
A significant challenge in robotics is providing a robot with the ability to sense its environment and then autonomously move while accommodating obstacles. The DARPA Grand Challenge, one of the most
Questions Unanswered , and Questions Not Yet
J.M. Bower (ed.), 20 Years of Computational Neuroscience, Springer Series in Computational Neuroscience 9, DOI 10.1007/978-1-4614-1424-7_12, © Springer Science+Business Media New York 2013 Abstract I
20 Years of Learning About Vision: Questions Answered, Questions Unanswered, and Questions Not Yet Asked
I have been asked to review the progress that computational neuroscience has made over the past 20 years in understanding how vision works. In reflecting on this question, I come to the conclusion
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Under these conditions, simulated annealing is shown to improve the performance of the MSC through a series of experiments involving the Hausdorff distance metric.
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Map-Seeking Circuits in Visual Cognition: A Computational Mechanism for Biological and Machine Vision
The author argues that map-seeking is a fundamental, broadly applicable computational operation with algorithmic, neuronal, and analog electronic implementations, and that its generality makes it suitable as the core of a computational explanation for several cognitive functions.
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It is shown that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex.
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It is found that monosynaptic horizontal connections within area V1 are of an appropriate spatial scale to mediate interactions within the SF of V1 neurons and to underlie contrast-dependent changes in SF size, which could represent an anatomical substrate for contextual modulation and global-to-local integration of visual signals.
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A mathematical property of pattern superpositions permits the construction of mechanisms capable of recognising learned patterns occurring under visual transformation within a visually complex field.
Computational subunits in thin dendrites of pyramidal cells
This work combined confocal imaging and dual-site focal synaptic stimulation of identified thin dendrites in rat neocortical pyramidal neurons found that nearby inputs on the same branch summed sigmoidally, whereas widely separated inputs or inputs to different branches summed linearly.
A Solution to the Generalized Correspondence Problem Using an Ordering Property of Superpositions
  • 2004
Map - Seeking : Recognition Under Transformation Using A Superposition Ordering Property
  • Electronics Letters
  • 2001
Map-Seeking: Recognition Under Transformation Using A Superposition Ordering Property
  • Electronics Letters
  • 2001
A Solution to the Generalized Correspondence Problem Using an Ordering Property of Superpositions, submitted 2004. 3D model of T-80 and WW-2 tank courtesy 3DCafe
  • com Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04)