a neural mechanism for robust junction representation in the visual cortex university of ulm dept....
Post on 22-Dec-2015
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![Page 1: A neural mechanism for robust junction representation in the visual cortex University of Ulm Dept. of Neural Information Processing Thorsten Hansen and](https://reader035.vdocuments.net/reader035/viewer/2022062421/56649d775503460f94a58820/html5/thumbnails/1.jpg)
A neural mechanism for robust junction representation
in the visual cortex
University of UlmDept. of Neural Information Processing
Thorsten Hansen and Heiko Neumann
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Overview of the talk
1. Motivation: Why junctions are important
2. Detection: The basic idea and its failure for natural images
3. Approach and Model
4. Simulations and ROC analysis
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Role of junctions for object recognition(Biederman 1987)
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Role of junctions for brightness perception (e.g., Adelson 2000):
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Definition of corners and junctions
(from Adelson 2000)
Corners and junctions are points where two or more lines join or intersect
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Junction detection in natural images
Junctions often cannot be detected locally(McDermott 2001):
13 pixel closeup 13 25 49 97 pixels
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Neural representation of corners and junctions
2. Read-out of distributed information
measure of circular variance
distributed activity for multiple orientations within a cortical hypercolumn
1. Robust generation of coherent contours
model of recurrent long-range interactionsin V1
Approach:
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Key mechanisms of the proposed V1 model
1. Excitatory long-range interactions between cells with colinear aligned RF (Bosking et al. 1997)
2. Inhibitory short-range interactions
3. Modulating feedback Initial bottom-up activity is necessary
(Hirsch & Gilbert 1991)
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Model architecture
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Recurrent interaction
modulating feedback
inhibition in both spatial andorientational domain
divisive inhibition
excitatory long-rangeinteraction
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Properties of the proposed model
input stimulus complex cells long-range
background: noise suppression
edge: enhancement of coherent structures
corner: preservation of circular variance
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high significance
Read-out of distributed information
low significance
orientation significance
circular variance
Batschelet 1981: Circular Statistics
Length of the resulting orientation vector
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Corner and junction detection
Corner candidates: high circular variance and high overall activity:
Corner points: sufficiently large local maxima of corner candidates
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Simulation I: distance from true location
V1 long-range model
feedforward complex cells
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Simulation II: feedforward vs. feedback
complex cells long-range
Real world camera image
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Simulation III: feedforward vs. feedback
complex cells
long-range
Attneave‘s cat
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Simulation IV: ROC analysis
Comparison of the new scheme to standard methods based on Gaussian curvature and the structure tensor (black)
input image
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Conclusions
• corners and junctions can be robustly represented by distributed activity within a cortical hypercolum
• recurrent colinear long-range interactions serve as a multi-purpose mechanism for
• contour enhancement• noise suppression• junction detection
• model performance superior to local junction detection schemes used in Computer Vision