A neural mechanism for robust junction representation

in the visual cortex

University of UlmDept. of Neural Information Processing

Thorsten Hansen and Heiko Neumann

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

Role of junctions for object recognition(Biederman 1987)

Role of junctions for brightness perception (e.g., Adelson 2000):

Definition of corners and junctions

(from Adelson 2000)

Corners and junctions are points where two or more lines join or intersect

Junction detection in natural images

Junctions often cannot be detected locally(McDermott 2001):

13 pixel closeup 13 25 49 97 pixels

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:

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)

Model architecture

Recurrent interaction

modulating feedback

inhibition in both spatial andorientational domain

divisive inhibition

excitatory long-rangeinteraction

Properties of the proposed model

input stimulus complex cells long-range

background: noise suppression

edge: enhancement of coherent structures

corner: preservation of circular variance

high significance

Read-out of distributed information

low significance

orientation significance

circular variance

Batschelet 1981: Circular Statistics

Length of the resulting orientation vector

Corner and junction detection

Corner candidates: high circular variance and high overall activity:

Corner points: sufficiently large local maxima of corner candidates

Simulation I: distance from true location

V1 long-range model

feedforward complex cells

Simulation II: feedforward vs. feedback

complex cells long-range

Real world camera image

Simulation III: feedforward vs. feedback

complex cells

long-range

Attneave‘s cat

Simulation IV: ROC analysis

Comparison of the new scheme to standard methods based on Gaussian curvature and the structure tensor (black)

input image

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