A neural mechanism for robust junction representation in the visual cortex University of Ulm Dept. of Neural Information Processing Thorsten Hansen and.

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Presentation transcript:

A neural mechanism for robust junction representation in the visual cortex University of Ulm Dept. 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 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 interactions in 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 and orientational domain divisive inhibition excitatory long-range interaction

Properties of the proposed model input stimuluscomplex cellslong-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 cellslong-range Real world camera image

Simulation III: feedforward vs. feedback complex cellslong-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