1. Giessen University Dept. of Psychology
Robust contour extraction and junction detection by a neural model utilizing recurrent long-range interactions
Thorsten Hansen and Heiko Neumann
Giessen University
Dept. of Psychology
Ulm University
Dept. of Neural Information Processing

2. Overview of the Talk
Motivation: empirical evidence for recurrent long-range interactions
2. Approach and Model
3. Results: Contour enhancement
Corner detection
4. Conclusions

3. Sketch of V1 Architecture
long-range connections
McGuire et al. 1991
LGN
recurrent intercolumnar
interactions

4. Specificity of Horizontal Long-Range Connections in V1
Bosking et al. 1997
”like connects to like” plus colinear arrangement
Long-range connections link neurons with same
orientation preference and collinear aligned RFs.

5. Functional Implications of Lateral Long-Range Interactions
Polat & Sagi (1993)
Measurement of contrast detection thresholds
for foveal Gabor elements with and without flankers.
Colinear flanking Gabors (up to a distance of 10 wavelengths)
facilitate contrast detection.

6. Key Mechanisms of the Proposed Model
Excitatory long-range interactions between cells with collinear aligned RF (Bosking et al. 1997)
Inhibitory short-range interactions
Modulating feedback: Initial bottom-up activity
is necessary (Hirsch & Gilbert 1991)

7. Model architecture

8. Recurrent Interaction
modulating feedback
divisive inhibition
inhibition in both spatial
and orientational domain
excitatory long-range
interaction

9. Results: Contour Enhancement
input image complex cells long-range
Activity that fits into a more global context
is enhanced by top-down feedback.

10. Results: Temporal Evolution
input image complex cells
long-range t= t = t=12

11. Quantitative Evaluation: Contur Saliency
High saliency: large values of (r,z)
discrete time steps
saliency measures z r
input image

12. Results: Natural Images
input image complex cells long-range

13. Simulation: Physiological Data
Kapadia et al. 1995
Simulation: Physiological Data
response relative to single bar
bar +flankers +texture +flankers+texture
enhancement for collinear bar; suppression for noisy textures

14. Properties of the Proposed Model
input image
complex cells
long-range
background: noise suppression
corner: preservation of multiple orientations
edge: enhancement of coherent structures

15. Definition of Corners and Junctions
Corners and junctions are points
where two or more lines join or intersect
(from Adelson 2000)

16. Junctions for Object Recognition (Biederman 1987)

17. Junctions and Brightness Perception
Adelson (2000)

18. Junction Detection in Natural Images
Junctions often cannot be detected locally
(McDermott 2001):
13 pixel closeup
pixels

19. Neural Representation of Junctions
distributed activity for multiple orientations
within a cortical hypercolumn
1. Robust generation of coherent contours
model of recurrent long-range interactions
in V1
Approach:
2. Read-out of distributed information
measure of circular variance

20. Read-out of Distributed Information
Orientation significance: Length of the resulting orientation vector in relation to the overall activity
high significance
low significance
orientation significance
circular variance
Batschelet 1981:
Circular Statistics

21. Corner and Junction Detection
Corner candidates:
high circular variance and high overall activity:
Corner points:
sufficiently large local maxima of corner candidates

22. Results: Localization Accuracy
deviation from true location
V1 long-range model
feedforward complex cells
generic junction configurations

23. Junction Detection on a Synthetic Image
Attneave‘s cat
complex cells
long-range

24. Junction Detection on Natural Images
Real world camera image
complex cells
long-range

25. Junction Detection on Natural Images
cut-out of a plant image
Van Hateren & van der Schaaf 1998
input image
complex cells
long-range

26. Evaluation using ROC Analysis
Comparison of the new scheme to standard methods based on Gaussian curvature and
the structure tensor (black)
input image

27. 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
Hansen & Neumann (2004)
Neural Computation 16(5).