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
Overview of the Talk
1. Motivation: empirical evidence for recurrent
long-range interactions
2. Approach and Model
3. Results: Contour enhancement
Corner detection
4. Conclusions
Sketch of V1 Architecture
long-range connections
McGuire et al. 1991
LGN
recurrent intercolumnar
interactions
Specificity of Horizontal Long-Range
Bosking et al. 1997
Connections in V1
”like connects to like”
plus
colinear arrangement
Long-range connections link neurons with same
orientation preference and collinear aligned RFs.
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.
Key Mechanisms of the Proposed Model
1. Excitatory long-range interactions between cells
with collinear 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
Results: Contour Enhancement
input image
complex cells
long-range
Activity that fits into a more global context
is enhanced by top-down feedback.
Results: Temporal Evolution
input image
complex cells
long-range t=1
t =2
t=12
Results: Natural Images
input image
complex cells
long-range
response relative to single bar
Simulation: Physiological Data
bar
+flankers +texture
Kapadia et
al. 1995
+flankers+texture
enhancement for collinear bar; suppression for noisy textures
Properties of the Proposed Model
input image
complex cells
long-range
edge: enhancement of coherent structures
background: noise suppression
corner: preservation of multiple orientations
Definition of Corners and Junctions
Corners and junctions are points
where two or more lines join or intersect
(from Adelson 2000)
Junctions for Object Recognition
(Biederman 1987)
Junction Detection in Natural Images
Junctions often cannot be detected locally
(McDermott 2001):
13 pixel closeup
13
25
49
97 pixels
Neural Representation of Junctions
distributed activity for multiple orientations
within a cortical hypercolumn
Approach:
1. Robust generation of coherent contours
model of recurrent long-range interactions
in V1
2. Read-out of distributed information
measure of circular variance
Corner and Junction Detection
Corner candidates:
high circular variance and high overall activity:
Corner points:
sufficiently large local maxima of corner candidates
deviation from true location
Results: Localization Accuracy
V1 long-range model
feedforward complex cells
generic junction configurations
Junction Detection on a Synthetic Image
Attneave‘s cat
complex cells
long-range
Junction Detection on Natural Images
Real world camera image
complex cells
long-range
Junction Detection on Natural Images
cut-out of a plant image
Van Hateren & van der Schaaf 1998
input image
complex cells
long-range
Evaluation using 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
Hansen & Neumann (2004)
Neural Computation 16(5).