Interactive Heuristic Edge
Detection
Douglas A. Lyon, Ph.D.
Chair, Computer Engineering Dept. Fairfield
University, CT, USA
[email protected], http://www.DocJava.com
Copyright 2002 © DocJava, Inc.
Background
• It is easy to find a bad edge!
• We know a good edge when we see it!
The Problem
• Given an expert + an image.
• The expert places markers on a good edge.
• Find a way to connect the markers.
Motivation
• Experts find/define good edges
• Knowledge is hard to encode.
Approach
•
•
•
•
Mark an important edge
Pixels=graph nodes
Search in pixel space using a heuristic
A* is faster than DP
Assumptions
• User is a domain expert
• Knowledge rep=heuristics+markers
Applications
• Photo interpretation
• Path planning (source+destination)
• Medical imaging
Photo Interpretation
Echocardiogram
•3D-multi-scale analysis
Path Plans, the idea
Path Planning-pre proc.
•hist+thresh
•Dil+Skel
Path Planning - Search
The Idea
• The mind selects from filter banks
• The mind tunes the filters
Gabor filter w/threshold
• The Strong edge is the wrong edge!
Sub bands for 3x3 Robinson
Sub Bands 7x7 Gabor
Gabor-biologically motivated
Log filters=prefilter+laplacian

2
x2  y2
1
2
2
e
2
2
1  x  y

1
4 
 
2 2
2
2
 
e


 f  f
 f (x, y)  2  2
x
y
2
2
2
x2 y2
2
2
Mexican Hat (LoG Kernel)
The Log kernel
Oriented Filters are steerable
Changing Scale at 0 Degrees
Changing Phase at 0 degrees
Summary
• Heuristics+markers= knowledge
• Low-level image processing still needed
• Global optimization is not appropriate for
all applications
• Sometimes we only want a single, good
edge
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