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Interactive Heuristic Edge DetectionDouglas A. Lyon

Computer Engineering DepartmentFairfield University

Lyon@DocJava.com, http://www.DocJava.com

Copyright 2002 © DocJava, Inc.

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Background

• Good Edge Detection is hard.

• We know a good edge when we see it!

• How do we know?

• Lets assume that we do!

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The Problem

• Given an image and points on good edges.

• Find a way to connect the points on the edges.

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Motivation

• Experts can find good edges

• Most edge detectors are not as good

• knowledge is hard to encode.

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Application

• Photo interpretation

• Path planning (source+destination)

• Medical imaging

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Approach

• Pixels=graph nodes

• mark an important edge

• Search in pixel space using a heuristic

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Assumptions

• A good edge is more important than speed

• Heuristics are available

• User is a domain expert

• Knowledge rep=heuristics+markers

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Photo Interpretation

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Echocardiogram

•3D-multi-scale analysis

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Path Plans, the idea

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Path Planning-pre proc.•UNAHE+thresh

•Dil+Skel

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Path Planning - Search

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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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Future work

• Teach the edge detector– Training sets?

– Neural Nets?

– Better justification for heuristics…

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Open Questions

• Does the mind use open-loop filtering?

• Does the mind select from filter banks?

• Does the mind tune the filters?

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Gabor filter w/threshold

• The Strong edge is the wrong edge!

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Sub bands for 3x3 Robinson

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Sub Bands 7x7 Gabor

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Gabor-biologically motivated

Δθ =150

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