Interactive Heuristic Edge Detection

Douglas A. Lyon, Ph.D.Chair, Computer Engineering Dept. Fairfield

University, CT, USA

Lyon@DocJava.com, 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 1

2 2 ex 2 y 2

2 2

1

4 1 x2 y2

2 2

ex 2 y 2

2 2

2 f (x, y) 2 f

x2 2 f

y2

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

http://www.DocJava.com