Interactive Heuristic Edge Detection Douglas A. Lyon, Ph.D. Chair, Computer Engineering Dept. Fairfield University, CT, USA 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
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