Figure of merit based fitness functions in genetic programming for edge detection

Authors:
Wenlong Fu;Mark Johnston;Mengjie Zhang
Affiliations:
School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Year:
2012

Citing 15
Cited 0

A Computational Approach to Edge Detection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: PIKS Inside

Digital Image Processing: PIKS Inside
Comparing Images Using the Hausdorff Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection Filters Based on Artificial Neural Networks

ICIAP '95 Proceedings of the 8th International Conference on Image Analysis and Processing
A GP Artificial Ant for Image Processing: Preliminary Experiments with EASEA

EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolution of a local boundary detector for natural images via genetic programming and texture cues

Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolving edge detectors with genetic programming

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Genetic programming for image analysis

GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
A new methodology for evaluation of edge detectors

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A novel genetic programming based morphological image analysis algorithm

Proceedings of the 12th annual conference on Genetic and evolutionary computation
Review article: Edge and line oriented contour detection: State of the art

Image and Vision Computing
Genetic programming for edge detection based on accuracy of each training image

AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Edge detection in untextured and textured images-a common computational framework

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The figure of merit (FOM) is popular for testing an edge detector's performance, but there are very few reports using FOM as an evaluation method in Genetic Programming (GP). In this study, FOM is investigated as a fitness function in GP for edge detection. Since FOM has some drawbacks from type II errors, new fitness functions are developed based on FOM in order to address these weaknesses. Experimental results show that FOM can be used to evolve GP edge detectors that perform better than the Sobel detector, and the new fitness functions clearly improve the ability of GP edge detectors to find edge points and give a single response on edges, compared with the fitness function using FOM.