Low-Level Feature Extraction for Edge Detection Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Fu:2014:ieeec,

• author = "Wenlong Fu and Mark Johnston and Mengjie Zhang",
• title = "Low-Level Feature Extraction for Edge Detection Using Genetic Programming",
• journal = "IEEE Transactions on Cybernetics",
• year = "2014",
• volume = "44",
• number = "8",
• month = "1459--1472",
• keywords = "genetic algorithms, genetic programming, Accuracy, Detectors, Educational institutions, Feature extraction, Image edge detection, Noise, Training, Edge detection, feature extraction",
• ISSN = "2168-2267",
• DOI = "doi:10.1109/TCYB.2013.2286611",
• size = "14 pages",
• abstract = "Edge detection is a subjective task. Traditionally, a moving window approach is used, but the window size in edge detection is a tradeoff between localisation accuracy and noise rejection. An automatic technique for searching a discriminated pixel's neighbours to construct new edge detectors is appealing to satisfy different tasks. In this paper, we propose a genetic programming (GP) system to automatically search pixels (a discriminated pixel and its neighbours) to construct new low-level subjective edge detectors for detecting edges in natural images, and analyse the pixels selected by the GP edge detectors. Automatically searching pixels avoids the problem of blurring edges from a large window and noise influence from a small window. Linear and second-order filters are constructed from the pixels with high occurrences in these GP edge detectors. The experiment results show that the proposed GP system has good performance. A comparison between the filters with the pixels selected by GP and all pixels in a fixed window indicates that the set of pixels selected by GP is compact but sufficiently rich to construct good edge detectors.",
• notes = "also known as \cite{6649981}",

}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations