Detecting Scale-Invariant Regions Using Evolved Image Operators

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Trujillo:2009:EIASP,

• author = "Leonardo Trujillo and Gustavo Olague",
• title = "Detecting Scale-Invariant Regions Using Evolved Image Operators",
• booktitle = "Evolutionary Image Analysis and Signal Processing",
• publisher = "Springer",
• year = "2009",
• editor = "Stefano Cagnoni",
• volume = "213",
• series = "Studies in Computational Intelligence",
• pages = "21--40",
• address = "Berlin / Heidelberg",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-01635-6",
• ISSN = "1860-949X",
• DOI = "doi:10.1007/978-3-642-01636-3_2",
• abstract = "This chapter describes scale-invariant region detectors that are based on image operators synthesised through Genetic Programming (GP). Interesting or salient regions on an image are of considerable usefulness within a broad range of vision problems, including, but not limited to, stereo vision, object detection and recognition, image registration and content-based image retrieval. A GP-based framework is described where candidate image operators are synthesized by employing a fitness measure that promotes the detection of stable and dispersed image features, both of which are highly desirable properties. After a significant number of experimental runs, a plateau of maxima was identified within the search space that contained operators that are similar, in structure and/or functionality, to basic LoG or DoG filters. Two such operators with the simplest structure were selected and embedded within a linear scale space, thereby making scale-invariant feature detection a straightforward task. The proposed scale-invariant detectors exhibit a high performance on standard tests when compared with state-of-the-art techniques. The experimental results exhibit the ability of GP to construct highly reusable code for a well known and hard task when an appropriate optimisation problem is framed.",
• notes = "EvoISAP, EvoNET, EvoStar",

}

Genetic Programming entries for Leonardo Trujillo Gustavo Olague

Citations