Reconstruction for Artificial Degraded Image Using Constructive Solid Geometry and Strongly Typed Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Yamagiwa:2009:CISIS,

• author = "Motoi Yamagiwa and Eiji Kikuchi and Minoru Uehara and Makoto Murakami and Masahide Yoneyama",
• title = "Reconstruction for Artificial Degraded Image Using Constructive Solid Geometry and Strongly Typed Genetic Programming",
• booktitle = "International Conference on Complex, Intelligent and Software Intensive Systems, CISIS '09",
• year = "2009",
• month = mar,
• pages = "162--168",
• keywords = "genetic algorithms, genetic programming, 2-dimensional sinc filter, acoustic imaging, acoustic impedance, artificial degraded image reconstruction, constructive solid geometry, neural network, acoustic imaging, image reconstruction, neural nets",
• DOI = "doi:10.1109/CISIS.2009.164",
• abstract = "Acoustic imaging is effective in extreme environments to take images without being influenced by optical properties. However, such images tend to deteriorate rapidly because acoustic impedance in air is low. It is thus necessary to restore the image of the object from a deteriorated image so that the object can be recognized in a search. We used a neural network in the previous work as a post processor and tried to reconstruct the original object image. However, this method needs to learn the original object image. In this work, we propose combining constructive solid geometry (CSG) with genetic programming (GP) as a new technique that does not require learning. To confirm the effectiveness of this technique, we reconstruct the image of an object from a deteriorated image created by applying a 2-dimensional sinc filter to the original image.",
• notes = "Also known as \cite{5066783}",

}

Genetic Programming entries for Motoi Yamagiwa Eiji Kikuchi Minoru Uehara Makoto Murakami Masahide Yoneyama

Citations