Title :
Reconstruction for Artificial Degraded Image Using Constructive Solid Geometry and Strongly Typed Genetic Programming
Author :
Yamagiwa, Motoi ; Kikuchi, Eiji ; Uehara, Minoru ; Murakami, Makoto ; Yoneyama, Masahide
Author_Institution :
Dept. of Inf. & Comput. Sci., Toyo Univ., Toyo
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 postprocessor 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.
Keywords :
acoustic imaging; genetic algorithms; image reconstruction; neural nets; 2-dimensional sinc filter; acoustic imaging; acoustic impedance; artificial degraded image reconstruction; constructive solid geometry; genetic programming; neural network; Acoustic imaging; Degradation; Genetic programming; Geometrical optics; Geometry; Image reconstruction; Optical computing; Optical filters; Solids; Ultraviolet sources; Genetic Algorithms; Geometric modeling; Image reconstruction;
Conference_Titel :
Complex, Intelligent and Software Intensive Systems, 2009. CISIS '09. International Conference on
Conference_Location :
Fukuoka
Print_ISBN :
978-1-4244-3569-2
Electronic_ISBN :
978-0-7695-3575-3
DOI :
10.1109/CISIS.2009.164