Title :
Genetic algorithm with competitive image labelling and least square
Author :
Yuen, Shiu Yin ; Ma, Chi Ho
Author_Institution :
Dept. of Electron. Eng., Hong Kong City Univ., Kowloon Tong, Hong Kong
Abstract :
A multi-modal genetic algorithm using a dynamic population concept is introduced. Each image point is assigned a label and for a chromosome to survive, it must have at least one image point with its label. In this way, the genetic algorithm dynamically segments the scene into one or more objects and the background noise. A repeated least square technique is applied to enhance the convergence performance. The integrated algorithm is tested using a 6 degrees-of-freedom template matching problem, and it is applied to a challenging image that contains multiple target objects as well as scene clutter due to unrelated objects
Keywords :
convergence of numerical methods; genetic algorithms; image enhancement; image matching; image segmentation; least squares approximations; object detection; background noise; chromosome; competitive image labelling; convergence performance enhancement; dynamic population concept; dynamic segmentation; image point; integrated algorithm; multi-modal genetic algorithm; multiple target objects; repeated least square technique; scene clutter; template matching; Electrical capacitance tomography; Genetic algorithms; Genetic engineering; Labeling; Layout; Least squares methods; Stochastic processes; Tellurium; Testing; Transforms;
Conference_Titel :
Image Analysis and Processing, 1999. Proceedings. International Conference on
Conference_Location :
Venice
Print_ISBN :
0-7695-0040-4
DOI :
10.1109/ICIAP.1999.797622
