DocumentCode :
256326
Title :
Visualization enhancement of segmented images using genetic algorithm
Author :
Radlak, Krystian ; Smolka, Bogdan
Author_Institution :
Inst. of Autom. Control, Silesian Univ. of Technol., Gliwice, Poland
fYear :
2014
fDate :
14-16 April 2014
Firstpage :
391
Lastpage :
396
Abstract :
The aim of the paper is to present the results of investigations concerning the implementation of pseudocolor visualization algorithm of segmented images, capable to find the color combination producing maximum contrast between the segmented areas. Very often there is a need of visualization of segmentation results and usually they are presented by assigning colors randomly or from predefined palettes, what could decrease the visualization effect, when neighboring regions have assigned similar colors. To alleviate this problem, we propose novel methodology for deriving optimized visualization based on maximizing local distance between colors. In the paper we present visualization results using a new color contrast measure optimized with a genetic algorithm and compare the effectiveness with a greedy algorithm. The proposed method can be used to obtain visually pleasing pseudocolor encoded images of segmentation results which can be useful for the presentation of various kinds of visual information.
Keywords :
data visualisation; genetic algorithms; image colour analysis; image enhancement; image segmentation; color combination; color contrast measure; genetic algorithm; greedy algorithm; image segmentation; predefined palettes; pseudocolor visualization algorithm; visual information; visualization effect; visualization enhancement; visually pleasing pseudocolor encoded images; Genetic algorithms; Greedy algorithms; Image color analysis; Image segmentation; Sociology; Statistics; Visualization; colorization; genetic algorithm; segmentation; visualization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia Computing and Systems (ICMCS), 2014 International Conference on
Conference_Location :
Marrakech
Print_ISBN :
978-1-4799-3823-0
Type :
conf
DOI :
10.1109/ICMCS.2014.6911269
Filename :
6911269
Link To Document :
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