期刊名称:TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN:2302-9293
出版年度:2019
卷号:17
期号:3
页码:1461-1467
DOI:10.12928/telkomnika.v17i3.10072
出版社:Universitas Ahmad Dahlan
摘要:The color image segmentation is one of most crucial application in image processing. It can
apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection
on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc.
The color image segmentation has faced the problem of multidimensionality. The color image is
considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry
(luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to
reduce the one dimensional and geometrically it converts in the array hence the further one dimension has
been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall
distance of the cluster, which is randomly placed at the start of the segmentation process. The
segmentation results of this method give clear segments based on the different color and it can be applied
to any application.
其他摘要:The color image segmentation is one of most crucial application in image processing. It can apply to medical image segmentation for a brain tumor and skin cancer detection or color object detection on CCTV traffic video image segmentation and also for face recognition, fingerprint recognition etc. The color image segmentation has faced the problem of multidimensionality. The color image is considered in five-dimensional problems, three dimensions in color (RGB) and two dimensions in geometry (luminosity layer and chromaticity layer). In this paper the, L*a*b color space conversion has been used to reduce the one dimensional and geometrically it converts in the array hence the further one dimension has been reduced. The a*b space is clustered using genetic algorithm process, which minimizes the overall distance of the cluster, which is randomly placed at the start of the segmentation process. The segmentation results of this method give clear segments based on the different color and it can be applied to any application.