Color Image Segmentation Using Chroma Space Based On Bolck Region Growing

Image segmentation is the basic and important technology in Image Engineering. It is a first task of any image analysis process because all the subsequent tasks (feature extraction and object recognition etc.) rely heavily on the quality of the segmentation. In recent years with the development of technologies, such as machine vision, pattern recognition and content-based image retrieve etc, and the wide use of color images, image segmentation, especially color image segmentation, has played a more and more important role.Many existing methods for image segmentation have been studied and compared in this thesis, and then an improved region growing algorithm is proposed-color image segmentation using chroma space based on block region growing. HSV color space that is consistent with human eyes is selected; achromatic information is used as the weigh on the hue value for strange pixels. Not only the pixel properties but also the pixel group properties are considered. Firstly an image is divided into most suitable blocks. For each block, the mean and variance values can be seen as the pixel group properties. The image segmentation is done with the approach of region growing based on block mean and variance. Considering the size of extracted objects and their relative position information, get rid of the "noise" objects and merge the meaningful fragmentary regions into their corresponding bigger ones. Finally all pixels in non-object blocks are classified into their corresponding adjacent objects.In order to analysis the performance of the whole algorithm, some pictures are tested and the segmentation results are analyzed. The experiments confirm thismethod is suitable for many kinds of images, its complexity of computation is less than traditional pixel-based methods, and also it can help do with the oversegmentation.