Watershed Color Image Segmentation Algorithm Research Based On Color Space Conversion

Image segmentation technology is widely used in the field of image segmentation,according to the image segmentation results can be analyzed and processed,so it is very important to image segmentation in Digital image processing.Compared with other segmentation algorithms,the watershed algorithm is widely concerned because of its fast calculation and continuous contour.However,because of the over-segmentation of watershed algorithm,it will have a certain effect on the segmentation effect.In order to improve this phenomenon,the watershed algorithm is further analyzed and studied,and the concept of color space conversion and maximum variance between clusters is introduced,and a watershed color image segmentation algorithm based on color space conversion is proposed.By changing the color space of RGB color image and avoiding the unfavorable factors of specularities,the maximum variance between clusters algorithm is used to obtain the threshold automatically when the mark is extracted.It is proved by experiments that the algorithm in this paper can restrain the insignificant area caused by specularities in comparison with RGB space,and effectively prevent the appearance of over-segmentation,and do not need artificially set threshold,and obtain accurate and continuous target contour,and improve the robustness and applicability.