In view of the segmentation problem caused by the traditional watershed algorithm

a new improved watershed algorithm for color image segmentation based on threshold mark is proposed. The method applied watershed algorithm directly to the original gradient image but not the simplified image

the purpose of which is to insure the loss edge information can be avoided. With different size structure element for color morphological gradient image

solved the contradiction between the protection of the edge and the image is simplified. At the same time

the new algorithm designed a method of threshold automaticly selection and marker extraction-extracting local minimum value related to the objects from the low-frequency components of the gradient

with these minimal values constituting the binary image force calibration of the original gradient image

then used watershed segmentation on the modified gradient. In the simulation experiment

the algorithm in this paper is compared with similar segmentation method.Algorithm obtained accurate and continuous closed boundary for the different RGB color images segmented

which got the minimum segmentation number that conforming to human vision

and improved the working efficiency. The method can adaptively extract marker without the need for prior knowledge

effectively solved the over segmentation problem of watershed

compared with the traditional algorithm improved the segmentation performance and having good applicability and robustness. It can be applied to the machine vision

biomedicine and hyperspectral remote sensing image segmentation.