Color image segmentation is an important image analysis technology

that has important applications in image recognition systems. The quality of image segmentation directly affects image processing. However

color images in real life are difficult to segment precisely because of noise

uneven color

and weak boundaries. This study proposed a watershed segmentation algorithm based on homomorphic filtering and morphological hierarchical reconstructions. By combining the advantages of homomorphic filtering

morphological operations

and watershed transform

the qualities of color image segmentations are improved. The watershed transform algorithm is widely used in image segmentation

because of its low computational burden

high accuracy

and continuous extraction. However

due to irregular regions and noises in an image

image segmentation relying solely on a watershed transform algorithm easily results in a large number of false contours. To improve the quality of image segmentation by watershed transform

this study adopted homomorphic filtering and morphological reconstruction. First

the proposed algorithm used the Sobel edge operator to compute the gradient of each color component according to the image's R

G

and B values. The maximum value was selected as the gradient of the color image. After extracting the gradient map of a color image

it was modified through homomorphic filtering using Fourier transform. Filtering highlights the foreground contour information and removes the detail texture noise. Irregular details and a small amount of noise still existed in the gradient image

especially in the boundary and background

after filtering

but the morphological reconstruction operators addressed this shortcoming. A modified gradient map was then reconstructed hierarchically by using the operators of open and close morphological reconstructions. According to the cumulative distribution function of the gradient map and the distribution information of the gradient histogram after filtering

the formula to calculate the number of gradient layers was provided

and the sizes of morphological structure elements

which decreased with the increase in the gradient value in each layer

were calculated adaptively. Finally

standard watershed transform was applied to the reconstructed gradient map

and image segmentation was realized. To verify the effectiveness of the algorithm

an experiment was conducted. Four color images of different features were utilized for segmentation in the experiment. The proposed algorithm effectively restrained over segmentation and maintained the weak boundary

so the segmentations were more accurate than those of other watershed algorithms. To objectively evaluate the performance of the different segmentation methods

the experimental results were quantified through an unsupervised evaluation of segmentations. The synthesize index combined with regional consistency and diversity indexes was applied. The evaluation index values of our algorithm in the four test images were 0.633 3

0.665 6

0.629 3

and 0.648 4

which were higher than the results of other watershed algorithms; the segmentation performance of our algorithm was also better. With regard to timeliness

our algorithm requires more time than the other two algorithms

but the difference was small. Watershed transform is a widely used algorithm for image segmentation

but it often leads to over segmentation. Many methods focus on solving this problem while ignoring the weak boundaries of images

which are also important in segmentation. This study proposed a new improved watershed algorithm for color images. In the algorithm

homomorphic filtering is used to preserve the weak boundary of an image

and adaptive morphological reconstruction is applied to suppress the over segmentation of watershed transform. Balance exists between under and over segmentations. The segmentation results of the proposed algorithm are closer to the human perception of mages. Whether in terms of the evaluation index or segmentation performance

the proposed algorithm performs well. The algorithm is insensitive to noise

possesses good robustness

and can be widely application in computer vision

traffic control

biomedicine

and other targeted segmentations.