Lab分通道直方图的彩色图像分割算法及应用.

于艺铭; 金典; 王琪; 张琪; 陈茜; 王小菊

In view of the color and texture features of the image are not taken into account in the traditional gray scale histogram segmentation algorithm, this paper proposed a new color image segmentation algorithm based on Lab sub channel histogram to solve the problems such as over segmentation or under segmentation for no obvious gray value difference images or grayscale overlapped images. The algorithm introduces three segmentation bases with sequence irrelevance:luminance "L" channel, red-green "a" channel and blue-yellow "b" channel, and then use the Newton interpolation method to fit operation. Users can choose the channel freely for different brightness and chromaticity attribute images. The paper also uses the matching principle of adjacent area gray value to solve the exact matching problem of pixels in the adjacent target area. The algorithm realizes the extraction of different objects in the image by sub office, sub morphology and subarea. In the light of images with significant difference in regional luminance characteristics or regional color difference greater than regional brightness difference, the segmentation results of the algorithm in this paper are better than the traditional gray scale histogram segmentation algorithm through a series of experimental verification, it greatly improves the applicability of histogram segmentation algorithm. Then we combine the new algorithm with the classic Reinhard color transfer algorithm, use the sub channel segmentation algorithm to obtain the target regions of interest from the source image and then transfer them separately. It solves problems of the interference to non-target area of image, color miscommunication and serious level loss of classical Reinhard color transfer algorithm. It breaks through the limitation that the traditional transfer algorithm can only coloring the whole region, and realizes accurate coloring in the subarea.

NEWTON-Raphson method; IMAGE segmentation; REGIONAL differences; LUMINANCE (Photometry); CHROMATICITY; IMAGE processing

Imaging Science & Photochemistry, 2019, Vol 37, Issue 1, p18

1674-0475

Academic Journal

10.7517/issn.1674-0475.181004