关键词: H-minima技术, 分水岭算法, 颜色直方图, 局部二值模式(LBP)纹理, Bhattacharyya系数, 区域合并

Abstract: In order to improve the segmentation effect of oil wear debris image and optimize the main content of automatic recognition of wear debris, an adaptive segmentation method of oil wear debris image which combines watershed algorithm and regional similarity has been proposed. First, the gradient image was modified by morphological reconstruction and H-minima technology, and the watershed algorithm was then used to segment the image. Second, after watershed, the Lab color feature and local binary patterns (LBP) texture feature of the homogenous region were extracted as their quantitative indicators, and the color similarity and texture similarity between the regions were calculated based on the Bhattacharyya coefficients. In order to merge the over-segmentation region with much accuracy, an efficient feature fusion rule was designed considering the dynamic weight of color and texture factors. Finally, some post-processing methods were taken to complete the segmentation. Sixty images were selected to test the segmentation effect of the proposed method. The results indicate that the average segmentation speed of single image is about 12 seconds, and the segmentation accuracy is more than 90%. This method avoids the interactive processing when segmenting wear debris images, well balances the segmentation efficiency and segmentation accuracy, and significantly improves the adaptation degree of segmentation program.

Key words: H-minima technology, watershed algorithm, color histogram, local binary patterns (LBP) texture, Bhattacharyya coefficient, region merging