融合局部纹理特征的颗粒图像SLIC超像素分割方法

摘要

将 SLIC超像素分割的方法引入颗粒图像检测的分割过程中，将颗粒图像分割成感兴趣的超像素块，可降低后续图像处理过程的复杂度。由于 SLIC超像素分割在聚类过程中计算相似度时没有考虑图像的纹理特征，一定程度上会影响颗粒目标外轮廓分割的细节。利用CRLBP局部纹理算子纹理特征，改进 SLIC分割中聚类相似度的计算，并按照符合颗粒形状的圆形邻域搜索相似点，保证了分割速度。通过对棉种颗粒图像的分割试验，与传统分水岭算法和 SLIC 超像素算法进行比较，结果表明改进的 SLIC 超像素分割方法能更有效地分割出颗粒目标。%This paper adopts SLIC-based superpixel segmentation method in the granular image detection.SLIC method segments the granular image into superpixel block which will reduce the complexity of the subsequent image processing.As SLIC superpixel segmentation method doesn’t use the texture feature in the distance calculation,the detail of the outline for the granular object is lost. This paper adopts the CRLBP local texture operator as the texture feature to improve the SLIC segmentation’s distance calculation and searches the similar pixel in circle neighborhood pixels to guarantee the processing speed.The test on cotton seed image shows that the improved SLIC superpixel segmentation method is more efficient than watershed and original SLIC method.