一种改进的Canny图像分割算法

An Improved Canny Image Segmentation Algorithm

针对目前图像分割算法普遍存在噪音鲁棒性差、易发生细小边界信息缺失以及适用范围较窄的缺点,改进Canny边缘提取算法中的问题阈值并与原色特征提取加权融合。首先针对Canny算子阈值的自适应性问题,通过计算图像背景与目标之间的类方差来减少错分概率来决定阈值。然后,在具有丰富信息的彩色图像上提出R、G、B这3种原色特征,通过原色特征提取的分割图像与阈值分割提取的图像加权融合形成全新的分割图像。该算法不仅克服了传统分割提取算法边缘信息丢失、鲁棒性差的问题,而且提高了细节点的单位精度,实验结果表明了本文改进Canny边缘算法的有效性。

Aiming at the shortcomings of image segmentation algorithm,such as poor noise robustness,small boundary information easily loss and narrow application scopes,we improve the threshold value of Canny edge extraction algorithm and extract weighted fusion with chromatic feature. According to the adaptive problem of the Canny operator threshold,the threshold is determined by calculating the class variance between the image background and the target to reduce the error probability. Then,we propose three color features of R,G and B on colorful images with rich information. The new segmentation images are fused by thresholding and segmented images extracted by chromaticity feature extraction. The algorithm not only overcomes the problem of edge information loss and poor robustness of the traditional segmentation and extraction algorithm,but also improves the unit accuracy of the detail. The experimental results show the effectiveness of the improved Canny edge algorithm.