基于Mean-shift和提升小波变换的棉花叶片边缘的图像检测

该文提出了一种基于Mean-shift和提升小波变换的具有复杂背景的棉花叶片边缘检测算法。该方法首先用Mean-shift算法对彩色图像进行平滑，然后对平滑后的图像进行提升小波变换，以将平滑后的图像进行灰度增强。最后基于Canny算子对图像进行边缘检测。该算法能有效减少非边缘噪声，并且能够有效提取相互重叠叶片的边缘。与传统边缘检测方法边缘检测结果进行对比，该方法能够更加鲁棒地提取复杂背景下的重叠叶片边缘，其有效性和准确性是很明显的。

Cotton leaf image edge detection using Mean-shift algorithm and lifting wavelet transform

Based on Mean-shift algorithm and lifting wavelet transform, a novel edge detection algorithm was proposed in this paper, in order to detect the edge of cotton leaf in an image with the presence of clutter and occlusion. Firstly, the color image was smoothed using Mean-shift algorithm. Then the lifting wavelet transform was used to enhance the edge of the smoothed image. Based on Canny operator, the edge of the cotton leaf was detected. The method can greatly reduce non-edge noises, and is able to effectively extract the edges between the overlapping leaves. Comparing with the experimental results of traditional edge detection methods, this approach can robustly detect the edge of the cotton leaf among clutter and occlusion while achieving obvious validity and accuracy.