基于同态滤波和K均值聚类算法的杨梅图像分割

Bayberry image segmentation based on homomorphic filtering and K-means clustering algorithm

针对自然环境下光照不均杨梅果实分割效果不理想问题展开研究。应用同态滤波算法对HSV(色调hue,饱和度saturation,亮度value)颜色空间下杨梅图像V分量进行亮度增强,以补偿光线。而后针对彩色杨梅图像的颜色特征,结合Lab(L(亮度Lightness),a(色度chromaticity,+a表示红色,-a表示绿色),b(色度chromaticity,+b表示黄色,-b表示蓝色)颜色空间a、和b分量的特点,应用K均值聚类算法在Lab颜色空间中对彩色杨梅图像进行分割。为了验证该算法的有效性,在100余幅图像中选用15幅因光照不均和生长状态不同而存在不同程度阴影影响的杨梅图像,进行了3组比较试验,先采用K均值聚类算法对光线补偿去除阴影前后的杨梅图像分割结果进行比较;接着,采用基于色差2*R-G-B自适应灰度阈值分割算法和K均值聚类算法2种不同分割算法对去除阴影后的杨梅图像分割结果进行比较;最后,与基于灰度变换法、直方图均衡化方法的图像增强法去除阴影的效果进行对比。试验结果表明,该文算法的分割误差、假阳性率、假阴性率平均值分别为3.78%,0.69%和6.8%,分别比光线补偿前降低了21.01,12.79和21.14个百分点;与基于色差(2*R-G-B)自适应灰度阈值分割算法相比,分割误差、假阳性率、假阴性率这3个指标的性能平均提高了12.93,1.45和7.11个百分点;与基于灰度变换法图像增强法比较表明,分割误差、假阳性率、假阴性率平均值分别降低了32.94,6.85和29.65个百分点,与直方图均衡化图像增强法相比,这3个值分别降低了24.92,6.12和33.06个百分点。通过试验结果图的主观判断和评价指标的定量分析,验证了该算法能有效地分割出杨梅目标,保证了杨梅目标在颜色、纹理和形状方面的完整度,研究结果为研究采摘机器人进行杨梅等果实的分割和识别提供参考。

For harvesting robot, fruit identification is the key step for accurate fruit positioning and successful picking. The primary task of fruit identification and picking is to separate fruit from complicated background of branches, trunk and sky by image segmentation. It is hard to accurately segment colorized bayberry image because there are fruits with low brightness or uneven illumination in nature scenes. In this study, RGB （red, green and blue） color space was transformed into HSV （hue, saturation and value） space. After that, the luminance component of image was strengthened by dynamic Butterworth homomorphism filter transfer function. Then, it was restored to RGB color space for colorized image illumination compensation and shadow removal. The bayberry image after shadow removal included red bayberry, green leave and white sky. Each pixel of colorized bayberry image to be segmented was considered as one point of data set X. These pixels were classified into red, green and white. According to the characteristics of the componentsa andb in Lab color space, RGB color space was transformed into CIELAB space. The K-means clustering algorithm was used for image segmentation, and the parameterK was selected as 3. In order to verify the effectiveness of the proposed algorithm, 15 bayberry images were selected from 100 images affected by different degrees of shadow under different growth conditions and uneven illumination conditions. Firstly,in order to prove effectiveness of illumination compensation, theK-means clustering algorithm was used to conduct image segmentation experiments before and after illumination compensation to shadow removal. Secondly, in order to validate segmentation effectiveness of images after illumination compensation based on different methods, this study applied adaptive 2＊R-G-B grey threshold andK-means clustering segmentation algorithms to compare their effects of shadow removal. Thirdly, homomorphism filter algorithm was compared with linear enhancement and histogram equalization methods, and theK-means algorithm was applied to analyze image segmentation effectiveness based on different strengthen methods. The experiments showed that the segmentation result based onK-means clustering algorithm was without wrong segmentation after illumination compensation for shadow removal compared with that before illumination compensation. Although grey threshold based on color difference 2＊R-G-B had better image segmentation effect after illumination compensation, some samples had large wrong segmentation and bright leaves were segmented and classified into bayberry. Therefore, image segmentation by grey threshold based on color difference 2＊R-G-B was worse than that segmentation algorithm based onK-means clustering. Three criteria such as segmentation error, false positive rate （FPR） and false negative rate （FNR） were used toevaluate the segmentations results as quantitative analysis. Under the proposed method in this paper, the average segmentation error, FPR and FNR were 3.78%, 0.69% and 6.8%, respectively. Compared with the gray scale transform method, the segmentation error was reduced by 32.94 percent point, FPR by 6.85 percent point and FNR by 29.65 percent point for this proposed method. Then the average segmentation error was reduced by 24.92 percent point compared with the result obtained by histogram equalization method, FPR by 6.12 percent point and FNR by 20.40 percent point. All these results show that image illumination compensation by homomorphism filter algorithm presents better effect of shadow removal.K-means clustering segmentation algorithm has better image segmentation effect after shadow removal. This paper provides reference for the research on bayberry image segmentation and bayberry fruit recognition.