复杂背景下甜瓜果实分割算法

为解决复杂背景下甜瓜果实与背景图像分割的问题,该文提出了一种融合颜色特征和纹理特征的图像分割算法。首先,把采集到的甜瓜果实图像从RGB色彩空间分别转换到CIELAB和HSV色彩空间,应用a*b*分量建立角度模型,根据甜瓜果实的颜色特点选取阈值并对图像作二值化处理;为降低光照分布不均匀对图像分割的影响,采用HSV空间的HS颜色分量对果实图像进行阈值分割。在以上2种色彩空间分割的基础上,融合角度模型分割和HS阈值分割的结果,得到基于颜色特征的分割结果。然后,再按照图像的纹理特征对图像进行分割处理,融合按照颜色特征和纹理特征的分割结果。最后,为解决分割结果中的分割误差和边缘毛刺问题,以颜色特征分割的果实区域为限定条件,对按照融合特征分割的果实区域进行约束性区域生长,得到最终的图像分割结果。为了对该文提出算法的分割效果进行检验,采用超绿阈值分割算法和归一化差异指数算法(NDI)对试验图像进行分割,3种算法的平均检出率分别为83.24%、43.12%、99.09%。对比3种分割算法的检出率和误检率,可以看出,该文提出的算法试验结果明显优于超绿阈值分割算法和归一化差异指数(NDI)分割算法。

Segmentation algorithm of muskmelon fruit with complex background

Abstract: In order to solve the problem of muskmelon fruit image segmentation under a complex background, an algorithm of image segmentation based on fusing color feature and texture feature was proposed in this paper. First, the collected muskmelon fruit images were transformed from RGB color space to CIELAB color space and HSV space respectively. According to the color characteristics of muskmelon fruit, the collected images were binarized using the threshold of angle model that was set up in using a*b* components in CIELAB color space. To reduce the influence of the uneven illumination distribution of segmentation, the H S components segmentation threshold was selected to binarize the collected images. Converging the results of the angle model segmentation and the HS weighted threshold segmentation, the results were obtained based on color feature segmentation. Then, the image texture features were extracted and the binarization images were obtained by using texture feature threshold. The segmentation results were achieved by fusing the texture features and the color feature segmentation result. Finally, taking the fruit color feature segmentation area as the qualification, the final segmentation results were obtained by binding growth based on the segmentation area that were obtained by fusing color features and texture features. In order to evaluate the effect of proposed algorithm, the collected images were segmented using the super green threshold algorithm and the NDI algorithm and the results were gained. The average detection rate of three algorithms were 83.24%, 43.12% and 99.09%, respectively. Comparing the results of the detection rate and false detection rate, the experimental results of the proposed algorithm were superior to super green feature segmentation and normalized difference index (NDI) segmentation algorithm.