摘要
为了减少噪声对苹果采摘机器人的目标识别所带来的影响,对含噪苹果图像的分割方法进行了研究。该研究设计一种针对噪声具有鲁棒性的苹果图像分割方法,首先计算苹果图像的三维空间特征点的紧致性函数,用以构造邻近点的相似矩阵实现苹果图像的去噪效果;再利用离群点矩阵拆分并由其他剩余列向量线性表示,对相似矩阵进行离群点调优实现聚类优化,进而提出基于空间特征的谱聚类含噪苹果图像分割的优化算法,旨在提高分割算法的效率和识别准确率。通过对两幅苹果图像添加不同程度的高斯和椒盐噪声(方差分别为0.01、0.05和0.1的高斯噪声和概率为0.01、0.05和0.1的椒盐噪声)进行试验,分别求出谱聚类方法、基于空间特征的谱聚类方法和该文优化方法的苹果目标图像的分割图,并计算三类方法的分割准确率。该文优化方法对于单个苹果受不同噪声影响下的分割准确率均在99%以上,对于重叠苹果的分割准确率均在98%以上,对于所选取的30幅苹果图在方差为0.05的高斯噪声和概率为0.01的椒盐噪声影响下的平均分割准确率为99.014%。结果表明:谱聚类方法受噪声的影响较大;基于空间特征的谱聚类方法的分割效果受噪声的影响较小,但在边界区域仍然有很多错分的像素;优化方法在边界区域的分割要优于基于空间特征的谱聚类方法;在设定的试验条件下,其分割结果准确率相对于基于空间特征的谱聚类方法和传统的谱聚类方法可分别提高5%~6%和9%~25%。在分割效率方面,该文优化方法的分割时间低于传统的谱聚类算法,且与基于空间特征谱聚类方法接近。研究结果为苹果采摘机器人的快速目标识别提供参考。
Restricted by imaging equipment and external natural environment, apple image produces lots of noise in the process of collection and transmission, which is one of the important factors that affect the accuracy and efficiency of image recognition. In order to reduce the effect of the noise on the target identification of apple harvesting robot, the segmentation method for apple image with noise is studied, which is not affected by noise. Firstly, by constructing similarity matrix, gray value, local spatial information and non-local spatial information of each pixel are utilized to construct a three-dimensional feature dataset. And then, the space compactness function is introduced to compute the similarity between each feature point and its nearest neighbors. Obviously, the similarity matrix is sparse matrix. Secondly, the outliers of similarity matrix are tuned by splitting the outlier matrix and representing it linearly with the other remaining column vector. Finally, tuned similarity matrix is decomposed by Laplacian vector, and eigenvector matrix is constructed and then normalized; the next step is that row vector of the matrix is clustered by k-means algorithm. The clustering result is obtained for three-dimensional feature dataset, and the image segmentation result is also obtained. The experiments of 2 apple images are carried out to validate the optimization algorithm proposed in the paper. The segmentation accuracy of the optimization method for a single apple under the influence of different noise is over 99%. The segmentation accuracy is over 98% for overlapping apple. The segmentation accuracy rate is 99.014% on average for 30 apple images, which is under the influence of Gaussian noise with the variance of 0.05 and salt and pepper noise with the probability of 0.01. The results of optimization method are compared with the results of the original spectral clustering algorithm and the spectral clustering algorithm based on space feature. The advantage of the optimization method is achieving de-noising effect. Also, the role of tuning the similar matrix's outliers is to achieve clustering optimization. In the setting conditions of this experiment, the segmentation accurate rate can be improved by 5%-6% compared to the spectral clustering algorithm based on space feature, and by 9%-25% compared to the original spectral clustering algorithm. At the end, the running time is analyzed and compared for the algorithms, and the experiments of 30 images, which contain 3 types of images i.e. 128×128, 256×256 and 512×512 pixels and each type has 10 images, are carried out to validate the algorithm's efficiency. From the result of experiments, we know the optimization algorithm's running time is less than the original spectral clustering algorithm and is close to the spectral clustering algorithm based on space feature on the premise of achieving better segmentation accurate rate. Through the analysis and comparison, the conclusions obtained from the study are as follows: first, the optimization algorithm has the robustness for the noise; second, the optimization algorithm reduces the wrong rate of the boundary region's pixels; third, the optimization algorithm improves the segmentation accuracy and efficiency. The results provide a reference for fast target recognition of apple harvesting robot.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第16期159-167,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金资助项目(31571571)
江苏省高校优势学科建设项目(PAPD)
高等学校博士学科点专项科研基金(20133227110024)
关键词
图像分割
算法
水果
空间特征
谱聚类
聚类优化
image segmentation
algorithms
fruits
space feature
spectral clustering
clustering optimization