基于空间特征谱聚类算法的含噪苹果图像优化分割
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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.
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.
引文
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[2]翟瑞芳,方益杭,林承达,等.基于高斯HI颜色算法的大田油菜图像分割[J].农业工程学报,2016,32(8):142-147.Zhai Ruifang,Fang Yihang,Lin Chengda,et al.Segmentation of field rapeseed plant image based on Gaussian HI color algorithm[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(8):142-147.(in Chinese with English abstract)
[3]王丹丹,徐越,宋怀波,等.融合K-means与Ncut算法的无遮挡双重叠苹果目标分割与重建[J].农业工程学报,2015,31(10):227-234.Wang Dandan,Xu Yue,Song Huaibo,et al.Fusion of K-means and Ncut algorithm to realize segmentation and reconstruction of two overlapped apples without blocking by branches and leaves[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(10):227-234.(in Chinese with English abstract)
[4]陈科尹,邹湘军,熊俊涛,等.基于视觉显著性改进的水果图像模糊聚类分割算法[J].农业工程学报,2013,29(6):157-165.Chen Keyi,Zou Xiangjun,Xiong Juntao,et al.Improved fruit fuzzy clustering image segmentation algorithm based on visual saliency[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(6):157-165.(in Chinese with English abstract)
[5]章毓晋.图像工程(中册)[M].北京:清华大学出版社,2005:73-105.
[6]Li XB,Wang YS,Fu LH,et al.Monitoring lettuce growth using K-means color image segmentation and principal component analysis method[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2016,32(12):179-186.(in English with Chinese abstract)
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[9]Ghabousian A,Shamsi M.Segmentation of apple color images utilizing fuzzy clustering algorithms[J].Advances in Digital Multimedia,2012,1(1):59-63.
[10]赵海波,周向红.基于计算机视觉的番茄催熟与正常熟识别[J].农业工程学报,2011,27(2):355-359.Zhao Haibo,Zhou Xianghong.Recognition of artificial ripening tomato and nature mature tomato based on computer vision[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2011,27(2):355-359.(in Chinese with English abstract)
[11]孙国祥,汪小旵,何国敏.基于边缘链码信息的番茄苗重叠叶面分割算法[J].农业工程学报,2010,26(12):206-211.Sun Guoxiang,Wang Xiaochan,He Guomin.Segmentation algorithm of overlapping tomato seedling leaves based on edge chaincode information[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(12):206-211.(in Chinese with English abstract)
[12]宋怀波,张传栋,潘景朋,等.基于凸壳的重叠苹果目标分割与重建算法[J].农业工程学报,2013,29(3):163-168.Song Huaibo,Zhang Chuandong,Pan Jingpen,et al.Segmentation and reconstruction of overlapped apple images based on convex hull[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2013,29(3):163-168.(in Chinese with English abstract)
[13]熊俊涛,邹湘军,陈丽娟,等.基于机器视觉的自然环境中成熟荔枝识别[J].农业机械学报,2011,42(9):162-166.Xiong Juntao,Zou Xiangjun,Chen Lijuan,et al.Based on machine vision environment ripe litchi recognition[J].Transactions of the Chinese Society for Agricultural Machinery,2011,42(9):162-166.(in Chinese with English abstract)
[14]熊俊涛,邹湘军,陈丽娟,等.荔枝采摘机械手果实识别与定位技术[J].江苏大学学报,2012,33(1):1-5.Xiong Juntao,Zou Xiangjun,Chen Lijuan,et al.Litchi fruit picking mechanical hand recognition and location technology[J].Journal of Jiangsu University,2012,33(1):1-5.(in Chinese with English abstract)
[15]李明,李旭,孙松林,等.基于全方位视觉传感器的农业机械定位系统[J].农业工程学报,2010,26(2):170-174.Li Ming,Li Xu,Sun Songlin,et al.Localization system of agricultural machinery based on omni-directional vision sensor[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2010,26(2):170-174.(in Chinese with English abstract)
[16]Shi J,Malik J.Normalized cuts and image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(8):888-905.
[17]Ng A Y,Jordan M I,Weiss Y.On spectral clustering:analysis and an algorithm[C]//Vancouver,BC,Canada.Proceedings of Advances in Neural Information Processing Systems,2002,2:849-856.
[18]王玲,薄列峰,焦李成.密度敏感的半监督谱聚类[J].软件学报,2007,18(10):2412-2422.Wang Lin,Bo Liefeng,Jiao Lichen.Density-sensitive semi-supervised spectral clustering[J].Journal of Software,2007,18(10):2412-2422.(in Chinese with English abstract)
[19]Chen SC,Zhang DQ.Robust image segmentation using FCMwith spatial constraints based on new kernel-induced distance measure[J].IEEE Trans on Systems,Man and Cybernetic,2004,34(4):1907-1916.
[20]Fowlkes C,Belongie S,Chung F,et al.Spectral grouping using the Nystrom Method[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(2):214-225.
[21]刘汉强,赵凤.基于空间特征的谱聚类含噪图像分割[J].模式识别与人工智能,2012,25(3):419-425.Liu Hanqiang,Zhao Feng.Space feature based spectral clustering for noisy image segmentation[J].Pattem Recognition and Aitificial Intelligence,2012,25(3):419-425.(in Chinese with English abstract)
[22]Buades A,Coll B,Morel J M.A non-local algorithm for image denoising[C]//San Diego,CA,United states.Proceeding of the IEEE International Conference on Computer Vision and Pattern Recognition.2005,II:60-65
[23]Macaire L,Vandenbroucke N,Postaire J G.Color image segmentation by analysis of subset connectedness and color homogeneity properties[J].Computer Vision and Image Understanding,2006,102(1):105-116.
[24]Cao F,Liang J,Jiang G.An initialization method for the K-means algorithm using neighborhood model[J].Computers and Mathematics with Applications,2009,58(3):474-483.
[25]谷瑞军,叶宾,须文波.一种改进的谱聚类算法[J].计算机研究与发展,2007(增刊2):145-149.Gu Ruijun,Ye Bin,Xu Wenbo.An improved spectral clustering algorithm[J].Journal of Computer Research and Development,2007(Supp.2):145-149.(in Chinese with English abstract)
[26]纳跃跃,于剑.一种用于谱聚类图像分割的像素相似度计算方法[J].南京大学学报:自然科学版,2013,49(2):159-167.Na Yueyue,Yu Jian.A new pixel affinity for spectral image segmentation[J].Journal of of Nanjing University:Natural Sciences,2013,49(2):159-167.(in Chinese with English abstract)
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