徐伟悦,田光兆,姬长英,张波,蒋思杰,张纯.自然场景下苹果图像FSLIC超像素分割方法[J].农业机械学报,2016,47(9):1-10.
Xu Weiyue,Tian Guangzhao,Ji Changying,Zhang Bo,Jiang Sijie,Zhang Chun.FSLIC Superpixel Segmentation Algorithm for Apple Image in Natural Scene[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(9):1-10.
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自然场景下苹果图像FSLIC超像素分割方法   [下载全文]
FSLIC Superpixel Segmentation Algorithm for Apple Image in Natural Scene   [Download Pdf][in English]
投稿时间:2016-06-01  
DOI:10.6041/j.issn.1000-1298.2016.09.001
中文关键词:  苹果图像  超像素分割  自然场景  简单线性迭代聚类
基金项目:国家自然科学基金项目(31401291)、江苏省自然科学基金项目(BK20140720、BK20140729)和中央高校基金科研业务费专项资金项目(KYZ201325)
作者单位
徐伟悦 南京农业大学
江苏省智能化农业装备重点实验室 
田光兆 南京农业大学
江苏省智能化农业装备重点实验室 
姬长英 南京农业大学
江苏省智能化农业装备重点实验室 
张波 南京农业大学
江苏省智能化农业装备重点实验室 
蒋思杰 南京农业大学
江苏省智能化农业装备重点实验室 
张纯 南京农业大学
江苏省智能化农业装备重点实验室 
中文摘要:应用Cauchy-Schwarz不等式,推导出一个聚类搜索过程中剥离不必要计算的条件,早期预估后舍掉符合预设条件的候选聚类,提出了基于自然场景的快速简单线性迭代聚类算法(FSLIC算法)。对包含极端恶劣条件下的500幅苹果图像进行了边界召回率检验和运行速度测试;统计了极端恶劣条件下的30幅苹果图像的全局错误率GCE、假阳性率FPR和假阴性率FNR。试验表明,提出的FSLIC算法减小了后续迭代过程中的冗余误差,边界召回率较GB超像素分割算法平均提高了21.7%,速度是GB超像素分割算法的1.83倍;整个图像分割过程中基于超像素的分割算法(GB、FSLIC)的GCE值较常规分割算法(BP、WT、SVM)平均减小了13%,较常规算法的GCE值减小了19%。
Xu Weiyue  Tian Guangzhao  Ji Changying  Zhang Bo  Jiang Sijie  Zhang Chun
Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province,Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province,Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province,Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province,Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province and Nanjing Agricultural University;Intelligent Agriculture Equipment Key Laboratory in Jiangsu Province
Key Words:apple image  superpixel segmentation  natural scene  simple linear iterative clustering
Abstract:Real time efficiency is one of the bottleneck problems in the field of image processing, especially in the natural scene of the agricultural robot vision system. Nowadays superpixel segmentation algorithm was proposed as the high robustness to deal with the random uncertainty in natural scene. Simple linear iterative clustering(SLIC) has drawn much attention due to its outstanding performance in terms of accuracy, speed, anti shadow and anti highlight. In this paper, by applying the Cauchy-Schwarz inequality, we derived a condition to leave unnecessary operations from the cluster inspection procedure. In the proposed algorithm, we reduced the redundant computation by using a robust inequality condition based on weighted L2 norm of pixel and cluster center representation. Then we put up with an advanced algorithm: FSLIC algorithm. We built a database with 2000 apple images in almost all natural conditions. Several kinds of extreme situations were chosen: high intensity of illumination light condition, low intensity of illumination backlight condition, uneven illumination of cloudy condition, adjacency and severe adhesion condition. The error rate curves of the insufficient segmentation, the hit rate curves of the boundary and execution time were analyzed with the 500 apple images; the GCE, FNR and FPR were detected with the 30 images in extreme condition. In the experimental results, it was confirmed that the GCE in Graph based and FSLIC algorithm was reduced by 13% than BP algorithm, WT algorithm and SVM algorithm, the GCE in FSLIC algorithm was reduced by 19% than the traditional algrithms. The hit rate of the boundary in FSLIC algorithm was increased by 21.7% and the speed was 1.83 times than Graph based algorithm.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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