基于机器视觉技术的鱼类识别研究进展
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摘要
随着机器视觉技术的快速发展,物体识别技术逐渐成为机器视觉技术研究的核心内容。而鱼类识别技术可以辅助海洋特定鱼类物种的分布调查统计、海洋生态系统监测以及水族馆自动识别出鱼类种类信息等。通过比较基于特征值、相关系数、分级分类、支持向量机、神经网络等机器视觉技术的鱼类识别方法,介绍了有关鱼类识别技术的研究背景应用、软硬件系统环境搭建、鱼类特征提取以及基于机器视觉鱼类识别技术存在的问题与展望。
In view of the rapid development of machine vision technology, object recognition technology has gradually become the core of machine vision technology research. Fish identification technology can assist the investigation of specific ocean fish species distribution, marine ecosystem monitoring and automatic identification of fish species in the aquarium. The article compares the machine vision technology of fish species identification based on eigenvalue comparison, correlation coefficient, grading classification, support vector machine, neural network and so on. The research background and application of fish identification technology, the construction of hardware and software system environment, fish feature extraction, analysis, classification method and problems based on machine vision fish identification technology are introduced.
In view of the rapid development of machine vision technology, object recognition technology has gradually become the core of machine vision technology research. Fish identification technology can assist the investigation of specific ocean fish species distribution, marine ecosystem monitoring and automatic identification of fish species in the aquarium. The article compares the machine vision technology of fish species identification based on eigenvalue comparison, correlation coefficient, grading classification, support vector machine, neural network and so on. The research background and application of fish identification technology, the construction of hardware and software system environment, fish feature extraction, analysis, classification method and problems based on machine vision fish identification technology are introduced.
引文
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[3] 张雅之,黄良敏.厦门东海域鱼类的群落结构及种类多样性研究[J].热带海洋学报,2009,28(2):66-76.
[4] 俞存根,陈全震,陈小庆,等.舟山渔场及临近海域鱼类种类组成和数量分布[J].海洋与湖沼,2010,41(1):410-417.
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[6] 王颖,张旗,赵国库,等.基于形变校正的形状识别方法[J].计算机工程与设计,2011,32(11):3789-3792.
[7] 吴一全,殷骏,戴一冕,等.基于蜂群优化多核支持向量机的淡水鱼种类识别[J].农业工程学报,2014,30(16):32-319.
[8] CHAMBAH M ,SEMANI D ,RENOUF A ,et al.Underwater color constancy:Enhancement of automatic live fish recognition[J].Proceedings of SPIE-The International Society for Optical Engineering,2004(5293):157-168.
[9] 张志强,牛智有,赵思明.基于机器视觉技术的淡水鱼品种识别[J].农业工程学报,2011,27(11):388-392.
[10] 涂兵,王锦萍,王思成,等.基于背部轮廓相关系数算法的淡水鱼种类识别研究[J].计算机工程与应用,2016,52(16):162-166.
[11] 林明旺.深度学习在鱼类图像识别与分类中的应用[J].数字技术与应用,2017(4):96-97.
[12] 张胜茂,张衡,唐锋华,等.计算机视觉技术在监测鱼类游泳行为中的研究进展[J].大连海洋大学学报,2017,32(4):493-500.
[13] 毛家发,胡海彪,程振波,等.半遮挡目标鱼体的识别与跟踪方法研究[J].浙江工业大学学报,2015,43(2):207-211.
[14] SPAMPINATO C ,GIORDANO D ,DISALVO R ,et al.Automatic fish classification for underwater species behavior understanding[C]// ACM International Workshop on Analysis & Retrieval of Tracked Events & Motion in Imagery Streams,2010.
[15] 万鹏,潘海兵,宗力,等.基于机器视觉的鲫鱼和鲤鱼品种识别方法研究[J].广东农业科学,2012,39(17):184-187.
[16] RAVANBAKHSH M,SHORTIS M R,SHAFAIT F,et al.Automated fish detection in underwater images using shape‐based level sets[J].The Photogrammetric Record,2015,30(149):46-62.
[17] ALMASDI M K,OMAR K B,NOAH S A.Fish recognition based on robust features extraction from size and shape measurements using neural network[J].Journal of Computer Science,2010,6(10):1059-1065.
[18] XIN Y,PAWLAK M,LIAO S.Accurate computation of Zernike moments in polar coordinates[J].IEEE Transactions on Image Processing,2007,16(2):581-587.
[19] YAP P T,PARAMESRAN R,ONG S H.Image analysis by Krawtchouk moments[J].IEEE Transactions on Image Processing,2003,12(11):1367-1377.
[20] NASREDDINE K,BENZINOU A.Shape-based fish recognition via shape space[C]// Signal Processing Conference.IEEE,2016:145-149.
[21] RANJIDHA A,RAMESH KUMAR A,SARANYA M.Survey on medical image retrieval based on shape features and relevance vector machine classification[C]// International Journal of Emerging Trends & Technology in Computer Science,2013.
[22] 薄华,马缚龙,焦李成.图像纹理的灰度共生矩阵计算问题的分析[J].电子学报,2006,34(1):155-158.
[23] HARALICK R M.Texture features for image classification[J].Systems Man & Cybernetics IEEE Transactions on,1973,smc-3(6):610-621.
[24] HU J,LI D,DUAN Q,et al.Fish species classification by color,texture and multi-class support vector machine using computer vision[J].Computers & Electronics in Agriculture,2012,88(4):133-140.
[25] 冈萨雷斯.数字图像处理[M].北京:电子工业出版社,2005.
[26] 刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4):622-635.
[27] SPAMPINATO C,GIORDANO D,SALVO R D,et al.Automatic fish classification for underwater species behavior understanding[C]// ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams,2010:45-50.
[28] ALSMADI M K,OMAR K,NOAH S A M,et al.Fish recognition based on robust features extraction from color texture measurements using back-propagation classifier[J].Journal of Theoretical and Applied Information Technology,2010,1(18):11-18.
[29] 高士忠.基于灰度共生矩阵的织物纹理分析[J].计算机工程与设计,2008,29(16):4385-4388.
[30] CHINDARO S ,SIRLANTZIS K ,DERAVI F .Colour space fusion for texture recognition[C]// IEEE,2003.
[31] BENCO M,HUDEC R.Novel method for color textures features extraction based on GLCM[J].Radioengineering,2007,16(4):64-67.
[32] 侯群群,王飞,严丽.基于灰度共生矩阵的彩色遥感图像纹理特征提取[J].国土资源遥感,2013,25(4):26-32.
[33] DAUGMAN J G.DAUGMAN,J.G.Uncertainty relation for resolution in space,spatial frequency,and orientation optimized by two-dimensional visual cortical filters[J].Journal of the Optical Society of American Optics & Image Science,1985,2(7):1160-1169.
[34] SENGUR A,TURKOGLU I,INCE M C.Wavelet packet neural networks for texture classification[J].Expert Systems with Applications,2007,32(2):527-533.
[35] 杨璟,朱雷.基于RGB颜色空间的彩色图像分割方法[J].计算机与现代化,2010(8):147-149.
[36] YANG N C,CHANG W H,KUO C M,et al.A fast MPEG-7 dominant color extraction with new similarity measure for image retrieval[J].Journal of Visual Communication & Image Representation,2008,19(2):92-105.
[37] YU Z,JAIN A K.Object localization using color,texture and shape[J].Pattern Recognition,2000,33(4):671-684.
[38] 姜兰池,沈国强,张国煊.基于HSV分块颜色直方图的图像检索算法[J].机电工程,2009,26(11):54-57.
[39] ALSMADI M K,OMAR K B,NOAH S A M.Fish classification based on robust features extraction from color signature using back-propagation classifier[J].Journal of Computer Science,2011,7(1):52-58.
[40] SPAMPINATO C,GIORDANO D,SALVO R D,et al.Automatic fish classification for underwater species behavior understanding[C]// ACM International Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams,2010:45-50.
[41] 傅小康,郑建立,王润田.基于鱼体回声图像的鱼类识别算法[J].测试技术学报,2006,20(2):150-156.
[42] 余心杰,吴雄飞,王建平,等.基于机器视觉的大黄鱼形态参数快速检测方法[J].集成技术,2014(5):45-51.
[43] 吴建辉,张晓飞,杨敏,等.基于鱼体背部弯曲潜能算法的四种主养鱼类识别[J].淡水渔业,2016,46(4):101-106.
[44] HUANG P X.Hierarchical classification for live fish recognition[J].Bmvc Student Workshop Paper,2012.
[45] HUANG P X,BOOM B J,FISHER R B.GMM improves the reject option in hierarchical classification for fish recognition[C]// Applications of Computer Vision IEEE,2014:371-376.
[46] HUANG P X .Hierarchical classification system with reject option for live fish recognition[C]// Conference on Empirical Methods in Natural Language Processing,2016.
[47] CHUANG M C,HWANG J N,KUO F F,et al.Recognizing live fish species by hierarchical partial classification based on the exponential benefit[C]// IEEE International Conference on Image Processing IEEE,2015:5232-5236.
[48] 张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-41.
[49] 于世飞,齐丙娟,谭红艳.支持向量机理论与算法研究综述[J].电子科技大学学报,2011,40(1):2-10.
[50] ROVA A,MORI G,DILL L M.One fish,two fish,butterfish,trumpeter:recognizing fish in underwater video[C]// Iapr Conference on Machine Vision Applications DBLP,2007:404-407.
[51] JORGE C G,MODESTO C S,ANTONIO D B,et al.Exploring the use of local descriptors for fish recognition in life[J].CLEF 2015.
[52] OGUNLANA O S,OLABODE O,OLUWADARE S A A,et al.Fish classification using support vector machine[J].African Journal of Computing & ICT,2015,8(2):75-82.
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