基于计算机视觉的鱼卵胚胎发育过程智能化识别方法研究
发布时间：2018-07-29 11:15
【摘要】：鱼卵质量问题是渔业养殖发展的根本,鱼卵阶段成活率的高低不仅直接影响着幼鱼的生产量,也决定着鱼的未来总产量。开展鱼卵发育过程研究,估计鱼卵生物量,准确鉴别鱼卵发育期,对于估计受精卵的发育能力,掌握环境因子对鱼卵发育的影响,以及提高鱼卵发育质量和效率都是至关重要的。本文以透明鱼卵为研究对象,利用计算机视觉技术、图像处理方法、统计学习理论和模式识别技术等进行了鱼卵发育过程自动化视觉检测方法的研究,实现了鱼卵自动化计数、鱼卵发育阶段自动化识别与分类等操作。主要工作如下：(1)鱼卵自动化计数方法研究。针对图像存在的对比度偏低,反射光线噪声干扰比较严重等问题,提出了基于背景差法的鱼卵感兴趣区域提取算法；建立了基于底帽变换和形态学灰度开运算的图像去噪模型,以及基于伽玛变换的图像增强模型。在图像分割方面,采用Otsu自适应阈值法对增强后的图像进行初始分割,并利用形态学处理实现断裂缝隙的连接、孔洞的填充,以及小目标噪声的去除等后期处理。针对结果图中存在的粘连鱼卵,提出了基于连通域面积分析的改进分水岭分割方法,在有效降低算法运行时间的同时,大幅度降低了过分割现象。实验结果表明：所提算法的计数准确率达90%以上,且与传统方法相比,操作上具有可重复性、灵活性,以及对鱼卵的无破坏性；结果上具有可靠性强、准确度高、不受主观影响等优势。(2)鱼卵个体发育显微图像处理方法研究。针对鱼卵个体图像存在的鱼卵目标亮度低于背景亮度,有少许随机噪声干扰等问题,提出了基于亮度取反运算和中值滤波处理的预处理模型。建立了基于Otsu自适应阈值法和数学形态学处理的鱼卵目标与背景分离算法。提出了基于改进的Sobel算子检测法和形态学处理的不完整鱼卵去除算法,并利用基于连通域个数和圆形度阈值约束的分水岭分割方法实现了粘连鱼卵的有效分割。最后采用改进的迭代阈值化方法对鱼卵目标结果灰度图执行二次分割,实现了鱼卵内核目标的准确提取。对96幅图像分割实验的结果表明本算法可实现所有鱼卵图像的正确识别和分割,识别率达100%。(3)鱼卵个体目标特征提取与选择方法研究。针对鱼卵个体目标特征空间存在的多质性问题,以获取到的110个鱼卵个体目标RGB图、灰度图和二值图为数据基础,分别提取计算了鱼卵目标的18个颜色特征、22个形状特征和11个纹理特征,构成了51维的初始特征集。针对特征空间存在的冗余,相互干扰等问题,以识别正确率为适应度函数的主要评价参数,提出了基于遗传算法的鱼卵目标特征选择方法,从51维的多质特征空间中优选出最具分类能力的16个特征项,实现了特征空间的有效降维。(4)鱼卵发育阶段识别分类方法研究。在分析多种分类方法特点的基础上,分别设计实现了最近邻分类器、BP神经网络分类器和支持向量机(SVM)分类器等多种分类算法,用于进行鱼卵发育阶段自动化识别的研究。其中在研究利用SVM分类方法实现鱼卵发育阶段识别方面,对基于1对多的MSVM算法和基于1对1投票策略的MSVM算法分别进行了算法设计与研究。最后采用留一交叉验证法对设计的分类器进行了测试验证,结果表明：提出的四种分类器的平均运行时间为5.8s、1679.1s、51.2s和29.2s;平均分类正确率为76.2%、71.36%、59.86%和88.13%。因此,研究发现基于1对1投票策略的MSVM分类器更适于鱼卵发育阶段的自动化识别分类。
[Abstract]:The quality of fish eggs is the root of the development of fishery culture. The survival rate of the fish egg stage not only directly affects the production of young fish, but also determines the total output of the fish in the future. The influence of development and the improvement of the quality and efficiency of fish egg development are very important. In this paper, the automatic visual inspection method of fish eggs development process is studied by computer vision technology, image processing method, statistical learning theory and pattern recognition technology, and the automatic counting of fish eggs is realized by using the transparent fish eggs as the research object. Automatic identification and classification of the development stage of fish eggs. The main work is as follows: (1) study on automatic counting method of fish eggs. Aiming at the low contrast of the image and the serious interference of the reflected light noise, the algorithm based on background difference method is proposed, based on the bottom cap transformation and morphology. The image denoising model of gray scale operation and the image enhancement model based on gamma transform. In the aspect of image segmentation, the Otsu adaptive threshold method is used for the initial segmentation of the enhanced image, and the connection of cracks, the filling of the holes, the removal of the small target noise and so on are realized by the morphological processing. The improved watershed segmentation method based on the area analysis of connected domain is proposed in this paper, which reduces the over segmentation greatly while effectively reducing the running time of the algorithm. The experimental results show that the counting accuracy of the proposed algorithm is above 90%, and the operation is repeatable and flexible compared with the transmission method. The results have the advantages of strong reliability, high accuracy and no subjective influence. (2) study on the microscopic image processing method for the individual development of fish eggs. A preprocessing model of median filter processing. A separation algorithm based on Otsu adaptive threshold method and mathematical morphological processing is established. An incomplete fish egg removal algorithm based on improved Sobel operator detection and morphological processing is proposed, and a watershed score based on the number of connected domains and the threshold of roundness threshold is used. The method realizes the effective segmentation of the fish eggs. Finally, the improved iterative threshold method is used to perform two segmentation of the target gray map of the fish egg target. The accurate extraction of the core target of the fish eggs is realized. The results of the 96 image segmentation experiments show that the algorithm can realize the correct recognition and segmentation of all the fish eggs and the recognition rate is 100%. (3) study on the feature extraction and selection method of individual target of fish eggs. Aiming at the multiple quality problem of the individual target characteristic space of the fish eggs, the RGB map of 110 individual target of fish eggs, gray map and two value map are obtained as the data basis, and 18 color features, 22 shape features and 11 texture features are extracted and calculated respectively. The 51 dimensional initial feature set, aiming at the problem of redundancy and mutual interference in the feature space, to identify the main evaluation parameters of the correct rate as the fitness function, proposed the method of selecting the target feature of the fish eggs based on the genetic algorithm, and optimized the 16 feature items with the most classification ability from the 51 dimensional multi feature space, and realized the feature space. Effective dimensionality reduction. (4) research on identification and classification of fish egg development stage. On the basis of analyzing the characteristics of various classification methods, several classification algorithms, such as nearest neighbor classifier, BP neural network classifier and support vector machine (SVM) classifier, are designed and implemented for automatic identification of fish eggs development stage. Using the SVM classification method to realize the identification of fish egg development stage, the algorithm based on 1 pairs of MSVM algorithm and the 1 pair 1 voting strategy based on MSVM algorithm is designed and studied respectively. Finally, the left one cross validation method is used to test and verify the designed classifier. The results show that the average running time of the four classifiers is proposed. For 5.8S, 1679.1s, 51.2s and 29.2s, the average classification accuracy was 76.2%, 71.36%, 59.86% and 88.13%.. Therefore, the study found that MSVM classifier based on 1 to 1 voting strategies was more suitable for automatic identification of fish eggs development stage.
【学位授予单位】：中国农业大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S917.4;TP391.41
，
本文编号：2152512
[Abstract]:The quality of fish eggs is the root of the development of fishery culture. The survival rate of the fish egg stage not only directly affects the production of young fish, but also determines the total output of the fish in the future. The influence of development and the improvement of the quality and efficiency of fish egg development are very important. In this paper, the automatic visual inspection method of fish eggs development process is studied by computer vision technology, image processing method, statistical learning theory and pattern recognition technology, and the automatic counting of fish eggs is realized by using the transparent fish eggs as the research object. Automatic identification and classification of the development stage of fish eggs. The main work is as follows: (1) study on automatic counting method of fish eggs. Aiming at the low contrast of the image and the serious interference of the reflected light noise, the algorithm based on background difference method is proposed, based on the bottom cap transformation and morphology. The image denoising model of gray scale operation and the image enhancement model based on gamma transform. In the aspect of image segmentation, the Otsu adaptive threshold method is used for the initial segmentation of the enhanced image, and the connection of cracks, the filling of the holes, the removal of the small target noise and so on are realized by the morphological processing. The improved watershed segmentation method based on the area analysis of connected domain is proposed in this paper, which reduces the over segmentation greatly while effectively reducing the running time of the algorithm. The experimental results show that the counting accuracy of the proposed algorithm is above 90%, and the operation is repeatable and flexible compared with the transmission method. The results have the advantages of strong reliability, high accuracy and no subjective influence. (2) study on the microscopic image processing method for the individual development of fish eggs. A preprocessing model of median filter processing. A separation algorithm based on Otsu adaptive threshold method and mathematical morphological processing is established. An incomplete fish egg removal algorithm based on improved Sobel operator detection and morphological processing is proposed, and a watershed score based on the number of connected domains and the threshold of roundness threshold is used. The method realizes the effective segmentation of the fish eggs. Finally, the improved iterative threshold method is used to perform two segmentation of the target gray map of the fish egg target. The accurate extraction of the core target of the fish eggs is realized. The results of the 96 image segmentation experiments show that the algorithm can realize the correct recognition and segmentation of all the fish eggs and the recognition rate is 100%. (3) study on the feature extraction and selection method of individual target of fish eggs. Aiming at the multiple quality problem of the individual target characteristic space of the fish eggs, the RGB map of 110 individual target of fish eggs, gray map and two value map are obtained as the data basis, and 18 color features, 22 shape features and 11 texture features are extracted and calculated respectively. The 51 dimensional initial feature set, aiming at the problem of redundancy and mutual interference in the feature space, to identify the main evaluation parameters of the correct rate as the fitness function, proposed the method of selecting the target feature of the fish eggs based on the genetic algorithm, and optimized the 16 feature items with the most classification ability from the 51 dimensional multi feature space, and realized the feature space. Effective dimensionality reduction. (4) research on identification and classification of fish egg development stage. On the basis of analyzing the characteristics of various classification methods, several classification algorithms, such as nearest neighbor classifier, BP neural network classifier and support vector machine (SVM) classifier, are designed and implemented for automatic identification of fish eggs development stage. Using the SVM classification method to realize the identification of fish egg development stage, the algorithm based on 1 pairs of MSVM algorithm and the 1 pair 1 voting strategy based on MSVM algorithm is designed and studied respectively. Finally, the left one cross validation method is used to test and verify the designed classifier. The results show that the average running time of the four classifiers is proposed. For 5.8S, 1679.1s, 51.2s and 29.2s, the average classification accuracy was 76.2%, 71.36%, 59.86% and 88.13%.. Therefore, the study found that MSVM classifier based on 1 to 1 voting strategies was more suitable for automatic identification of fish eggs development stage.
【学位授予单位】：中国农业大学
【学位级别】：博士
【学位授予年份】：2016
【分类号】：S917.4;TP391.41
，
本文编号：2152512
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