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基于opencv实现苹果图像识别,基于opencv实现苹果图像识别2.2万字自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用摘要计算机视觉检测技术是检测技术中一个新兴的应用方向和备受关注的前沿课题，是计算机技术、模式识别、检测技术、数字图像处理、人工智能等多门学科的结晶。如今，计算机视觉技术正在向更智能化的方向发展，即不需要人为干预，便可利...
内容介绍
此文档由会员 淘宝大梦 发布
基于OpenCV实现苹果图像识别
2.2万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘 要
计算机视觉检测技术是检测技术中一个新兴的应用方向和备受关注的前沿课题，是计算机技术、模式识别、检测技术、数字图像处理、人工智能等多门学科的结晶。如今，计算机视觉技术正在向更智能化的方向发展，即不需要人为干预，便可利用图像处理、模式识别等方法，获取一定区域内的信息并自动分析，实现对场景目标的识别、定位甚至跟踪，得出对图像内容含义以及客观场景的理解，最终给出检测结果。在农业机械方面，农业机器人已经逐步应用到农业生产中，特别是在设施农业的生产过程中，机器人的使用将会成为现代农业向自动化和智能化发展的重要标志。但是，相对于国外，我国对农业机器人这一领域的研究还相对落后，且离实际应用还有很大距离。所以，要想转变农业格局、优化农业结构、发展新式农业，必须对农业机器人进行更深入的研究。
本文主要研究农业采摘机器人视觉系统中的图像处理部分，以成熟的苹果图像为研究对象，考虑苹果采摘的实际操作要求，利用计算机图像处理技术及特征提取技术，从苹果的颜色、形状等特征着手，在Linux系统上运用OpenCV等软件，对图像进行预处理，然后使用阈值分割法、分水岭算法等方法对自然情况下成熟苹果图像进行分割、识别，为下一步实现苹果的准确定位与无损采摘打下基础。
此次研究的主要内容有：
1.实验的总体规划和整体设计。本次实验着重于采摘机器人系统中的图像处理环节，运用以C/C++语言为基础的开放源代码的OpenCV软件进行图像处理。OpenCV具有强大的图像和矩阵运算能力，提供了高效和丰富的图像处理算法，使用起来较为简单、快捷。研究中先对苹果图像进行预处理，再运用分水岭算法对苹果图像进行分割、识别。
2.图像的预处理。果实在成长过程中受各种因素的影响，导致表面受到不均匀的光照，使得采集到的苹果图像偏暗或偏亮。针对自然光照条件下采集果实图像的特点，采用直方图均衡化和高斯去噪的方法，增强图像的亮度及对比度，这样既保持果实图像的边缘和细节，又并不增加新颜色。下一步选用HSV颜色空间模型中的H分量，为图像分割做准备。
3.基于分水岭算法图像的分割。选用苹果的形状和颜色作为特征提取对象，通过对预处理后的苹果图像进行阈值分割，将图像粗略的分割成两大区域——苹果、背景；然后运用数学形态学中的膨胀、腐蚀等运算操作，去除噪点，填充苹果的小空洞，并平滑各区域的边界；接下来用距离法标识每个点离边缘的距离，然后用阈值法把两个连在一起的苹果分离开，再将标定里确定的背景、各个苹果及不确定的部分分别用不同编号标识；最后调用OpenCV分水岭算法，灌注各个部分，并确认出最终的边界。这样便将图像中两个相连的苹果从背景中识别出来并相互区分开来。
关键词：农业机器人；苹果采摘；图像处理；OpenCV；图像分割；分水岭算法
ABSTRACT
Computer vision detection technology is an emerging technology to detect the direction and application topics at the forefront of concern, is the crystallization of computer technology, pattern recognition, detection technology, digital image processing, artificial intelligence, and many other disciplines. Today, computer vision technology is to a more intelligent direction that does not require human intervention, you can take advantage of image processing, pattern recognition and other methods to obtain information in a certain area and automatically analyze the scene to identify the target to achieve, even positioning follow, come on image content and objective of the scene to understand the meaning of the final test results are given. Agricultural machinery, agricultural robots have gradually applied to agricultural production, especially in the agricultural production process facilities, the use of robots will become an important symbol of modern agriculture to automation and intelligent development. However, compared to other countries, our research in this area for agricultural robot is relatively backward, and there is a great distance away from the actual application. Therefore, in order to transform the agricultural structure, optimizing agricultural structure, develop new agriculture, agricultural robot must be more in-depth study.
This paper studies the agricultural picking robot vision system image processing section to mature apple image as the research object, consider the practical requirements of apple picking, using computer image processing technology and feature extraction technology from Apple's color, shape and other characteristics to proceed, using OpenCV on Linux systems, such as software, image preprocessing, and then use the threshold segmentation method, and other methods of watershed algorithm ripe apple image segmentation under natural conditions, identification, as the next step to achieve accurate positioning and Apple lossless picking basis.
The main contents are:
1. Overall planning and overall design of the experiment. The experiment focused on picking robot system image processing chain, the use in C / C + + language -based open source OpenCV software for image processing, OpenCV has a powerful image and matrix operations capabilities, provides an efficient and rich image processing algorithms, relatively simple to use and fast. Apple first study image preprocessing, then use Apple watershed image segmentation algorithm to identify.
2. The image preprocessing. In the process of growing fruit affected by variou..
2.2万字
自己原创的毕业论文，仅在本站独家出售，重复率低，推荐下载使用
摘 要
计算机视觉检测技术是检测技术中一个新兴的应用方向和备受关注的前沿课题，是计算机技术、模式识别、检测技术、数字图像处理、人工智能等多门学科的结晶。如今，计算机视觉技术正在向更智能化的方向发展，即不需要人为干预，便可利用图像处理、模式识别等方法，获取一定区域内的信息并自动分析，实现对场景目标的识别、定位甚至跟踪，得出对图像内容含义以及客观场景的理解，最终给出检测结果。在农业机械方面，农业机器人已经逐步应用到农业生产中，特别是在设施农业的生产过程中，机器人的使用将会成为现代农业向自动化和智能化发展的重要标志。但是，相对于国外，我国对农业机器人这一领域的研究还相对落后，且离实际应用还有很大距离。所以，要想转变农业格局、优化农业结构、发展新式农业，必须对农业机器人进行更深入的研究。
本文主要研究农业采摘机器人视觉系统中的图像处理部分，以成熟的苹果图像为研究对象，考虑苹果采摘的实际操作要求，利用计算机图像处理技术及特征提取技术，从苹果的颜色、形状等特征着手，在Linux系统上运用OpenCV等软件，对图像进行预处理，然后使用阈值分割法、分水岭算法等方法对自然情况下成熟苹果图像进行分割、识别，为下一步实现苹果的准确定位与无损采摘打下基础。
此次研究的主要内容有：
1.实验的总体规划和整体设计。本次实验着重于采摘机器人系统中的图像处理环节，运用以C/C++语言为基础的开放源代码的OpenCV软件进行图像处理。OpenCV具有强大的图像和矩阵运算能力，提供了高效和丰富的图像处理算法，使用起来较为简单、快捷。研究中先对苹果图像进行预处理，再运用分水岭算法对苹果图像进行分割、识别。
2.图像的预处理。果实在成长过程中受各种因素的影响，导致表面受到不均匀的光照，使得采集到的苹果图像偏暗或偏亮。针对自然光照条件下采集果实图像的特点，采用直方图均衡化和高斯去噪的方法，增强图像的亮度及对比度，这样既保持果实图像的边缘和细节，又并不增加新颜色。下一步选用HSV颜色空间模型中的H分量，为图像分割做准备。
3.基于分水岭算法图像的分割。选用苹果的形状和颜色作为特征提取对象，通过对预处理后的苹果图像进行阈值分割，将图像粗略的分割成两大区域——苹果、背景；然后运用数学形态学中的膨胀、腐蚀等运算操作，去除噪点，填充苹果的小空洞，并平滑各区域的边界；接下来用距离法标识每个点离边缘的距离，然后用阈值法把两个连在一起的苹果分离开，再将标定里确定的背景、各个苹果及不确定的部分分别用不同编号标识；最后调用OpenCV分水岭算法，灌注各个部分，并确认出最终的边界。这样便将图像中两个相连的苹果从背景中识别出来并相互区分开来。
关键词：农业机器人；苹果采摘；图像处理；OpenCV；图像分割；分水岭算法
ABSTRACT
Computer vision detection technology is an emerging technology to detect the direction and application topics at the forefront of concern, is the crystallization of computer technology, pattern recognition, detection technology, digital image processing, artificial intelligence, and many other disciplines. Today, computer vision technology is to a more intelligent direction that does not require human intervention, you can take advantage of image processing, pattern recognition and other methods to obtain information in a certain area and automatically analyze the scene to identify the target to achieve, even positioning follow, come on image content and objective of the scene to understand the meaning of the final test results are given. Agricultural machinery, agricultural robots have gradually applied to agricultural production, especially in the agricultural production process facilities, the use of robots will become an important symbol of modern agriculture to automation and intelligent development. However, compared to other countries, our research in this area for agricultural robot is relatively backward, and there is a great distance away from the actual application. Therefore, in order to transform the agricultural structure, optimizing agricultural structure, develop new agriculture, agricultural robot must be more in-depth study.
This paper studies the agricultural picking robot vision system image processing section to mature apple image as the research object, consider the practical requirements of apple picking, using computer image processing technology and feature extraction technology from Apple's color, shape and other characteristics to proceed, using OpenCV on Linux systems, such as software, image preprocessing, and then use the threshold segmentation method, and other methods of watershed algorithm ripe apple image segmentation under natural conditions, identification, as the next step to achieve accurate positioning and Apple lossless picking basis.
The main contents are:
1. Overall planning and overall design of the experiment. The experiment focused on picking robot system image processing chain, the use in C / C + + language -based open source OpenCV software for image processing, OpenCV has a powerful image and matrix operations capabilities, provides an efficient and rich image processing algorithms, relatively simple to use and fast. Apple first study image preprocessing, then use Apple watershed image segmentation algorithm to identify.
2. The image preprocessing. In the process of growing fruit affected by variou..
