基于rbf网络的典型机械零件.doc
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基于rbf网络的典型机械零件,摘要在科技和工业不断发展的同时，地球的资源也在极度的消耗之中，如何节约和再利用资源也越来越受到重视。在这种环境下，本文利用机器视觉技术对废旧机械零件进行识别分类研究，以便于以后再次循环利用。机器视觉是一门通过研究图像或视频数据来观察周围世界的学科，其核心内容是图像的处理和识别。机器视觉是现代制造的一个重要组成部分，在现...
内容介绍
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摘要
在科技和工业不断发展的同时，地球的资源也在极度的消耗之中，如何节约和再利用资源也越来越受到重视。在这种环境下，本文利用机器视觉技术对废旧机械零件进行识别分类研究，以便于以后再次循环利用。机器视觉是一门通过研究图像或视频数据来观察周围世界的学科，其核心内容是图像的处理和识别。机器视觉是现代制造的一个重要组成部分，在现代机械行业中，为了提高生产的柔性和自动化程度，机器视觉得到了广泛的应用。例如，机械零件的检测和识别、产品分类以及自动化生产的装配检测等。因此，利用机器视觉对机械零件进行识别研究具有重要的意义。
本文主要以废旧螺栓为研究对象，根据螺栓的几何形状特征提出了一种适合螺栓等典型机械零件的识别方法。本文的主要工作内容包括图像识别理论和方法的介绍与分析、机械零件图像的预处理、图像的特征提取、机械零件识别和典型零件识别系统的设计。
在图像识别理论及方法方面，本文主要对图像匹配识别、统计模式识别、句法模式识别、模糊模式识别和人工神经网络模式识别等图像识别方法进行介绍和分析。
在图像的预处理方面，本文着重对图像去噪和图像边缘提取进行研究。在对图像进行去噪的过程中，本文主要对均值滤波、中值滤波和小波滤波进行研究。通过对几种方法的比较，发现经过中值滤波和均值滤波相结合的混合滤波方法具有较好的滤波效果。在机械零件图像的边缘提取过程中，经过对现有几种常见边缘检测算子的比较研究，发现利用Sobel算子进行边缘提取，具有速度快、效果好的优点。
在特征的提取方面，本文着重根据螺栓的几何形状特征对螺栓进行特征提取的研究。本文提出的螺栓的特征向量具有尺寸不变性、旋转不变性和平移不变性等优点。在识别的算法方面，本文根据提取出来的螺栓的特征向量利用RBF网络对螺栓进行识别研究。仿真实验表明RBF网络技术应用于零件识别，具有较好的识别效果，且识别速度快。
关键词：特征提取；零件识别；预处理；几何形状特征；RBF网络
Abstract
With the technology and industry developing, the earth's resources are being consumed extremely. So how to save and reuse of resources are getting more and more attention. Under such conditions, we use the machine vision technology to identify classification of wasted machine parts for recycling in the future again. Machine vision is a discipline of studying image or video data to observe the world around. Its core content is the image processing and recognition. Its core content is the image processing and recognition. Machine vision is an important component of modern manufacturing. In order to raise flexibility and automation in production, machine vision has been widely used. Such as, detection and identification of mechanical parts, product classification, assembly testing in automated production. Using machine vision to study identification of mechanical parts is significant.
In this paper, mainly used wasted bolts for study object, according to the geometry characteristics of the bolt, we propose a suitable method for identification of bolts and other typical mechanical parts. Recognition of the simple machines, the main content of the work with mechanical parts image preprocessing, image feature extraction, part identification and part identification system design simple. In this paper, the main work includes analysis and introduces the theory and method of image recognition, the image preprocessing of machine parts, mechanical parts identification, the design of typical parts identification system.
In theory and methods of image recognition, this article mainly introduces and analyzes the image matching recognition, statistical pattern recognition, syntactic pattern recognition, fuzzy pattern recognition, artificial neural network pattern recognition and other image recognition.
In image preprocessing, the paper focuses on studying the image denoising and the image edge extraction. In the process of image denoising, the paper mainly studies the mean filter, median filter and wavelet filter. Through the comparison of several methods, we find a hybrid filter method which combined by median filter and mean filter has better filtering effect. In the image edge extraction of mechanical parts , after comparing several common edge detection operator, we find using Sobel operator for edge detection is fast and effective.
In the feature extraction, the paper mainly studies extracting the bolt feature based on the geometry characteristics of the bolt. The bolt characteristic vector which this paper presented has certain advantages. Such as , scale invariant, rotational invariance and translation invariance. In the recognition algorithm, based on the extracted feature vector of the bolt, the RBF network is used in the bolt identification. Simulation results show that the RBF network technology used in parts identification, with good recognition effect, and quick recognition rate.
Key words: feature extraction；parts identification；pretreatment；geometry characteristics；
RBF network
目录
摘要 I
Abstract II
第1章 绪论 1
1.1 引言 1
1.2 机械零件图像识别和检测的研究现状及发展趋势 1
1.2.1 机械零件图像识别和检测的研究现状 2
1.2.2 机械零件图像识别和检测的发展趋势 3
1.3 机械零件图像识别研究的内容 4
1.4 选题的研究背景和意义 5
1.5 本文的主要研..
在科技和工业不断发展的同时，地球的资源也在极度的消耗之中，如何节约和再利用资源也越来越受到重视。在这种环境下，本文利用机器视觉技术对废旧机械零件进行识别分类研究，以便于以后再次循环利用。机器视觉是一门通过研究图像或视频数据来观察周围世界的学科，其核心内容是图像的处理和识别。机器视觉是现代制造的一个重要组成部分，在现代机械行业中，为了提高生产的柔性和自动化程度，机器视觉得到了广泛的应用。例如，机械零件的检测和识别、产品分类以及自动化生产的装配检测等。因此，利用机器视觉对机械零件进行识别研究具有重要的意义。
本文主要以废旧螺栓为研究对象，根据螺栓的几何形状特征提出了一种适合螺栓等典型机械零件的识别方法。本文的主要工作内容包括图像识别理论和方法的介绍与分析、机械零件图像的预处理、图像的特征提取、机械零件识别和典型零件识别系统的设计。
在图像识别理论及方法方面，本文主要对图像匹配识别、统计模式识别、句法模式识别、模糊模式识别和人工神经网络模式识别等图像识别方法进行介绍和分析。
在图像的预处理方面，本文着重对图像去噪和图像边缘提取进行研究。在对图像进行去噪的过程中，本文主要对均值滤波、中值滤波和小波滤波进行研究。通过对几种方法的比较，发现经过中值滤波和均值滤波相结合的混合滤波方法具有较好的滤波效果。在机械零件图像的边缘提取过程中，经过对现有几种常见边缘检测算子的比较研究，发现利用Sobel算子进行边缘提取，具有速度快、效果好的优点。
在特征的提取方面，本文着重根据螺栓的几何形状特征对螺栓进行特征提取的研究。本文提出的螺栓的特征向量具有尺寸不变性、旋转不变性和平移不变性等优点。在识别的算法方面，本文根据提取出来的螺栓的特征向量利用RBF网络对螺栓进行识别研究。仿真实验表明RBF网络技术应用于零件识别，具有较好的识别效果，且识别速度快。
关键词：特征提取；零件识别；预处理；几何形状特征；RBF网络
Abstract
With the technology and industry developing, the earth's resources are being consumed extremely. So how to save and reuse of resources are getting more and more attention. Under such conditions, we use the machine vision technology to identify classification of wasted machine parts for recycling in the future again. Machine vision is a discipline of studying image or video data to observe the world around. Its core content is the image processing and recognition. Its core content is the image processing and recognition. Machine vision is an important component of modern manufacturing. In order to raise flexibility and automation in production, machine vision has been widely used. Such as, detection and identification of mechanical parts, product classification, assembly testing in automated production. Using machine vision to study identification of mechanical parts is significant.
In this paper, mainly used wasted bolts for study object, according to the geometry characteristics of the bolt, we propose a suitable method for identification of bolts and other typical mechanical parts. Recognition of the simple machines, the main content of the work with mechanical parts image preprocessing, image feature extraction, part identification and part identification system design simple. In this paper, the main work includes analysis and introduces the theory and method of image recognition, the image preprocessing of machine parts, mechanical parts identification, the design of typical parts identification system.
In theory and methods of image recognition, this article mainly introduces and analyzes the image matching recognition, statistical pattern recognition, syntactic pattern recognition, fuzzy pattern recognition, artificial neural network pattern recognition and other image recognition.
In image preprocessing, the paper focuses on studying the image denoising and the image edge extraction. In the process of image denoising, the paper mainly studies the mean filter, median filter and wavelet filter. Through the comparison of several methods, we find a hybrid filter method which combined by median filter and mean filter has better filtering effect. In the image edge extraction of mechanical parts , after comparing several common edge detection operator, we find using Sobel operator for edge detection is fast and effective.
In the feature extraction, the paper mainly studies extracting the bolt feature based on the geometry characteristics of the bolt. The bolt characteristic vector which this paper presented has certain advantages. Such as , scale invariant, rotational invariance and translation invariance. In the recognition algorithm, based on the extracted feature vector of the bolt, the RBF network is used in the bolt identification. Simulation results show that the RBF network technology used in parts identification, with good recognition effect, and quick recognition rate.
Key words: feature extraction；parts identification；pretreatment；geometry characteristics；
RBF network
目录
摘要 I
Abstract II
第1章 绪论 1
1.1 引言 1
1.2 机械零件图像识别和检测的研究现状及发展趋势 1
1.2.1 机械零件图像识别和检测的研究现状 2
1.2.2 机械零件图像识别和检测的发展趋势 3
1.3 机械零件图像识别研究的内容 4
1.4 选题的研究背景和意义 5
1.5 本文的主要研..
