基于機器視覺的構件表面缺陷特征提取

電子機電論文編號:JD875  附開題報告,外文翻譯,論文字數:18664,頁數:40

摘要 
目次 
1 緒論  1
1.1 引言  1
1.2 課題的研究背景及意義  1
1.3 論文的結構  1
2 機器視覺的簡介  2
2.1 機器視覺技術  2
2.1.1 機器視覺的構成  2
2.1.2 機器視覺的特點  2
2.1.3 機器視覺系統的組成  3
2.2 機器視覺的應用  5
2.2.1 機器視覺應用的現狀  5
2.2.2 機器視覺檢測應用  7
2.3 機器視覺的分類  7
2.3.1 定量檢測  7
2.3.2 定性檢測 8
3 圖像處理軟件介紹  9
3.1 Matlab系統軟件簡介  9
3.1.1 Matlab系統包括五個主要部分  9
3.1.2 Matlab的系統開發環境  9
3.2 Matlab圖象處理命令  12
4 圖像處理基本算法  13
4.1 圖像灰度變換  13
4.1.1 灰度直方圖  14
4.1.2 灰度的線性變換  14
4.1.3 灰度的閾值變換  14
4.1.4 灰度的窗口變換  15
4.1.5 灰度拉伸  15
4.1.6 灰度均衡  16
4.2 圖像增強  16
4.3 圖像的分割  17
4.3.1 閾值分割  18
4.3.2 區域生長法分割  20
5 表面缺陷特征的檢測的結果  23
5.1 圖像的預處理  23
5.2 圖像的分割  25
5.2.1 迭代閾值法分割圖像 25
5.2.2大津法分割圖像  28
5.2.3 區域生長法分割圖像  30
6 總結  34
參考文獻  35
致謝  36
附錄  37

摘要:構件的表面缺陷是影響構件質量的重要因素之一，對它進行檢測將達到質量控制的目的,鑒于這樣的目的，本設計介紹的是基于機器視覺的構件的表面缺陷特征提取。
本文在現有技術基礎上，通過了大量的實驗和圖像算法仿真，以現有的圖像處理理論為基礎，設計了適合本設計所涉及的表面缺陷檢測的算法，具體的工作如下：
(1).通過確當的途徑獲取缺陷圖像，并以圖像進行灰度變換、中值濾波對圖像進行增強處理，為下一步圖像的進一步處理做前期的準備。
(2).對圖像表面缺陷進行研究，這一過程本文采用了大津法、區域生長法、迭代法這三種分割方法，并且認真研究設計了它們的算法。
(3).把各個缺陷圖片的分割算法在MATLAB中進行算法仿真，提取正確的實驗結果，并且重復多次實驗，確保結果的精確性。
關鍵詞:機器視覺；表面缺陷；圖像增強；圖像分割

The Extraction of Surface Defect Characteristics of Component Parts Based on Machine Vision

Abstract: The surface defects of component parts are an important factor affecting the quality of the component parts, so defect detection is a means to achieve the object of quality control; this article presents the extraction of surface defect characteristics of component parts based on machine vision. Through large amount of experiments and the simulation with image algorithm, and based on the existing image process theory, this article designs an algorithm that is suitable to the surface defect detection related to this design on the basis of existing technology. The specific process is as follows:
    (1) To obtain defect images through appropriate approach and conduct gray-level transformation and the images are enhancement processed with median filtering, so as to get ready for further processing.
(2) To analyze the surface defects of the images using three segmentation methods, i.e. OTSU method, region-growing method, and iteration method, and their algorithms are studied and designed.
(3) To carry out algorithm simulation in MATLAB to simulate the segmentation algorithm of the defect images, and correct experimental results are extracted, and it is repeated for several times so as to ensure the accuracy.
Key words: machine vision; surface defect; image enhancement; image segmentation