Surface Defect Inspection and Classification of Segment Magnet by Using Machine Vision Technique

2011 ◽  
Vol 339 ◽  
pp. 32-35 ◽  
Author(s):  
Hong Hai Jiang ◽  
Guo Fu Yin
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Surface Defects ◽  
Processing System ◽  
Support Vector ◽  
Inspection System ◽  
Low Contrast ◽  
Constituent Material ◽  
Canny Edge

In this paper, we propose a machine vision based approach for detecting and classifying irregular low-contrast surface defects of segment magnet. The constituent material of it is ferrite which varies from silver gray to black in color .For this reason, the defects embedded in a low-contrast surface show no big different from its surrounding region, and even worse, all the surfaces and chamfers of segment magnet must be inspected. Our system is able to analyze all surfaces under inspection, to discover and classify its defects by means of image processing algorithms and support vector machine (SVM). A working prototype of the system has been built and tested to validate the proposed approach and to reproduce the difficult issues of the inspection system. The developed prototype includes three subsystems: an array of several CCD area cameras (Fig.1); a controllable roller LED light source(Fig.1); and a PC-based image processing system. The detection of the defects is performed by means of Canny edge detection, morphology and other feature extraction operations. The image processing and classification results demonstrate that the proposed method can identify surface defects effectively.

Color Region Classification of Flue-cured Tobacco Leaves Based on the Image Processing and Support Vector Machine

2013 ◽  
Vol 38 (2) ◽  
pp. 374-379 ◽  
Author(s):  
Zhi-Li PAN ◽  
Meng QI ◽  
Chun-Yang WEI ◽  
Feng LI ◽  
Shi-Xiang ZHANG ◽  
...  
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Support Vector ◽  
Tobacco Leaves ◽  
Region Classification

scholarly journals An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

2019 ◽  
Vol 52 (7-8) ◽  
pp. 1102-1110 ◽  
Author(s):  
Yu Wu ◽  
Yanjie Lu
Keyword(s):  
Support Vector Machine ◽  
Machine Vision ◽  
Defect Detection ◽  
Surface Defects ◽  
Vision System ◽  
Rapid Development ◽  
Training Model ◽  
Support Vector ◽  
Ideal Condition ◽  
Product Packaging

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

AUTOMATED CLASSIFICATION OF BREAST CARCINOMA CELL BASED ON IMAGE PROCESSING AND SUPPORT VECTOR MACHINE

2010 ◽  
Vol 22 (02) ◽  
pp. 127-135 ◽  
Author(s):  
Yung-Lung Kuo ◽  
Chien-Chuan Ko ◽  
Yueh-Min Lin ◽  
Yong-Min Chen
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Cancer Diagnosis ◽  
Support Vector ◽  
Automated Classification ◽  
Processing Technologies ◽  
Major Health ◽  
Suitable Treatment ◽  
Breast Tissues

As breast cancer is a substantial threat to the lives of women, it has become a major health issue in the world over the past 50 years, and its incidence has increased in the recent years. Early diagnosis and suitable treatment is relatively important. In the process of breast screening, tissue biopsy is an important operation in determining the presence of breast cancer. It not only provides an accurate diagnosis of the disease but also determines the prognosis for breast cancer. The main goal of this study is to develop a breast cancer diagnosis system based on histopathology and a sequence of image-processing technologies to analyze H&E stained images of breast tissues. The proposed system can automatically detect the mitosis of nuclei and analyze the size and the shape of nuclei to evaluate the duct structure of the breast tissue. Moreover, it provides physicians quantitative prognosis and classification of tissue malignancy, which will improve the diagnostic accuracy and efficiency of the cancer.

Detection and Classification of Watermelon Seeds Exterior Quality Based on LS-SVM Using Machine Vision

2013 ◽  
Vol 278-280 ◽  
pp. 727-730
Author(s):  
Xiai Chen ◽  
Shuang Ke ◽  
Ling Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Machine Vision ◽  
Vision System ◽  
Least Square ◽  
Support Vector ◽  
Machine Vision System ◽  
High Quality ◽  
Surface Irregularities

A machine vision system was developed to investigate the detection of watermelon seeds exterior quality. The main characteristics of watermelon seeds appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least square support vector machine optimized by genetic algorithm was applied for the classification of watermelon seeds exterior quality, and the broken seeds, normal seeds and high-quality seeds were distinguished finally. The surface irregularities defects of watermelon seeds were detected by machine vision grid laser. The experimental results show that the watermelon seeds exterior quality could be well detected and classified by machine vision based on least squares support vector machine.

scholarly journals Algorithm to Image Identification and Artix-7 FPGA Implementation for Adequate Edge Detection.

2020 ◽  
Vol 8 (5) ◽  
pp. 1656-1660
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Low Contrast ◽  
Image Identification ◽  
Canny Edge ◽  
Set Up ◽  
Canny Edge Detection Algorithm

For any image identification based applications, edge detection is the primary step. The intention of the edge detection in image processing is to minimize the information that is not required in the analysis of identification of an image. In the process of reduction of insignificant data in the image, it may lead to some loss in information which in turn raise some problems like missing of boundaries with low contrast, false edge detection and some other noise affected problems. In order to reduce the effects due to noise, a modified version of popular edge detection algorithm “Canny edge detection algorithm” is proposed. Artix 7 FPGA board set up is used to implement, by using Xilinx platform the image that is obtained as output is displayed on monitor which is connected with FPGA board using connector port DVI. MATLAB Simulink is used for algorithm simulation and then it is executed on FPGA board using Xilinx platform. The results provide good motivation to use in different edge detection applications.

scholarly journals Lemon Leaf Disease Detection and Classification using SVM and CNN

2019 ◽  
Vol 8 (4) ◽  
pp. 11485-11488
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Color Space ◽  
Input Image ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Support Vector ◽  
Training Time ◽  
Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

scholarly journals Blended Features Classification of Leaf-Based Cucumber Disease Using Image Processing Techniques

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jaweria Kainat ◽  
Syed Sajid Ullah ◽  
Fahd S. Alharithi ◽  
Roobaea Alroobaea ◽  
Saddam Hussain ◽  
...  
Keyword(s):  
Image Processing ◽  
Feature Fusion ◽  
Principal Component ◽  
Features Extraction ◽  
Support Vector ◽  
Features Fusion ◽  
Extraction Step ◽  
Processing Techniques ◽  
Feature Score

Existing plant leaf disease detection approaches are based on features of extracting algorithms. These algorithms have some limits in feature selection for the diseased portion, but they can be used in conjunction with other image processing methods. Diseases of a plant can be classified from their symptoms. We proposed a cucumber leaf recognition approach, consisting of five steps: preprocessing, normalization, features extraction, features fusion, and classification. Otsu’s thresholding is implemented in preprocessing and Tan–Triggs normalization is applied for normalizing the dataset. During the features extraction step, texture and shape features are extracted. In addition, increasing the instances improves some characteristics. Through a principal component analysis approach, serial feature fusion is employed to provide a feature score. Fused features can be classified through a support vector machine. The accuracy of the Fine KNN is 94.30%, which is higher than the previous work in past papers.

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