An Adaptive Support Vector Machine-Based Workpiece Surface Classification System Using High-Definition Metrology

2015 ◽  
Vol 64 (10) ◽  
pp. 2590-2604 ◽  
Author(s):  
Shi-Chang Du ◽  
De-Lin Huang ◽  
Hui Wang
Keyword(s):  
Support Vector Machine ◽  
Classification System ◽  
Support Vector ◽  
High Definition ◽  
Workpiece Surface ◽  
Adaptive Support ◽  
Surface Classification

A Selective Multiclass Support Vector Machine Ensemble Classifier for Engineering Surface Classification Using High Definition Metrology

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
Shichang Du ◽  
Changping Liu ◽  
Lifeng Xi
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Manufacturing Process ◽  
Classification Accuracy ◽  
Support Vector ◽  
Ensemble Classifiers ◽  
Complex Wavelet Transform ◽  
High Definition ◽  
Workpiece Surface ◽  
Surface Patterns

The surface appearance is sensitive to change in the manufacturing process and is one of the most important product quality characteristics. The classification of workpiece surface patterns is critical for quality control, because it can provide feedback on the manufacturing process. In this study, a novel classification approach for engineering surfaces is proposed by combining dual-tree complex wavelet transform (DT-CWT) and selective ensemble classifiers called modified matching pursuit optimization with multiclass support vector machines ensemble (MPO-SVME), which adopts support vector machine (SVM) as basic classifiers. The dual-tree wavelet transform is used to decompose three-dimensional (3D) workpiece surfaces, and the features of workpiece surface are extracted from wavelet sub-bands of each level. Then MPO-SVME is developed to classify different workpiece surfaces based on the extracted features and the performance of the proposed approach is evaluated by computing its classification accuracy. The performance of MPO-SVME is validated in case study, and the results demonstrate that MPO-SVME can increase the classification accuracy with only a handful of selected classifiers.

scholarly journals Breast Microcalcification Detection Algorithm Based on Contourlet and ASVM

Algorithms ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 135
Author(s):  
Cai ◽  
Liu ◽  
Luo ◽  
Du ◽  
Tang
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Early Breast Cancer ◽  
Detection Algorithm ◽  
Support Vector ◽  
Important Landmark ◽  
Professional Doctor ◽  
Breast Microcalcification ◽  
Adaptive Support ◽  
Microcalcification Detection

Microcalcification is the most important landmark information for early breast cancer. At present, morphological artificial observation is the main method for clinical diagnosis of such diseases, but it is easy to cause misdiagnosis and missed diagnosis. The present study proposes an algorithm for detecting microcalcification on mammography for early breast cancer. Firstly, the contrast characteristics of mammograms are enhanced by Contourlet transformation and morphology (CTM). Secondly, split the ROI by the improved K-means algorithm. Thirdly, calculate grayscale feature, shape feature, and Histogram of Oriented Gradient (HOG) for the ROI region. The Adaptive support vector machine (ASVM) is used as a tool to classify the rough calcification point and the false calcification point. Under the guidance of a professional doctor, 280 normal images and 120 calcification images were selected for experimentation, of which 210 normal images and 90 images with calcification images were used for training classification. The remaining 100 are used to test the algorithm. It is found that the accuracy of the automatic classification results of the Adaptive support vector machine (ASVM) algorithm reaches 94%, and the experimental results are superior to similar algorithms. The algorithm overcomes various difficulties in microcalcification detection and has great clinical application value.

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