A Novel Feature Extraction Method for Mechanical Part Recognition

2011 ◽  
Vol 88-89 ◽  
pp. 116-121
Author(s):  
Su Qun Cao ◽  
Ge Lan Yang ◽  
Quan Yin Zhu ◽  
Hai Fei Zhai
Keyword(s):  
Feature Extraction ◽  
Image Features ◽  
Image Feature ◽  
Fisher Criterion ◽  
Feature Extraction Method ◽  
Automated Production ◽  
Mechanical Parts ◽  
Mechanical Part ◽  
Extraction Algorithm ◽  
Time Requirements

Mechanical part recognition has great significance in automated sorting and processing. The core of mechanical part recognition is the part image feature extraction. Thus, how to extract part features to meet the real time requirements for the automated production line has a crucial role. A novel feature extraction algorithm is presented for part image features in this paper. It aims to optimize the fuzzy Fisher criterion function to figure out two orthogonal optimal discriminant vectors in an unsupervised way. Based on these two vectors, the linear transformation from d-dimension to 2-dimension can be obtained. Experimental results on three mechanical parts show its effectiveness.

A modified welding image feature extraction algorithm for rotating arc narrow gap MAG welding

2015 ◽  
Vol 42 (3) ◽  
pp. 222-227 ◽  
Author(s):  
Wenhang Li ◽  
Yunhong Ji ◽  
Jing Wu ◽  
Jiayou Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Image Feature ◽  
Mag Welding ◽  
Narrow Gap ◽  
Image Feature Extraction ◽  
Content Type ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
Welding Image

Purpose The purpose of this paper is to provide a modified welding image feature extraction algorithm for rotating arc narrow gap metal active-gas welding (MAG) welding, which is significant for improving the accuracy and reliability of the welding process. Design/methodology/approach An infrared charge-coupled device (CCD) camera was utilized to obtain the welding image by passive vision. The left/right arc position was used as a triggering signal to capture the image when the arc is approaching left/right sidewall. Comparing with the conventional method, the authors’ sidewall detection method reduces the interference from arc; the median filter removes the welding spatter; and the size of the arc area was verified to reduce the reflection from welding pool. In addition, the frame loss was also considered in the authors’ method. Findings The modified welding image feature extraction method improves the accuracy and reliability of sidewall edge and arc position detection. Practical implications The algorithm can be applied to welding seam tracking and penetration control in rotating or swing arc narrow gap welding. Originality/value The modified welding image feature extraction method is robust to typical interference and, thus, can improve the accuracy and reliability of the detection of sidewall edge and arc position.

scholarly journals Feature Extraction for Medical CT Images of Sports Tear Injury

2020 ◽  
Author(s):  
Qi Nie ◽  
Ye-bing Zou ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Feature Extraction ◽  
Auxiliary Information ◽  
Low Frequency ◽  
Frequency Component ◽  
Ct Images ◽  
Image Features ◽  
Image Feature ◽  
Ct Image ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Abstract Analysis of medical CT images directly affects the accuracy of clinical case diagnosis. Therefore, feature extraction problem of medical CT images is extremely important. A feature extraction algorithm for medical CT images of sports tear injury is proposed. First, CT images are decomposed into a low frequency component and a series of high frequency components in different directions by wavelet fast decomposition method. The high- and low-frequency information of CT images is enhanced by wavelet layered multi-directional image enhancement algorithm, and the multi-scale enhancement for medical CT images of sports tear injury is completed. Then, edge of the enhanced CT images is extracted using an image edge extraction algorithm based on extended mathematical morphology. Finally, based on the extracted edge information of CT images, feature extraction for medical CT images of sports tear injury is completed by the NSCT-GLCM based CT image feature extraction algorithm. Research results show that the proposed algorithm effectively extracts CT image features of sports tear injury and provides auxiliary information for doctor diagnosis.

The Image Feature Extraction Algorithm Based on the DWT and the Improved 2DPCA

2014 ◽  
Vol 556-562 ◽  
pp. 5042-5045 ◽  
Author(s):  
Wu Li
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Target Recognition ◽  
Image Feature ◽  
Image Feature Extraction ◽  
Two Dimension ◽  
Feature Extraction Method ◽  
Extraction Algorithm ◽  
New Feature ◽  
Pca Algorithm

The technology of 2DPCA is the feature extraction method proposed aiming at two-dimension image based on the traditional PCA algorithm. The paper proposed a improved weighting 2DPCA algorithm, combined with the two-dimension discrete DWT to handle the image, posing the new feature abstraction method, experiment improved that the new feature abstraction method can improve the target recognition efficiently compared with the original 2DPCA algorithm.

Tactile image computation using a feature extraction algorithm

Robotica ◽  
1992 ◽  
Vol 10 (3) ◽  
pp. 241-254
Author(s):  
M. Mehdian
Keyword(s):  
Feature Extraction ◽  
Local Property ◽  
Recognition System ◽  
Image Feature ◽  
Image Size ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm ◽  
Tactile Image ◽  
Feature Values ◽  
Image Computation

SUMMARYA binary tactile image feature extraction algorithm using image primitive notation and perceptrons is presented. The basic image segments are defined as geometric factors by which the image structure is described so that effective feature values such as image shape, image size, perimeter and texture may be extracted on the basis of local image computation. The local property of the tactile image computation is evaluated by the concept called order of the perceptrons and based on this feature extraction algorithm, an efficient tactile image recognition system is realised.

Application Research of Face Feature Extraction Based on DM642

2010 ◽  
Vol 139-141 ◽  
pp. 2024-2028
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan ◽  
Dong Cao
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Application Research ◽  
Feature Extraction Method ◽  
Modified Method ◽  
Face Images ◽  
Safety Awareness ◽  
Time Requirements ◽  
Hardware Platforms ◽  
Ica Algorithm

With the safety awareness strengthened, identification authentication technology has been increasingly concerned. Face recognition is attractive in pattern recognition and artificial intelligence field, and face feature extraction is a very important part in face recognition. This paper first introduced preprocessing of face images, PCA and ICA algorithm. Considering PCA and ICA their respective strengths and weaknesses, then a novel face feature extraction method based on PCA and ICA is proposed. The NN classifier is select to face classification and recognition on the ORL face database. From the actual requirements, the paper analyses hardware platforms based on DM642, and finally use tool CCS software to optimize program and implementation base on DM642 to meet the real-time requirements. Experiments indicated that the modified method is superior to PCA and ICA algorithm.

scholarly journals Adaptive Principal Component Analysis Combined with Feature Extraction-Based Method for Feature Identification in Manufacturing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Tsun-Kuo Lin
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
High Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Image Features ◽  
Image Feature ◽  
Support Vector ◽  
Feature Identification ◽  
Adaptive Pca

This paper developed a principal component analysis (PCA)-integrated algorithm for feature identification in manufacturing; this algorithm is based on an adaptive PCA-based scheme for identifying image features in vision-based inspection. PCA is a commonly used statistical method for pattern recognition tasks, but an effective PCA-based approach for identifying suitable image features in manufacturing has yet to be developed. Unsuitable image features tend to yield poor results when used in conventional visual inspections. Furthermore, research has revealed that the use of unsuitable or redundant features might influence the performance of object detection. To address these problems, the adaptive PCA-based algorithm developed in this study entails the identification of suitable image features using a support vector machine (SVM) model for inspecting of various object images; this approach can be used for solving the inherent problem of detection that occurs when the extraction contains challenging image features in manufacturing processes. The results of experiments indicated that the proposed algorithm can successfully be used to adaptively select appropriate image features. The algorithm combines image feature extraction and PCA/SVM classification to detect patterns in manufacturing. The algorithm was determined to achieve high-performance detection and to outperform the existing methods.

scholarly journals Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhangjing Yang ◽  
Chuancai Liu ◽  
Pu Huang ◽  
Jianjun Qian
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
High Dimensional ◽  
Membership Degree ◽  
Extraction Techniques ◽  
Fisher Criterion ◽  
Extraction Algorithm ◽  
Definition Of

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzyk-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm.

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