An Identification Method for Cracked Eggs Based on Image Wavelet Transform and Multi-Features Synthesis

2014 ◽  
Vol 704 ◽  
pp. 412-418
Author(s):  
Li Rong Xiong ◽  
Zhi Hui Zhu
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
High Frequency ◽  
Vision System ◽  
Joint Probability ◽  
Principal Component ◽  
Ccd Camera ◽  
Classification Model ◽  
Support Vector ◽  
Identification Method

An identification method for cracked eggs by means of the digital image technology was proposed in this paper. Firstly, an ideal machine vision system was built and the images of good eggs and cracked eggs were obtained by CCD camera. Secondly, each image was decomposed on two layers of wavelet, so 6 high-frequency sub-images and 2 low-frequency sub-images were extracted. Then joint probability matrix after wavelet transform had been calculated and 5 parameters for each high-frequency sub-images were extracted, so the total of the joint probability matrix parameters was 30 for 6 high-frequency sub-images. At the same time, 10 wavelet energy parameters were obtained. Thirdly, four main factor component scores were selected from above 40 feature parameters after principal component analysis, which were input to support vector machine. Finally, classification model was built by support vector machine. Experiments show that the proposed method was effective to identify the cracked eggs from good eggs and the identification rate was 93.75%.

scholarly journals Experimental investigation and multi-conditions identification method of centrifugal pump using Fisher discriminant ratio and support vector machine

2019 ◽  
Vol 11 (9) ◽  
pp. 168781401987804
Author(s):  
Guangqi Qiu ◽  
Si Huang ◽  
Yingkui Gu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Domain Analysis ◽  
Test System ◽  
Support Vector ◽  
Centrifugal Pumps ◽  
Flow Conditions ◽  
Identification Method ◽  
Fisher Discriminant ◽  
Time Flow

For identifying the operation situations of centrifugal pumps by artificial intelligence, we performed an experiment on multi-flow conditions. The multi-flow conditions were simulated by adjusting an automatic flow-regulating valve installed on outlet pipe, and the vertical vibration signals of 20 flow points at the bearing house were collected by the test system. By time-domain analysis, frequency-domain analysis, information entropy, empirical modal decomposition, and wavelet packet decomposition methods, a comprehensive feature space was constructed. In addition, the optimal features were selected by Fisher discriminant ratio, and the dimensionality of the selected optimal features was reduced with principal component analysis. Finally, support vector machine algorithm was employed to identify the real-time flow condition, and the hyper-parameters of support vector machine classifier model were optimized by a grid search technique. Results show that the vibration test can effectively simulate the operation situation of centrifugal pumps under multi-flow conditions, and the proposed multi-flow conditions identification method has achieved a good identification performance.

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

scholarly journals Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

2021 ◽  
Author(s):  
SANTI BEHERA ◽  
PRABIRA SETHY
Keyword(s):  
Support Vector Machine ◽  
Skin Cancer ◽  
Principal Component ◽  
Skin Lesions ◽  
Classification Model ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Feature ◽  
Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

Classification of different kinds of pesticide residues on lettuce based on fluorescence spectra and WT–BCC–SVM algorithm

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740082 ◽  
Author(s):  
Xin Zhou ◽  
Sun Jun ◽  
Bing Zhang ◽  
Wu Jun
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Pesticide Residues ◽  
Fluorescence Spectra ◽  
Classification Model ◽  
Support Vector ◽  
Diagnostic Model ◽  
Bacterial Colony ◽  
Svm Algorithm ◽  
Spectrum Feature

In order to improve the reliability of the spectrum feature extracted by wavelet transform, a method combining wavelet transform (WT) with bacterial colony chemotaxis algorithm and support vector machine (BCC–SVM) algorithm (WT–BCC–SVM) was proposed in this paper. Besides, we aimed to identify different kinds of pesticide residues on lettuce leaves in a novel and rapid non-destructive way by using fluorescence spectra technology. The fluorescence spectral data of 150 lettuce leaf samples of five different kinds of pesticide residues on the surface of lettuce were obtained using Cary Eclipse fluorescence spectrometer. Standard normalized variable detrending (SNV detrending), Savitzky–Golay coupled with Standard normalized variable detrending (SG–SNV detrending) were used to preprocess the raw spectra, respectively. Bacterial colony chemotaxis combined with support vector machine (BCC–SVM) and support vector machine (SVM) classification models were established based on full spectra (FS) and wavelet transform characteristics (WTC), respectively. Moreover, WTC were selected by WT. The results showed that the accuracy of training set, calibration set and the prediction set of the best optimal classification model (SG–SNV detrending-WT–BCC–SVM) were 100%, 98% and 93.33%, respectively. In addition, the results indicated that it was feasible to use WT–BCC–SVM to establish diagnostic model of different kinds of pesticide residues on lettuce leaves.

scholarly journals An Efficient Brain Stroke Image Classification Model Based on Artificial Bee Colony Optimization with Kernel Support Vector Machine

2019 ◽  
Vol 8 (11) ◽  
pp. 1274-1284
Keyword(s):  
Support Vector Machine ◽  
Artificial Bee Colony ◽  
Principal Component ◽  
Feature Space ◽  
Classification Model ◽  
Support Vector ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Kernel Support Vector Machine ◽  
Mr Brain

In present decade, identification of abnormalities in brain gains significant attention for medical diagnosis. Though numerous existing models are available, only a few methods have been proposed which classifies a set of different kinds of brain defects. This paper introduces an efficient hybridization model for classifying the provided MR brain image as normal or abnormal. The presented model initially makes use of digital wavelet transform (DWT) for extracting features and utilizes principal component analysis (PCA) for feature space reduction. Next, a kernel support vector machine (KSVM) with radial basis function (RBF) kernel is built by artificial bee colony (ABC) for optimizing the parameters namely C and σ. For experimentation, 5-fold cross validation procedure is involved and a detailed investigation of the results takes place by comparing it with the existing models. To select the parameters, ABC algorithm has undergone a comparison with the random selection approach. The presented model is tested using a benchmark MR brain dataset. The experimental values indicated that the ABC is highly efficient for constructing optimal KSVM.

scholarly journals Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Yansong Diao ◽  
Xue Men ◽  
Zuofeng Sun ◽  
Kongzheng Guo ◽  
Yumei Wang
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Damage Identification ◽  
Wavelet Packet ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Detection Accuracy ◽  
Wavelet Packet Decomposition ◽  
Identification Method

A novel damage identification method based on transmissibility function and support vector machine is proposed and outlined in this paper. Basically, the transmissibility function is calculated with the acceleration responses from damaged structure. Then two damage features, namely, wavelet packet energy vector and the low order principal components, are constructed by analyzing the amplitude of the transmissibility function with wavelet packet decomposition and principal component analysis separately. Finally, the classification algorithm and regression algorithm of support vector machine are employed to identify the damage location and damage severity respectively. The numerical simulation and shaking table model test of an offshore platform under white noise excitation are conducted to verify the proposed damage identification method. The results show that the proposed method does not need the information of excitation and the data from undamaged structure, needs only small size samples, and has certain antinoise ability. The detection accuracy of the proposed method with damage feature constructed by principal component analysis is superior to that constructed by wavelet packet decomposition.

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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