scholarly journals An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Hai Guo ◽  
Jinghua Yin ◽  
Jingying Zhao ◽  
Yuanyuan Liu ◽  
Lei Yao ◽  
...  
Keyword(s):  
Nanocomposite Film ◽  
Detection Method ◽  
Principal Component ◽  
Automatic Detection ◽  
Experimental Result ◽  
Support Vector ◽  
Gray Level ◽  
Detection Model ◽  
Different Types ◽  
Film Element

An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements.

Selected Shape and Texture Features for Automatic Detection of Acute Lymphoblastic Leukemia

2018 ◽  
pp. 162-183
Author(s):  
Vanika Singhal ◽  
Preety Singh
Keyword(s):  
Acute Lymphoblastic Leukemia ◽  
Lymphoblastic Leukemia ◽  
Principal Component ◽  
Texture Features ◽  
Automatic Detection ◽  
Support Vector ◽  
Shape Features ◽  
Geometrical Properties ◽  
Correlation Based Feature Selection ◽  
Human Interpretation

Acute Lymphoblastic Leukemia is a cancer of blood caused due to increase in number of immature lymphocyte cells. Detection is done manually by skilled pathologists which is time consuming and depends on the skills of the pathologist. The authors propose a methodology for discrimination of a normal lymphocyte cell from a malignant one by processing the blood sample image. Automatic detection process will reduce the diagnosis time and not be limited by human interpretation. The lymphocyte images are classified based on two types of extracted features: shape and texture. To identify prominent shape features, Correlation based Feature Selection is applied. Principal Component Analysis is applied on the texture features to reduce their dimensionality. Support Vector Machine is used for classification. It is observed that 16 shape features are able to give a classification accuracy of 92.3% and that changes in the geometrical properties of the nucleus emerge as significant features contributing towards detecting a malignant lymphocyte.

An Object Detection Method Based on Independent Local Features

2006 ◽  
Vol 18 (6) ◽  
pp. 744-750
Author(s):  
Ryouta Nakano ◽  
◽  
Kazuhiro Hotta ◽  
Haruhisa Takahashi
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Local Features ◽  
Local Feature ◽  
Superior Performance ◽  
Support Vector ◽  
Car Detection ◽  
Training Samples

This paper presents an object detection method using independent local feature extractor. Since objects are composed of a combination of characteristic parts, a good object detector could be developed if local parts specialized for a detection target are derived automatically from training samples. To do this, we use Independent Component Analysis (ICA) which decomposes a signal into independent elementary signals. We then used the basis vectors derived by ICA as independent local feature extractors specialized for a detection target. These feature extractors are applied to a candidate area, and their outputs are used in classification. However, the number of dimension of extracted independent local features is very high. To reduce the extracted independent local features efficiently, we use Higher-order Local AutoCorrelation (HLAC) features to extract the information that relates neighboring features. This may be more effective for object detection than simple independent local features. To classify detection targets and non-targets, we use a Support Vector Machine (SVM). The proposed method is applied to a car detection problem. Superior performance is obtained by comparison with Principal Component Analysis (PCA).

Automatic Detection of Diabetic Retinopathy with Statistical Method and Bayesian Classifier

2020 ◽  
Vol 10 (5) ◽  
pp. 1225-1233 ◽  
Author(s):  
Yafen Kang ◽  
Ying Fang ◽  
Xiaobo Lai
Keyword(s):  
Diabetic Retinopathy ◽  
Statistical Method ◽  
Texture Features ◽  
Automatic Detection ◽  
Bayesian Classifier ◽  
Gray Level ◽  
Data Set ◽  
Naive Bayesian ◽  
Detection Model ◽  
Naïve Bayesian

Currently, the underlying medical conditions in China lag behind those in urban areas. There are some problems such as lack of resources of primary ophthalmologists and insufficient fundus image of diabetic retinopathy (DR) with markers. To solve the above questions, an automated detection model of diabetic retinopathy based on the statistical method and Naïve Bayesian (NB) classifier is proposed in this paper. Firstly, three sets of texture features are extracted, which are gray-level co-occurrence matrix texture features, different statistical texture features, and gray-level run-length matrix texture features. Secondly, the extracted texture features are used as input of the Naïve Bayesian classifier to classify the fundus images of diabetic retinopathy into three categories. The proposed automatic detection model for diabetic retinopathy is validated by a data set consisting of 568 images from China diabetic retinopathy screening project. The positive predictive accuracy of the system is 93.44%, the sensitivity and specificity are 91.94% and 88.24%, respectively.

Identification of the Seaweed Fluorescence Spectroscopy Based on the PCA and ICA-SVM

2012 ◽  
Vol 182-183 ◽  
pp. 1958-1961
Author(s):  
Jiang Tao Lv ◽  
Qiong Chan Gu
Keyword(s):  
Fluorescence Spectrum ◽  
Three Dimensional ◽  
Characteristic Root ◽  
Principal Component ◽  
Matrix Decomposition ◽  
Component Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Fluorescent Light ◽  
Fast Method

Current years, the offing red tide of china is recurrent mutation, the direct and fast method which can analyze the amount and the kind of the phytoplankton is needed imperious. Three-dimensional fluorescence spectrum can show entire fingerprint information of fluorescent light that in the range of excitation and emission wavelength, but the dimension of three-dimensional fluorescence spectrum is higher, the characteristic spectrum of different kinds pelagic plant are multifarious, it is complex identification. In this paper, the principal component analysis (PCA) is used to reduce the dimensions of the spectroscopy. The independent component analysis (ICA) is used to do the matrix decomposition from the perspective of independence to extract the main feature of the spectroscopy data processed by the PCA. The support vector machine (SVM) is used to assort the main characteristic root books which are abstracted by the ICA. The correct laboratory sorting of seaweed is realized. Experimental result indicate, this method can identify the chief component of admixture seaweed, the high dimensional spectroscopy information of seaweed is proceed effective feature extraction, the sorting speed is increase greatly, the discrimination of sorting is reach 92% percent.

scholarly journals Detection of Fusarium Head Blight in Wheat Ears Using Continuous Wavelet Analysis and PSO-SVM

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 998
Author(s):  
Linsheng Huang ◽  
Kang Wu ◽  
Wenjiang Huang ◽  
Yingying Dong ◽  
Huiqin Ma ◽  
...  
Keyword(s):  
Wavelet Analysis ◽  
Fusarium Head Blight ◽  
Detection Method ◽  
Support Vector ◽  
Spectral Features ◽  
Continuous Wavelet ◽  
Head Blight ◽  
Detection Model ◽  
Svm Algorithm ◽  
Wavelet Features

Fusarium head blight, caused by a fungus, can cause quality deterioration and severe yield loss in wheat. It produces highly toxic deoxynivalenol, which is harmful to human and animal health. In order to quickly and accurately detect the severity of fusarium head blight, a method of detecting the disease using continuous wavelet analysis and particle swarm optimization support vector machines (PSO-SVM) is proposed in this paper. First, seven wavelet features for fusarium head blight detection were extracted using continuous wavelet analysis based on the hyperspectral reflectance of wheat ears. In addition, 16 traditional spectral features were selected using correlation analysis, including two continuous removal transformed spectral features, six differential spectral features, and eight vegetation indices. Finally, wavelet features and traditional spectral features were used as input features to construct fusarium head blight detection models in combination with the PSO-SVM algorithm, and the results were compared with those obtained using random forest (RF) and a back propagation neural network (BPNN). The results show that, under the same feature variables, the PSO-SVM detection method gave an overall higher accuracy than the BPNN detection method, while the overall accuracy of the RF detection model was the lowest. The overall accuracy of the RF, BPNN and PSO-SVM detection models with wavelet features was higher by 3.7%, 2.9% and 8.3% compared to the corresponding methodological models with traditional spectral features. The detection model with wavelet features combining the PSO-SVM algorithm gave the highest overall accuracies (93.5%) and kappa coefficients (0.903) in the six monitoring models. These results suggest that the PSO-SVM algorithm combined with continuous wavelet analysis can significantly improve the accuracy of fusarium head blight detection on the wheat ears scale.

scholarly journals Weed recognition by SVM texture feature classification in outdoor vegetable crops images

2017 ◽  
Vol 37 (1) ◽  
pp. 68 ◽  
Author(s):  
Camilo Pulido Rojas ◽  
Leonardo Solaque Guzmán ◽  
Nelson Velasco Toledo
Keyword(s):  
Scale Parameter ◽  
Texture Feature ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Gray Level ◽  
Vegetable Crops ◽  
Nonlinear Case ◽  
Classifier Performance ◽  
Weed Recognition

This paper presents a classification system for weeds and vegetables from outdoor crop images. The classifier is based on support vector machine (SVM) with its extension to nonlinear case using radial basis function (RBF) and optimizing its scale parameter σ to smooth the decision boundary. The feature space is the result of principal component analysis (PCA) for 10 texture measurements calculated from gray level co-occurrence matrices (GLCM). The results indicate that classifier performance is above 90%, validated with specificity, sensitivity and precision calculations.

Edge Detection Combined Local Gray Level Difference and Competitive Fuzzy Logic

2012 ◽  
Vol 220-223 ◽  
pp. 1284-1287
Author(s):  
Tong Tong ◽  
Yan Cai ◽  
Da Wei Sun
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Experimental Result ◽  
Gray Level ◽  
Human Visual Perception ◽  
Edge Image ◽  
New Approach ◽  
Level Difference ◽  
Edge Detection Method ◽  
Noisy Condition

In this paper, we present a new approach by local gray level difference based competitive fuzzy edge detection. In the light of human visual perception, a preprocessing step is proposed to simplify original images and further enhance the performance of edge extraction. Then we define the feature vector of each pixel in four directions and six edge prototype. Finally, BP neural network is used to classify the type of edge, and the competitive rule is adopted to thin the thick edge image. From the experimental result, it can be seen that the edge detection method proposed in this paper is superior to Canny method and Log method under the noisy condition.

A Principal Component Analysis Based Fault Detection Method in Etch Process of Semiconductor Manufacturing

2012 ◽  
Vol 522 ◽  
pp. 793-798 ◽  
Author(s):  
Jun Gang Yang ◽  
Jie Zhang ◽  
Jian Xiong Yang ◽  
Ying Huang
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Semiconductor Manufacturing ◽  
Detection Method ◽  
Main Idea ◽  
Simple Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Training Data ◽  
Detection Model

A Principal Component Analysis based Fault Detection method is proposed here to detect faults in etch process of semiconductor manufacturing. The main idea of this method is to calculate the loading vector and build the fault detection model according to training data. Using this model, the main information of fault data can be obtained immediately and easily. Also the principal component subspace and residual subspace can be constructed. Then, faults are detected by calculating Squared Prediction Error. Finally, an industrial example of Lam 9600 TCP metal etcher at Texas Instruments is used to demonstrate the performance of the proposed PCA-based method in fault detection, and the results show that it has such advantages as simple algorithm and low time cost, thus especially adapts to the real time fault detection of semiconductor manufacturing.

A Novel Estimation Method of Fatigue Using EEG Based on KPCA-SVM and Complexity Parameters

2013 ◽  
Vol 373-375 ◽  
pp. 965-969 ◽  
Author(s):  
Yi Jun Xiong ◽  
Rong Zhang ◽  
Chong Zhang ◽  
Xiao Lin Yu
Keyword(s):  
Estimation Method ◽  
Principal Component ◽  
Approximate Entropy ◽  
Mental Fatigue ◽  
Kernel Principal Component Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Classification Models ◽  
Eeg Data ◽  
Nonlinear Features

In this study, Kolmogorov complexity (KC) and approximate Entropy (AE) were adopted to characterize the irregularity and complexity of EEG data. Fifty subjects were instructed to perform two different mental tasks to induce two kinds of fatigue states. Then the Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) are combined to differentiate these two states. The KPCA was used to extract nonlinear features from the complexity parameters of EEG and to effectively reduce the dimensionality of the feature vectors. SVM was used to classify two fatigue states. The experimental result shows that complexity parameters are significantly decreased as the fatigue level increases, which suggests that the proposed parameters can be used to characterize mental fatigue level. Furthermore, compared with several typical classification models, the joint method KPCA-SVM can achieve higher classification accuracy (85%) of mental fatigue with less training and classifying time, which indicates that KPCA-SVM is suitable for the estimation of mental fatigue.

scholarly journals Data Mining to Identify Anomalies in Public Procurement Rating Parameters

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2873
Author(s):  
Yeferson Torres-Berru ◽  
Vivian F. López Batista
Keyword(s):  
Supervised Learning ◽  
Public Procurement ◽  
Principal Component ◽  
Support Vector ◽  
Self Organizing Map ◽  
Detection Model ◽  
Process Qualification ◽  
Multi Phase ◽  
Direct Contracting

The awarding of public procurement processes is one of the main causes of corruption in governments, due to the fact that in many cases, contracts are awarded to previously agreed suppliers (favouritism); for this selection, the qualification parameters of a process play a fundamental role, seeing as due to their manipulation, bidders with high prices win, causing prejudice to the state. This study identifies processes with anomalies and generates a model for detecting possible corruption in the assignment of process qualification parameters in public procurement. A multi-phase model was used (the identification of anomalies and generation of the detection model), which uses different algorithms, such as clustering (K-Means), Self-Organizing map (SOM), Support Vector Machine (SVM) and Principal Component Analysis (PCA). SOM was used to determine the level of influence of each rating parameter, K-Means to create groups by clustering, semi-supervised learning with SVM and PCA to generate a model to detect anomalies in the processes. By means of a case study, four groups of processes were obtained, highlighting the presence of the group “null economic offer” where the values for the economic offer do not exceed 1%, and a greater weight is given to other qualification parameters, which include direct contracting. The processes in this cluster are considered anomalous. Following this methodology, a semi-supervised learning model is built for the detection of anomalies, which obtains an accuracy of 95%, allowing the detection of procedures where the aim is to benefit a particular supplier by means of the qualification assignment parameters.

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