scholarly journals Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1527
Author(s):  
Xi Pan ◽  
Kang Li ◽  
Zhangjing Chen ◽  
Zhong Yang
Keyword(s):  
Near Infrared ◽  
Spatial Clustering ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Image Features ◽  
Identification Accuracy ◽  
Support Vector ◽  
Gray Level ◽  
Wood Identification

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features.

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.

scholarly journals A Hyperspectral Imaging Approach for Classifying Geographical Origins of Rhizoma Atractylodis Macrocephalae Using the Fusion of Spectrum-Image in VNIR and SWIR Ranges (VNIR-SWIR-FuSI)

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2045 ◽  
Author(s):  
Chenlei Ru ◽  
Zhenhao Li ◽  
Renzhong Tang
Keyword(s):  
Data Fusion ◽  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Image Features ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Projection Algorithm ◽  
Support Vector ◽  
Gray Level ◽  
Biomedical Analysis

Hyperspectral data processing technique has gained increasing interests in the field of chemical and biomedical analysis. However, appropriate approaches to fusing features of hyperspectral data-cube are still lacking. In this paper, a new data fusion approach was proposed and applied to discriminate Rhizoma Atractylodis Macrocephalae (RAM) slices from different geographical origins using hyperspectral imaging. Spectral and image features were extracted from hyperspectral data in visible and near-infrared (VNIR, 435–1042 nm) and short-wave infrared (SWIR, 898–1751 nm) ranges, respectively. Effective wavelengths were extracted from pre-processed spectral data by successive projection algorithm (SPA). Meanwhile, gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) were employed to extract textural variables. The fusion of spectrum-image in VNIR and SWIR ranges (VNIR-SWIR-FuSI) was implemented to integrate those features on three fusion dimensions, i.e., VNIR and SWIR fusion, spectrum and image fusion, and all data fusion. Based on data fusion, partial least squares-discriminant analysis (PLS-DA) and support vector machine (SVM) were utilized to establish calibration models. The results demonstrated that VNIR-SWIR-FuSI could achieve the best accuracies on both full bands (97.3%) and SPA bands (93.2%). In particular, VNIR-SWIR-FuSI on SPA bands achieved a classification accuracy of 93.2% with only 23 bands, which was significantly better than those based on spectra (80.9%) or images (79.7%). Thus it is more rapid and possible for industry applications. The current study demonstrated that hyperspectral imaging technique with data fusion holds the potential for rapid and nondestructive sorting of traditional Chinese medicines (TCMs).

scholarly journals Pedestrian count estimation using texture feature with spatial distribution

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668359 ◽  
Author(s):  
Hongyu Hu ◽  
Zhenhai Gao ◽  
Yiteng Sun
Keyword(s):  
Spatial Distribution ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Experimental Result ◽  
Support Vector ◽  
Global Features ◽  
Multi Scale ◽  
Global Representation ◽  
Count Estimation

We present a novel pedestrian count estimation approach based on global image descriptors formed from multi-scale texture features that considers spatial distribution. For regions of interest, local texture features are represented based on histograms of multi-scale block local binary pattern, which jointly constitute the feature vector of the whole image. Therefore, to achieve an effective estimation of pedestrian count, principal component analysis is used to reduce the dimension of the global representation features, and a fitting model between image global features and pedestrian count is constructed via support vector regression. The experimental result shows that the proposed method exhibits high accuracy on pedestrian count estimation and can be applied well in the real world.

Performance of a semi-automatic machine leaning method for discriminating HER2 2+ status of breast cancers based on DCE-MRI (Preprint)

2019 ◽  
Author(s):  
Lirong Song ◽  
Zejun Jiang ◽  
Hecheng Lu ◽  
Jiandong Yin
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Low Cost ◽  
Principal Component ◽  
Texture Features ◽  
Classification Performance ◽  
Image Features ◽  
Support Vector ◽  
Breast Cancers ◽  
Dce Mri

BACKGROUND Amplification status of human epidermal growth factor receptor2 (HER2) 2+ is currently tested by fluorescence in situ hybridization (FISH). However, the FISH technique is expensive, time consuming, and requires off-site testing. The requirement for alternative low-cost and accurate surrogate measures to formal genetic analysis is urgent. In addition, machine learning is broadly accepted for its ability to decipher complicated connections between medical image features and gene expression status. OBJECTIVE To investigate the potential association between texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and HER2 2+ amplification status of breast cancer. METHODS 92 patients with HER2 2+ breast cancer who underwent 3T MRI and FISH detection in 2018 were retrospectively selected, including 52 HER2 2+ positive and 40 negative cases. The lesion area was delineated semi-automatically with MATLAB, and a total of 307 texture features were extracted from precontrast, postcontrast, and subtraction images, independently. The Student’s t-test or Mann-Whitney U test was performed to identify significant features between different HER2 2+ amplification status. Principal component analysis was used to eliminate the feature correlations. Three machine learning classifiers, logistic regression analysis, quadratic discriminant analysis, and support vector machine (SVM), were with a leave-one-outcross validation method used to establish the classification models of HER2 2+ amplification status. Classification performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS Texture features calculated from subtraction images showed more promising results than those obtained from pre- and postcontrast images. The model with the SVM based on features from subtraction image achieved the best performance, with an area under the ROC curve of 0.890, sensitivity of 80.77%, specificity of 85.00%, and accuracy of 82.61%. CONCLUSIONS To a certain extent, texture features of breast cancer extracted from DCE-MRI are associated with HER2 2+ amplification status. Additional studies are necessary to confirm the present preliminary findings.

scholarly journals Apple Variety Identification Using Near-Infrared Spectroscopy

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Caihong Li ◽  
Lingling Li ◽  
Yuan Wu ◽  
Min Lu ◽  
Yi Yang ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Identification Accuracy ◽  
Variety Identification ◽  
Support Vector ◽  
Successive Projections Algorithm ◽  
Apple Variety ◽  
Pattern Recognition Methods ◽  
Learning Machine

Near-infrared (NIR) spectra of apple samples were submitted in this paper to principal component analysis (PCA) and successive projections algorithm (SPA) to conduct variable selection. Three pattern recognition methods, backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM), were applied to establish models for distinguishing apples of different varieties and geographical origins. Experimental results show that ELM models performed better on identifying apple variety and geographical origin than others. Especially, the SPA-ELM model could reach 98.33% identification accuracy on the calibration set and 96.67% on the prediction set. This study suggests that it is feasible to identify apple variety and cultivation region by using NIR spectroscopy.

scholarly journals Quadratic Support Vector Machine For The Bomba Traditional Textile Motif Classification

2018 ◽  
Vol 11 (3) ◽  
pp. 1004
Author(s):  
Nuraedah Nuraedah ◽  
Muhammad Bakri ◽  
Anita Ahmad Kasim
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Human Life ◽  
Texture Feature ◽  
Texture Features ◽  
Classification Model ◽  
Support Vector ◽  
Gray Level ◽  
Textural Feature ◽  
Textile Fabrics

<p><em>The Bomba textile is one of the textile fabrics in Indonesia used in a province called Sulawesi Tengah. Bomba Textile has a unique pattern and has a philosophical meaning in human life in Sulawesi Tengah. Bomba Textile has many motif patterns and varied colors. The problem in this research is the difficulty in classifying every The Bomba textile motif in each class. Data classification is needed to recognize the motif of each Bomba textile pattern and to cluster it into the appropriate class. The features used to classify the Bomba textile motif is the textural feature. Texture features obtained from Gray-Level Co-occurrence matrices (GLCM) method consisting of energy, contrast, homogeneity and correlation with four angles 0</em><em>°</em><em>, 45</em><em>°</em><em>, 90</em><em>°</em><em>, and 135</em><em>°</em><em>. This research will implement Quadratic Vector Machine (QSVM) method with texture feature on Bomba textile pattern. The use of a single texture feature with angles 90</em><em>°</em><em> has an accuracy of 90.3%. The incorporation of texture features by involving all features at all angles can improve the accuracy of the classification model. This research produces a model of motif classification on the Bomba textile which has the classification accuracy of 94.6% and error rate of 5.4%.</em></p>

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

scholarly journals Upgraded Spatial Gray Level Dependence Matrices for Textural Analysis in Colon Cancer Tissues

2018 ◽  
Vol 7 (2.20) ◽  
pp. 291 ◽  
Author(s):  
B Saroja ◽  
A Selwin Mich Priyadharson
Keyword(s):  
Colon Cancer ◽  
Texture Feature ◽  
Image Data ◽  
Texture Features ◽  
Gray Level ◽  
Colon Tissue ◽  
Data Set ◽  
Histopathological Image ◽  
Cancer Biopsy ◽  
Cancer Tissues

Colon or Bowel or Colorectal Cancer (CRC) is commonly determined by diagnosing a sample of colon tissue and further analysed by medical imaging. The colon tissue classification method count on specific changes between texture features extracted from benign and malignant regions. The variations in the image acquisition methods effects the colon tissue analysis. In this paper, an Upgraded Spatial Gray Level Dependence Matrices (U-SGLDM) is emphasized to extract textural features. The licensed image set of all applicable types of tissues within colon cancer are used for experimentation. Several texture feature sets are extracted to show the significant differences among the eight colon cancer biopsy images in the image data set. The fractal dimension-Hurst Coefficient is added to U-SGLDM for long range assessment. The Prominence of the analysis evoked in the representation of histopathological image structure over longer periods.  

Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

2021 ◽  
pp. 6787-6794
Author(s):  
Anisha Rebinth, Dr. S. Mohan Kumar
Keyword(s):  
Principal Component Analysis ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

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