scholarly journals Identification method of cashmere and wool based on texture features of GLCM and Gabor

2021 ◽  
Vol 16 ◽  
pp. 155892502198917
Author(s):  
Yaolin Zhu ◽  
Jiayi Huang ◽  
Tong Wu ◽  
Xueqin Ren
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Texture Feature ◽  
Identification Accuracy ◽  
Gabor Wavelet ◽  
Gray Level ◽  
Gabor Wavelet Transform ◽  
Second Moment ◽  
Feature Vectors ◽  
Occurrence Matrix

The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

Study of Gabor Features and Heart Sound Signal Recognition by the Principal Component Analysis

2014 ◽  
Vol 644-650 ◽  
pp. 4452-4454 ◽  
Author(s):  
Le Juan Zhang ◽  
Lu Zhang ◽  
Zhi Ming LI ◽  
Shi Yao Cui
Keyword(s):  
Wavelet Transform ◽  
Frequency Domain ◽  
Visual Information ◽  
Principal Component ◽  
Tight Frame ◽  
Gabor Wavelet ◽  
Gabor Wavelet Transform ◽  
Stimulus Response ◽  
Gabor Features ◽  
Local Space

Simple cells Gabor wavelet transform and human visual system in the visual stimulus response very similar. It has the good characteristics of the local space in the extraction of target and frequency domain information. Although the Gabor wavelet does not of itself constitute orthogonal basis, but in the specific parameters can form a tight frame. Gabor wavelet is sensitive to the image edge, can provide good direction and scale selection characteristics, but also insensitive to illumination changes, can provide the illumination change good adaptability. These features make Gabor wavelet is widely used in visual information understanding. The two-dimensional Gabor wavelet transform is an important tool for signal analysis and processing in frequency domain in, the coefficient of wavelet transform with the visual characteristics and good biology background, so it is widely used in image processing, pattern recognition and other fields.

A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm

2013 ◽  
Vol 811 ◽  
pp. 430-434
Author(s):  
Hai Feng Wang ◽  
Kun Zhang ◽  
Hong E Ren
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transformation ◽  
Texture Feature ◽  
Near Neighbor ◽  
Classification Algorithm ◽  
Gabor Wavelet ◽  
Accuracy Rate ◽  
Gabor Wavelet Transform ◽  
Texture Image Classification ◽  
Gabor Wavelet Transformation

In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

Aluminum Profile Type Recognition Based on Texture Features

2014 ◽  
Vol 556-562 ◽  
pp. 2846-2851 ◽  
Author(s):  
Zhen Hua Liu ◽  
Hai Bo Shi ◽  
Xiao Feng Zhou
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Recognition Accuracy ◽  
Detection Efficiency ◽  
Texture Feature ◽  
Gabor Wavelet ◽  
Image Texture ◽  
Support Vector ◽  
Gabor Wavelet Transform ◽  
Aluminum Profile

Because aluminum profile’s structure is complex and diverse, we need to match the different parameters for different profiles before automated detection of surface defects of aluminum profile. This process often requires manual input, affecting the detection efficiency. To solve this problem, we analyze the characteristics of aluminum profile, through GLCM algorithm and Gabor wavelet transform methods, which are image texture feature extraction methods to get aluminum profile’s texture feature, then we use the Support Vector Machine (SVM) classification algorithm based on radial basis function (RBF) core classify the feature, for the aim of matching parameters automatically. By feature extraction time and the recognition accuracy rate and other indicators to compare the experimental results of each method, derived using Gabor wavelet transform is the best both on recognition accuracy or identify time effects, and can satisfy the actual needs.

Down Syndrome Diagnosis Based on Gabor Wavelet Transform

2011 ◽  
Vol 36 (5) ◽  
pp. 3205-3213 ◽  
Author(s):  
Şafak Saraydemir ◽  
Necmi Taşpınar ◽  
Osman Eroğul ◽  
Hülya Kayserili ◽  
Nuriye Dinçkan
Keyword(s):  
Down Syndrome ◽  
Wavelet Transform ◽  
Gabor Wavelet ◽  
Gabor Wavelet Transform ◽  
Syndrome Diagnosis

scholarly journals Ekstraksi Ciri Sel Darah Putih Berdasarkan Gray Level Co-occurence Matrix

2020 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Anwar Siswanto ◽  
Abdul Fadlil ◽  
Anton Yudhana
Keyword(s):  
Gray Level ◽  
Second Moment ◽  
Occurrence Matrix

Dalam tubuh manusia terkandung darah yang terdiri dari komponen selular dan non selulardimana salah satu komponen selular adalah sel darah putih. Darah didistribusikan melalui pembuluh darah dari jantung ke seluruh tubuh dan kembali lagi menuju jantung. Sistem ini berfungsi untuk memenuhi kebutuhan sel atau jaringan akan nutrien dan oksigen serta mentranspor sisa metabolisme sel atau jaringan keluar dari tubuh. Dalam berbagai penegakan diagnosis penyakit, sel darah putih merupakan indikator yang dibutuhkan. Pengenalan secara manual membutuhkan waktu yang lama dan cenderung subjektif tergantung dari pengalaman petugas. Sel darah putih diketahui dengan pemeriksaan Sediaan Apus Darah Tepi (SADT) dengan pewarnaan My Grundwald. Penelitian ini bertujuan untuk membantu pengenalan sel darah putih secara otomatis sehingga didapatkan hasil yang cepat dan akurat. Sel darah putih terdiri dari Eosinofil, Basofil, Neutrofil, Limfosit dan Monosit.Penelitian ini menggunakan citra dari apusan darah tepi menggunakan mikroskop digital. Sistem pengenalan sel darah putih ini berdasarkan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) yaitu menggunakan fitur Contrast, Anguler Second Moment (ASM) serta Inverse Difference Moment (IDM) dan Correlation. Klasifikasi dengan menggunakan K-means Clustering dihasilkan plot berbeda-beda dan terlihat beberapa ciri yang mirip sesuai jenis sel darah putih. 

scholarly journals Color Laser Printer Identification through Discrete Wavelet Transform and Gray Level Co-occurrence Matrix

2010 ◽  
Vol 17B (3) ◽  
pp. 197-206 ◽  
Author(s):  
Ji-Yeoun Baek ◽  
Heung-Su Lee ◽  
Seung-Gyu Kong ◽  
Jung-Ho Choi ◽  
Yeon-Mo Yang ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Laser Printer ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Occurrence Matrix

NIMG-56. USING RADIOMIC FEATURES FROM DAILY MAGNETIC RESONANCE IMAGING TO PREDICT RESPONSE TO RADIATION THERAPY IN GLIOBLASTOMA PATIENTS: A PILOT STUDY

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi142-vi142
Author(s):  
Kaylie Cullison ◽  
Garrett Simpson ◽  
Danilo Maziero ◽  
Kolton Jones ◽  
Radka Stoyanova ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Tumor Growth ◽  
Texture Feature ◽  
Texture Features ◽  
Gray Level ◽  
Difference Matrix ◽  
Patient Response ◽  
Significant Difference ◽  
Occurrence Matrix ◽  
Surrounding Areas

Abstract A dilemma in treating glioblastoma is that MRI after chemotherapy and radiation therapy (chemoRT) shows areas of presumed tumor growth in up to 50% of patients. These areas can represent true progression (TP), tumor growth with tumors non-responsive to treatment, or pseudoprogression (PP), edema and tumor necrosis with favorable treatment response. On imaging, TP and PP are usually not discernable. Patients in this study undergo six weeks of chemoRT on a combination MRI/RT device, receiving daily MRIs. The goal of this study is to explore the correlation of radiomics features with progression. The tumor lesion and surrounding areas of growth/edema were manually outlined as regions of interest (ROIs) for each daily T2-weighted MRI scan. The ROIs were used to calculate texture features: statistical features based on the gray-level co-occurrence matrix (GLCM), the gray-level zone size matrix (GLZSM), the gray-level run length matrix (GLRLM), and the neighborhood gray-tone difference matrix (NGTDM). Each of these matrix classes describe the probability of spatial relationships of gray levels occurring within the ROI. Daily texture features were averaged per week of treatment for each patient. Patient response was retrospectively defined as no progression (NP), TP, or PP. A Kruskal-Wallis test was performed to identify texture features that correlated most strongly with patient response. Forty texture features were calculated for 12 patients (19 treated, 7 excluded due to no T2 lesion or progression status unknown, 6 NP, 3 TP, 3 PP). There was a trend of more texture features correlating significantly with response in weeks 4-6 of treatment, compared to weeks 1-3. A particular texture feature, GLSZM Small Zone Low Gray-Level Emphasis, showed increasing difference between PP and TP over time, with significant difference during week 6 of treatment (p=0.0495). Future directions include correlating early outcomes with greater numbers of patients and daily multiparametric MRI.

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