scholarly journals Clifford Algebra and Gabor filter for color image texture characterization

2013 ◽  
Author(s):  
A.T. Sanda ◽  
E.C. Ezin ◽  
P. Gouton ◽  
J. Tossa
Keyword(s):  
Clifford Algebra ◽  
Color Image ◽  
Gabor Filter ◽  
Image Texture ◽  
Texture Characterization

An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses

2021 ◽  
pp. 016173462199809
Author(s):  
Dhurgham Al-karawi ◽  
Hisham Al-Assam ◽  
Hongbo Du ◽  
Ahmad Sayasneh ◽  
Chiara Landolfo ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Empirical Evaluation ◽  
Texture Features ◽  
Image Texture ◽  
Support Vector ◽  
Simple Majority ◽  
Ultrasound Scan ◽  
Histograms Of Oriented Gradients ◽  
The Individual ◽  
Ovarian Masses

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k ( k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

scholarly journals Variable Selection from Image Texture Feature for Automatic Classification of Concrete Surface Voids

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ziting Zhao ◽  
Tong Liu ◽  
Xudong Zhao
Keyword(s):  
Variable Selection ◽  
Gabor Filter ◽  
Texture Feature ◽  
Concrete Surface ◽  
Automatic Classification ◽  
Ensemble Classification ◽  
Image Texture ◽  
Inspection Time ◽  
Selection Strategy

Machine learning plays an important role in computational intelligence and has been widely used in many engineering fields. Surface voids or bugholes frequently appearing on concrete surface after the casting process make the corresponding manual inspection time consuming, costly, labor intensive, and inconsistent. In order to make a better inspection of the concrete surface, automatic classification of concrete bugholes is needed. In this paper, a variable selection strategy is proposed for pursuing feature interpretability, together with an automatic ensemble classification designed for getting a better accuracy of the bughole classification. A texture feature deriving from the Gabor filter and gray-level run lengths is extracted in concrete surface images. Interpretable variables, which are also the components of the feature, are selected according to a presented cumulative voting strategy. An ensemble classifier with its base classifier automatically assigned is provided to detect whether a surface void exists in an image or not. Experimental results on 1000 image samples indicate the effectiveness of our method with a comparable prediction accuracy and model explicable.

scholarly journals A cluster-based feature selection method for image texture classification

2019 ◽  
Vol 14 (3) ◽  
pp. 1433 ◽  
Author(s):  
Abbas F. H. Alharan ◽  
Hayder K. Fatlawi ◽  
Nabeel Salih Ali
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Clustering Algorithm ◽  
Gabor Filter ◽  
Texture Feature ◽  
Image Feature ◽  
Image Texture ◽  
Support Vector ◽  
Texture Image ◽  
Texture Feature Extraction

<p>Computer vision and pattern recognition applications have been counted serious research trends in engineering technology and scientific research content. These applications such as texture image analysis and its texture feature extraction. Several studies have been done to obtain accurate results in image feature extraction and classifications, but most of the extraction and classification studies have some shortcomings. Thus, it is substantial to amend the accuracy of the classification via minify the dimension of feature sets. In this paper, presents a cluster-based feature selection approach to adopt more discriminative subset texture features based on three different texture image datasets. Multi-step are conducted to implement the proposed approach. These steps involve texture feature extraction via Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP) and Gabor filter. The second step is feature selection by using K-means clustering algorithm based on five feature evaluation metrics which are infogain, Gain ratio, oneR, ReliefF, and symmetric. Finally, K-Nearest Neighbor (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers are used to evaluate the proposed classification performance and accuracy. Research achieved better classification accuracy and performance using KNN and NB classifiers that were 99.9554% for Kelberg dataset and 99.0625% for SVM in Brodatz-1 and Brodatz-2 datasets consecutively. Conduct a comparison to other studies to give a unified view of the quality of the results and identify the future research directions.</p>

Tongue Image Texture Segmentation Based on Gabor Filter Plus Normalized Cut

2010 ◽  
pp. 115-125 ◽  
Author(s):  
Jianfeng Li ◽  
Jinhuan Shi ◽  
Hongzhi Zhang ◽  
Yanlai Li ◽  
Naimin Li ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Texture Segmentation ◽  
Image Texture ◽  
Normalized Cut

scholarly journals A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6429
Author(s):  
Lotfi Tlig ◽  
Moez Bouchouicha ◽  
Mohamed Tlig ◽  
Mounir Sayadi ◽  
Eric Moreau
Keyword(s):  
Forest Fire ◽  
Color Image ◽  
Gabor Filter ◽  
Human Life ◽  
Principal Component ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Full Account ◽  
Native Flora ◽  
Novel Approach

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

scholarly journals An improved pix2pix model based on Gabor filter for robust color image rendering

2021 ◽  
Vol 19 (1) ◽  
pp. 86-101
Author(s):  
Hong-an Li ◽  
◽  
Min Zhang ◽  
Zhenhua Yu ◽  
Zhanli Li ◽  
...  
Keyword(s):  
Color Image ◽  
Gabor Filter ◽  
Texture Feature ◽  
Generative Adversarial Networks ◽  
Feature Maps ◽  
Penalty Term ◽  
Multi Scale ◽  
Adversarial Networks ◽  
Robust Image ◽  
Color Rendering

<abstract><p>In recent years, with the development of deep learning, image color rendering method has become a research hotspot once again. To overcome the detail problems of color overstepping and boundary blurring in the robust image color rendering method, as well as the problems of unstable training based on generative adversarial networks, we propose an color rendering method using Gabor filter based improved pix2pix for robust image. Firstly, the multi-direction/multi-scale selection characteristic of Gabor filter is used to preprocess the image to be rendered, which can retain the detailed features of the image while preprocessing to avoid the loss of features. Moreover, among the Gabor texture feature maps with 6 scales and 4 directions, the texture map with the scale of 7 and the direction of 0° has the comparable rendering performance. Finally, by improving the loss function of pix2pix model and adding the penalty term, not only the training can be stabilized, but also the ideal color image can be obtained. To reflect image color rendering quality of different models more objectively, PSNR and SSIM indexes are adopted to evaluate the rendered images. The experimental results of the proposed method show that the robust image rendered by this method has better visual performance and reduces the influence of light and noise on the image to a certain extent.</p></abstract>

BACKPROPAGATION NEURAL NETWORK DESIGN AND EVALUATION FOR CLASSIFYING WEED SPECIES USING COLOR IMAGE TEXTURE

2000 ◽  
Vol 43 (4) ◽  
pp. 1029-1037 ◽  
Author(s):  
T. F. Burks ◽  
S. A. Shearer ◽  
R. S. Gates ◽  
K. D. Donohue
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Color Image ◽  
Image Texture ◽  
Backpropagation Neural Network ◽  
Weed Species

Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks

Meat Science ◽  
2014 ◽  
Vol 96 (2) ◽  
pp. 837-842 ◽  
Author(s):  
X. Sun ◽  
K.J. Chen ◽  
E.P. Berg ◽  
D.J. Newman ◽  
C.A. Schwartz ◽  
...  
Keyword(s):  
Color Image ◽  
Troponin T ◽  
Texture Features ◽  
Image Texture
Sign in / Sign up

Export Citation Format

Share Document