TEXTURE ANALYSIS USING LOCAL TEXTURE PATTERNS: A FUZZY LOGIC APPROACH

2011 ◽  
Vol 25 (05) ◽  
pp. 741-762 ◽  
Author(s):  
E. M. SRINIVASAN ◽  
K. RAMAR ◽  
A. SURULIANDI
Keyword(s):  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Vital Role ◽  
Image Texture ◽  
Feature Extraction Method ◽  
Local Texture ◽  
Texture Model ◽  
Fuzzy Logic Approach

Texture analysis plays a vital role in image processing. The prospect of texture based image analysis depends on the texture features and the texture model. This paper presents a new texture feature extraction method 'Fuzzy Local Texture Patterns (FLTP)' and 'Fuzzy Pattern Spectrum (FPS)', suitable for texture analysis. The local image texture is described by FLTP and the global image texture is described by FPS. The proposed method is tested with texture classification, texture segmentation and texture edge detection. The results show that the proposed method provides a very good and robust performance for texture analysis.

ON TEXTURE CLASSIFICATION USING FRACTAL DIMENSION

1999 ◽  
Vol 13 (06) ◽  
pp. 929-943 ◽  
Author(s):  
YAN QIU CHEN ◽  
GUOAN BI
Keyword(s):  
Fractal Dimension ◽  
Texture Analysis ◽  
Texture Classification ◽  
Texture Feature ◽  
Experimental Results ◽  
Image Texture ◽  
Feature Sets ◽  
Mathematical Properties ◽  
Underlying Causes

The fractal dimension has been studied as a feature for texture analysis. It has been found that the fractal dimension is not an effective image texture measure but little is known about the reasons for the fractal dimension failing to be effective for texture analysis. This paper investigates into the underlying causes why the fractal dimension is not an effective image texture feature. Four mathematical properties have been identified which are responsible for the fractal dimension's ineffectiveness. The experimental results show that while the fractal dimension itself is hardly an effective feature for texture classification, it can considerably enhance other feature sets.

COMPUTING IMAGE TEXTURE BY ISOPATTERN

2014 ◽  
Vol 28 (05) ◽  
pp. 1454003
Author(s):  
S. MOHAMED MANSOOR ROOMI ◽  
R. RAJA ◽  
D. KALAIYARASI
Keyword(s):  
Texture Analysis ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Weighting Function ◽  
Texture Classification ◽  
Texture Feature ◽  
Region Of Interest ◽  
Image Texture ◽  
Textural Feature ◽  
Bit Plane

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.

scholarly journals UNSUPERVISED TEXTURE CLASSIFICATION OF ENTROPY BASED LOCAL DESCRIPTOR USING K-MEANS CLUSTERING ALGORITHM

2011 ◽  
pp. 133-140 ◽  
Author(s):  
S. S. Sreeja Mole ◽  
L. Ganesan
Keyword(s):  
Texture Analysis ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Texture Classification ◽  
Clustering Algorithms ◽  
Texture Feature ◽  
Texture Features ◽  
Local Descriptor ◽  
Descriptor Approach

This paper presents an efficient approach for unsupervised Texture Segmentation and Classification, based on features extracted from entropy based local descriptor using K-means clustering with spatial information. The K- means clustering algorithm is commonly used in computer vision as a form of image segmentation. Texture analysis refers to a class of mathematical procedures and models that characterizes the spatial variations within imagery as a means of extracting information. Texture analysis may require the solution of two different problems first is Segmentation and Classification of a given image according to the different texture and second was for of a given texture with respect to a set of known textures. Based on the proposed concept, this paper describes the entropy based local descriptor using K-Means with spatial information approach. Experimental results show that the proposed framework performs very well compared to other clustering algorithms in all measured criteria. Spatial information has been effectively used for unsupervised texture classification for Brodatz of texture images. The model is not specifically confined to a particular texture feature. We tested this algorithm using other texture features. The proposed entropy based local descriptor approach gives good accuracy when compared with other methods.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

Land Cover Classification Using the Proposed Texture Model and Fuzzy k-NN Classifier

2018 ◽  
pp. 226-261
Author(s):  
Jenicka S.
Keyword(s):  
Nearest Neighbor ◽  
Confusion Matrix ◽  
Texture Feature ◽  
Image Texture ◽  
Kappa Statistics ◽  
Grey Level ◽  
Classification Problems ◽  
K Nearest Neighbor ◽  
Texture Model ◽  
Proposed Model

Texture feature is a decisive factor in pattern classification problems because texture features are not deduced from the intensity of current pixel but from the grey level intensity variations of current pixel with its neighbors. In this chapter, a new texture model called multivariate binary threshold pattern (MBTP) has been proposed with five discrete levels such as -9, -1, 0, 1, and 9 characterizing the grey level intensity variations of the center pixel with its neighbors in the local neighborhood of each band in a multispectral image. Texture-based classification has been performed with the proposed model using fuzzy k-nearest neighbor (fuzzy k-NN) algorithm on IRS-P6, LISS-IV data, and the results have been evaluated based on confusion matrix, classification accuracy, and Kappa statistics. From the experiments, it is found that the proposed model outperforms other chosen existing texture models.

A fabric defect detection algorithm via context-based local texture saliency analysis

2015 ◽  
Vol 27 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Quanjun Zhao ◽  
Liang Liao ◽  
Yan Dong
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Texture ◽  
Content Type ◽  
Local Texture ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Saliency Analysis

Purpose – Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach – In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings – The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value – In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

Adaptive Texture Feature Extraction with Application to Ultrasonic Image Analysis

1998 ◽  
Vol 20 (2) ◽  
pp. 132-148 ◽  
Author(s):  
H.J. Huisman ◽  
J.M. Thijssen
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Texture Features ◽  
Small Data ◽  
Data Sets ◽  
Large Set ◽  
Texture Feature Extraction ◽  
Feed Forward Neural Network ◽  
Feature Extraction Method ◽  
Optimal Feature

Computer texture analysis methods use texture features that are traditionally chosen from a large set of fixed features known in literature. These fixed features are often not specifically designed to the problem at hand, and as a result they may have low discriminative power, and/or may be correlated. Increasing the number of selected fixed features is statistically not a good solution in limited data environments such as medical imaging. For that reason, we developed an adaptive texture feature extraction method (ATFE) that extracts a small number of features that are tuned to the problem at hand. By using a feed-forward neural network, we ensure that even nonlinear relations are captured from the data. Using extensive, repeated synthetic ultrasonic images, we compared the performance of ATFE with the optimal feature set. We show that the ATFE method is capable of robust operation on small data sets with a performance close to that of the optimal feature set. Another experiment confirms that our ATFE is capable of capturing nonlinear relations from the dataset. We conclude that our method can improve performance in practical, limited dataset situations where an optimal fixed feature set can be hard to find.

Ultrasound Image Texture Analysis for Characterizing Intramuscular Fat Content of Live Beef Cattle

1998 ◽  
Vol 20 (3) ◽  
pp. 191-205 ◽  
Author(s):  
Nam-Deuk Kim ◽  
Viren Amin ◽  
Doyle Wilson ◽  
Gene Rouse ◽  
Satish Udpa
Keyword(s):  
Beef Cattle ◽  
Texture Analysis ◽  
Prediction Models ◽  
Ultrasound Image ◽  
Texture Features ◽  
Intramuscular Fat ◽  
Image Texture ◽  
Edge Density ◽  
Fat Percentage ◽  
Image Texture Analysis

The primary factors in determining beef quality grades are the amount and distribution of intramuscular fat percentage (IMFAT). Texture analysis was applied to ultrasound B-mode images from ribeye muscle of live beef cattle to predict its IMFAT. We used wavelet transform (WT) for multiresolutional texture analysis and second-order statistics using a gray-level co-occurrence matrix (GLCM) technique. Sets of WT-and GLCM-based texture features were calculated from ultrasonic images from 207 animals and linear regression methods were used for IMFAT prediction. WT-based features included energy ratios, central moments of wavelet-decomposed subimages and wavelet edge density. The regression model using WT features provided a root mean square error (RMSE) of 1.44 for prediction of IMFAT using validation images, while that of GLCM features provided an RMSE of 1.90. The prediction models using the WT features showed potential for objective quality evaluation in the live animals.

Relationships of Image Texture Properties with Chewing Activity and Mechanical Properties during Mastication of Bread

2016 ◽  
Vol 12 (4) ◽  
pp. 311-321
Author(s):  
Qian Mao ◽  
Yonghai Sun ◽  
Jumin Hou ◽  
Libo Yu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Texture Analysis ◽  
Texture Features ◽  
Image Texture ◽  
Emg Activity ◽  
Analysis Method ◽  
Texture Properties ◽  
Property Changes ◽  
Occurrence Matrix ◽  
Chewing Activity

Abstract The objective of this study was to investigate the relationships of image texture properties with chewing behaviors, and mechanical properties during mastication of bread. Gray-level gradient co-occurrence matrix (GGCM) was used to process the images of boluses. The chewing behaviors were recorded by electromyography (EMG), and the mechanical properties were measured by texture analyzer. The results showed that among the texture features, the inverse difference moment (IDMGGCM) was selected as the main parameter to describe the decomposition of boluses. IDMGGCM was positively related to the weight gain (r = 0.865, p < 0.01), negatively correlated with hardness (r = –0.835, p <0.01) and EMG activity per cycle (r = –0.767, p < 0.01). GGCM is an effective texture analysis method that could correctly identify 70.1–80.8 % of food bolus images to the corresponding chewing cycles. This study provided a new clue for texture analysis of bread bolus images and offered data revealing the bolus property changes during the mastication of bread.

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