scholarly journals The histogram of partitioned localized image textures

2017 ◽  
Author(s):  
◽  
Eric B. Brewster
Keyword(s):  
Object Detection ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Directional Pattern ◽  
Bag Of Words ◽  
Data Set ◽  
Pixel Location ◽  
Local Direction ◽  
Robustness To Noise

In the field of machine learning and pattern recognition, texture has been a prominent area of research. Humans are uniquely equipped to distinguish texture; however, computers are more equipped to automate the process. Computers accomplish this by taking images and extracting meaningful features that describe their texture. Some of these features are the Haralick texture features, local binary pattern (LBP), and the local direction pattern (LDP). Using the local directional pattern as an example, we propose a new texture feature called the histogram of partitioned localized image textures (HoPLIT). This feature utilizes a set of filters, not necessarily directional, and generates filter response vectors at every pixel location. These response vectors can be thought of as words in a document, which causes one to think of the bag-of-words model. Using the bag-of-words model, a codebook is created by partitioning a subset of response vectors from the entire data set. The partitions are represented by their mean texture and thus a word in the codebook. The mean textures now represent the keywords within the document, i.e. image. A histogram descriptor for an image is the frequency of pixels that belong to each partition. This feature is applied to a texture classification and segmentation problem as well as object detection. Within each problem domain, the HoPLIT feature is compared to the Haralick texture features, LBP, and LDP. The HoPLIT feature does very well classifying texture as well as segmenting large texture mosaics. HoPLIT also shows a surprising robustness to noise. Object detection proves to be slightly more difficult than texture classification for HoPLIT. However, it continues to outperform LBP and LDP.

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.  

scholarly journals ROLE OF MACHINE VISION FOR IDENTIFICATION OF KIDNEY STONES USING MULTI FEATURES ANALYSIS

2021 ◽  
Vol 5 (3) ◽  
pp. 1-14
Author(s):  
Salman Qadri
Keyword(s):  
Random Forest ◽  
Machine Vision ◽  
Optimization Technique ◽  
Texture Feature ◽  
Region Of Interest ◽  
Texture Features ◽  
Feature Space ◽  
Data Set ◽  
Feature Optimization ◽  
Frame Work

The purpose of this study is to highlight the significance of machine vision for the Classification of kidney stone identification. A novel optimized fused texture features frame work was designed to identify the stones in kidney.  A fused 234 texture feature namely (GLCM, RLM and Histogram) feature set was acquired by each region of interest (ROI). It was observed that on each image 8 ROI’s of sizes (16x16, 20x20 and 22x22) were taken. It was difficult to handle a large feature space 280800 (1200x234). Now to overcome this data handling issue we have applied feature optimization technique namely POE+ACC and acquired 30 most optimized features set for each ROI. The optimized fused features data set 3600(1200x30) was used to four machine vision Classifiers that is Random Forest, MLP, j48 and Naïve Bayes. Finally, it was observed that Random Forest provides best results of 90% accuracy on ROI 22x22 among the above discussed deployed Classifiers

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.

Texture Segmentation by Genetic Programming

2008 ◽  
Vol 16 (4) ◽  
pp. 461-481 ◽  
Author(s):  
Andy Song ◽  
Vic Ciesielski
Keyword(s):  
Feature Extraction ◽  
Genetic Programming ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Texture Segmentation ◽  
Segmentation Method ◽  
Texture Feature Extraction ◽  
Powerful Approach ◽  
Computation Algorithms

This paper describes a texture segmentation method using genetic programming (GP), which is one of the most powerful evolutionary computation algorithms. By choosing an appropriate representation texture, classifiers can be evolved without computing texture features. Due to the absence of time-consuming feature extraction, the evolved classifiers enable the development of the proposed texture segmentation algorithm. This GP based method can achieve a segmentation speed that is significantly higher than that of conventional methods. This method does not require a human expert to manually construct models for texture feature extraction. In an analysis of the evolved classifiers, it can be seen that these GP classifiers are not arbitrary. Certain textural regularities are captured by these classifiers to discriminate different textures. GP has been shown in this study as a feasible and a powerful approach for texture classification and segmentation, which are generally considered as complex vision tasks.

scholarly journals A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

2019 ◽  
Vol 9 (11) ◽  
pp. 2173 ◽  
Author(s):  
Mingwei Wang ◽  
Lang Gao ◽  
Xiaohui Huang ◽  
Ying Jiang ◽  
Xianjun Gao
Keyword(s):  
Gabor Filter ◽  
Texture Classification ◽  
Texture Feature ◽  
Texture Features ◽  
Feature Subset ◽  
Optimal Parameters ◽  
Practical Applications ◽  
Integrated Optimization ◽  
Ant Lion Optimizer ◽  
Ant Lion

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

scholarly journals Texture Feature Extraction of Lumbar Spine Trabecular Bone Radiograph Image using Laplacian of Gaussian Filter with KNN Classification to Diagnose Osteoporosis

2021 ◽  
Vol 2070 (1) ◽  
pp. 012137
Author(s):  
Kavita Avinash Patil ◽  
K V Mahendra Prashanth ◽  
A Ramalingaiah
Keyword(s):  
Lumbar Spine ◽  
Trabecular Bone ◽  
Bone Fractures ◽  
Directional Derivative ◽  
Texture Feature ◽  
Texture Features ◽  
Training Data ◽  
Gaussian Filter ◽  
Data Set ◽  
Laplacian Of Gaussian Filter

Abstract The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy whereas the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. The detection of Osteoporosis in Lumbar Spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. The paper is systematized in two different sections to classify normal (non-osteoporosis) and abnormal(osteoporosis)Lumbar spine trabecular bone. In this method, the first section is based on discriminating the lumbar spine trabecular bone micro-architecture predisposing by means of first and second order directional derivative of Laplacian of Gaussian filter with different standard deviation to acquire the minimum and maximum responses. The dimension reduction of texture features, quantization and adjacent scale coding with weighted multipliers are used to lessen the intensity variations of texture features. The second section is based on the reduction of histogram features as a training data set for classification of normal and osteoporotic images of lumbar spine (L1-L4) using K-Nearest Neighborhood (KNN) classifier. The tested dataset result gives effective classification accuracy of 97.22% with lesser texture feature dimension. The usage of weight multiplier as well as quantization technique plays a major role for the improvement of accuracy to diagnose osteoporosis for an input noisy and noiseless image.

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 Background subtraction for night videos

2021 ◽  
Vol 7 ◽  
pp. e592
Author(s):  
Hongpeng Pan ◽  
Guofeng Zhu ◽  
Chengbin Peng ◽  
Qing Xiao
Keyword(s):  
Object Detection ◽  
Background Subtraction ◽  
Texture Feature ◽  
Large Data ◽  
Surveillance Systems ◽  
Light Detection ◽  
Data Set ◽  
Reflected Light ◽  
Enhancement Method ◽  
Detection Unit

Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.

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.

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