Reflection invariant local binary patterns for image texture classification

2015 ◽  
Author(s):  
Enmin Song ◽  
Ning Pan ◽  
Chih-Cheng Hung ◽  
Xiang Li ◽  
Lianghai Jin
Keyword(s):  
Texture Classification ◽  
Local Binary Patterns ◽  
Image Texture

scholarly journals Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review

2020 ◽  
Vol 170 ◽  
pp. 03007
Author(s):  
Aparna Goyal ◽  
Reena Gunjan
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Local Binary Patterns ◽  
Image Texture ◽  
Discriminative Power ◽  
Computer Image Analysis ◽  
Bleeding Detection ◽  
Segmentation Of Images ◽  
Effective Description ◽  
Lacunarity Analysis

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

A new algorithm for remotely sensed image texture classification and segmentation

2004 ◽  
Vol 25 (19) ◽  
pp. 4043-4050 ◽  
Author(s):  
Yao-Wei Wang ◽  
Yan-Fei Wang ◽  
Yong Xue ◽  
Wen Gao
Keyword(s):  
Texture Classification ◽  
Remotely Sensed ◽  
Image Texture ◽  
Remotely Sensed Image

Image Texture Classification Based on Wavelet Transform and SVM

2008 ◽  
Author(s):  
Lan Gao ◽  
Qingguo Song ◽  
Chuang Li ◽  
Qing Hua ◽  
Chuang Yang
Keyword(s):  
Wavelet Transform ◽  
Texture Classification ◽  
Image Texture

scholarly journals Supervised Image Classification by Scattering Transform with Application to Weed Detection in Culture Crops of High Density

2019 ◽  
Vol 11 (3) ◽  
pp. 249 ◽  
Author(s):  
Pejman Rasti ◽  
Ali Ahmad ◽  
Salma Samiei ◽  
Etienne Belin ◽  
David Rousseau
Keyword(s):  
Texture Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Local Binary Patterns ◽  
High Density ◽  
Weed Detection ◽  
Data Set ◽  
Scattering Transform ◽  
Supervised Image Classification ◽  
Occurrence Matrix

In this article, we assess the interest of the recently introduced multiscale scattering transform for texture classification applied for the first time in plant science. Scattering transform is shown to outperform monoscale approaches (gray-level co-occurrence matrix, local binary patterns) but also multiscale approaches (wavelet decomposition) which do not include combinatory steps. The regime in which scatter transform also outperforms a standard CNN architecture in terms of data-set size is evaluated ( 10 4 instances). An approach on how to optimally design the scatter transform based on energy contrast is provided. This is illustrated on the hard and open problem of weed detection in culture crops of high density from the top view in intensity images. An annotated synthetic data-set available under the form of a data challenge and a simulator are proposed for reproducible science (https://uabox.univ-angers.fr/index.php/s/iuj0knyzOUgsUV9). Scatter transform only trained on synthetic data shows an accuracy of 85 % when tested on real data.

scholarly journals Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hong Zhu ◽  
Qianhao Fang ◽  
Yihe Huang ◽  
Kai Xu
Keyword(s):  
Feature Extraction ◽  
Pituitary Tumor ◽  
Sequence Data ◽  
Texture Classification ◽  
Pituitary Tumors ◽  
Image Data ◽  
Extraction Process ◽  
Small Sample ◽  
Image Texture ◽  
Diagnostic Efficiency

Abstract Background Accurately determining the softness level of pituitary tumors preoperatively by using their image textures can provide a basis for surgical options and prognosis. Existing methods for this problem require manual intervention, which could hinder the efficiency and accuracy considerably. Methods We present an automatic method for diagnosing the texture of pituitary tumors using unbalanced sequence image data. Firstly, for the small sample problem in our pituitary tumor MRI image dataset where T1 and T2 sequence data are unbalanced (due to data missing) and under-sampled, our method uses a CycleGAN (Cycle-Consistent Adversarial Networks) model for domain conversion to obtain fully sampled MRI spatial sequence. Then, it uses a DenseNet (Densely Connected Convolutional Networks)-ResNet(Deep Residual Networks) based Autoencoder framework to optimize the feature extraction process for pituitary tumor image data. Finally, to take advantage of sequence data, it uses a CRNN (Convolutional Recurrent Neural Network) model to classify pituitary tumors based on their predicted softness levels. Results Experiments show that our method is the best in terms of efficiency and accuracy (91.78%) compared to other methods. Conclusions We propose a semi-supervised method for grading pituitary tumor texture. This method can accurately determine the softness level of pituitary tumors, which provides convenience for surgical selection and prognosis, and improves the diagnostic efficiency of pituitary tumors.

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