Texture Classification
28 papers with code • 0 benchmarks • 5 datasets
Texture Classification is a fundamental issue in computer vision and image processing, playing a significant role in many applications such as medical image analysis, remote sensing, object recognition, document analysis, environment modeling, content-based image retrieval and many more.
Source: Improving Texture Categorization with Biologically Inspired Filtering
Benchmarks
These leaderboards are used to track progress in Texture Classification
ElBa
Multidimensional Texture Perception
ISOD
CBTex
Latest papers with no code
Texture Classification Network Integrating Adaptive Wavelet Transform
Graves' disease is a common condition that is diagnosed clinically by determining the smoothness of the thyroid texture and its morphology in ultrasound images.
TexTile: A Differentiable Metric for Texture Tileability
We introduce TexTile, a novel differentiable metric to quantify the degree upon which a texture image can be concatenated with itself without introducing repeating artifacts (i. e., the tileability).
Grey Level Co-occurrence Matrix (GLCM) Based Second Order Statistics for Image Texture Analysis
There are significant correlation between Dissimilarity & Contrast, Homogeneity & Contrast, Entropy & Contrast, Energy & Contrast, Standard Deviation & Contrast, Correlation & Contrast, and Probability of Occurrence of Difference of 0-19 & Contrast with correlation coefficients of 0. 9322, -0. 5011, 0. 6681, -0. 4255, -0. 4914, 0. 5428, and -0. 8346 respectively.
Latent space configuration for improved generalization in supervised autoencoder neural networks
The proposed methods include loss configuration using a geometric loss term that acts directly in LS, and encoder configuration.
Riesz feature representation: scale equivariant scattering network for classification tasks
In this work, we define an alternative feature representation based on the Riesz transform.
Interpretable simultaneous localization of MRI corpus callosum and classification of atypical Parkinsonian disorders using YOLOv5
Structural MRI(S-MRI) is one of the most versatile imaging modality that revolutionized the anatomical study of brain in past decades.
Deep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation
This study aims to develop an artificial intelligence (AI) deep learning model for lung texture classification in CT Pulmonary Angiography (CTPA), and evaluate its correlation with clinical assessment methods.
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients
The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients.
Texture Representation via Analysis and Synthesis with Generative Adversarial Networks
We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks.
Neutrosophic set based local binary pattern for texture classification
As a result, using neutrosophic truth and false sets with a grayscale input image has resulted in more robust features.
