Papers Texture Classification
“Texture Classification” 태그가 달린 논문 206편 · 필터 해제
Riesz feature representation: scale equivariant scattering network for classification tasks
Scattering networks yield powerful and robust hierarchical image descriptors which do not require lengthy training and which work well with very few training data. However, they rely on sampling the scale dimension. Henc…
ClassificationTexture ClassificationInterpretable 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. The corpus callosum (CC) is the principal white matter fibre tract, enabling all kind…
Texture ClassificationDeep learning automated quantification of lung disease in pulmonary hypertension on CT pulmonary angiography: A preliminary clinical study with external validation
Purpose: Lung disease assessment in precapillary pulmonary hypertension (PH) is essential for appropriate patient management. This study aims to develop an artificial intelligence (AI) deep learning model for lung textur…
ClassificationManagementTexture ClassificationPenalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients
Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) emplo…
feature selectionSurvival AnalysisTexture ClassificationRADAM: Texture Recognition through Randomized Aggregated Encoding of Deep Activation Maps
Texture analysis is a classical yet challenging task in computer vision for which deep neural networks are actively being applied. Most approaches are based on building feature aggregation modules around a pre-trained ba…
DecoderTexture ClassificationTexture Representation via Analysis and Synthesis with Generative Adversarial Networks
We investigate data-driven texture modeling via analysis and synthesis with generative adversarial networks. For network training and testing, we have compiled a diverse set of spatially homogeneous textures, ranging fro…
Texture ClassificationNeutrosophic set based local binary pattern for texture classification
This paper presents novel neutrosophic set-based Completed Local Binary Pattern (CLBP) hybrid methods. These methods first transform the input image into a neutrosophic domain, and the image's texture is characterized by…
ClassificationTexture ClassificationNEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research
A shared goal of several machine learning communities like continual learning, meta-learning and transfer learning, is to design algorithms and models that efficiently and robustly adapt to unseen tasks. An even more amb…
Continual LearningDiversityMeta-LearningOptical Character Recognition (OCR)+3PIPPI2021: An Approach to Automated Diagnosis and Texture Analysis of the Fetal Liver & Placenta in Fetal Growth Restriction
Fetal growth restriction (FGR) is a prevalent pregnancy condition characterised by failure of the fetus to reach its genetically predetermined growth potential. We explore the application of model fitting techniques, lin…
regressionTexture ClassificationAutomated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks
Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differenti…
Texture ClassificationTransfer LearningTexture image analysis based on joint of multi directions GLCM and local ternary patterns
Human visual brain use three main component such as color, texture and shape to detect or identify environment and objects. Hence, texture analysis has been paid much attention by scientific researchers in last two decad…
Texture ClassificationMultilayer deep feature extraction for visual texture recognition
Convolutional neural networks have shown successful results in image classification achieving real-time results superior to the human level. However, texture images still pose some challenge to these models due, for exam…
Classificationimage-classificationImage ClassificationTexture ClassificationTexture features in medical image analysis: a survey
The texture is defined as spatial structure of the intensities of the pixels in an image that is repeated periodically in the whole image or regions, and makes the concept of the image. Texture, color and shape are three…
image-classificationImage ClassificationMedical Image AnalysisMedical Image Classification+2Large-Margin Representation Learning for Texture Classification
This paper presents a novel approach combining convolutional layers (CLs) and large-margin metric learning for training supervised models on small datasets for texture classification. The core of such an approach is a lo…
ClassificationMetric LearningRepresentation LearningTexture ClassificationUnsupervised Learning of the Total Variation Flow
The total variation (TV) flow generates a scale-space representation of an image based on the TV functional. This gradient flow observes desirable features for images, such as sharp edges and enables spectral, scale, and…
Texture ClassificationCan autism be diagnosed with AI?
Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are usin…
Texture Classification2-d signature of images and texture classification
We introduce a proper notion of 2-dimensional signature for images. This object is inspired by the so-called rough paths theory, and it captures many essential features of a 2-dimensional object such as an image. It thus…
ClassificationObjectTexture ClassificationSelf-Supervised Learning to Guide Scientifically Relevant Categorization of Martian Terrain Images
Automatic terrain recognition in Mars rover images is an important problem not just for navigation, but for scientists interested in studying rock types, and by extension, conditions of the ancient Martian paleoclimate a…
SandSelf-Supervised LearningTexture ClassificationMultiscale Analysis for Improving Texture Classification
Information from an image occurs over multiple and distinct spatial scales. Image pyramid multiresolution representations are a useful data structure for image analysis and manipulation over a spectrum of spatial scales.…
ClassificationTexture ClassificationAutomated Surface Texture Analysis via Discrete Cosine Transform and Discrete Wavelet Transform
Surface roughness and texture are critical to the functional performance of engineering components. The ability to analyze roughness and texture effectively and efficiently is much needed to ensure surface quality in man…
Texture Classification