Papers Texture Classification
“Texture Classification” 태그가 달린 논문 206편 · 필터 해제
Assessment of the Local Tchebichef Moments Method for Texture Classification by Fine Tuning Extraction Parameters
In this paper we use machine learning to study the application of Local Tchebichef Moments (LTM) to the problem of texture classification. The original LTM method was proposed by Mukundan (2014). The LTM method can be us…
General ClassificationTexture ClassificationFusion of Convolutional Neural Network and Statistical Features for Texture classification
Texture is a fundamental characteristic of many types of images, especially those with significant rotation, scale illumination, and viewpoint change. Texture image classification is one of the challenging problems that …
General Classificationimage-classificationImage ClassificationMaterial Recognition+2Spatio-spectral networks for color-texture analysis
Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color informa…
AttributeTexture ClassificationAdaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to di…
DiagnosticTexture ClassificationAre Quantitative Features of Lung Nodules Reproducible at Different CT Acquisition and Reconstruction Parameters?
Consistency and duplicability in Computed Tomography (CT) output is essential to quantitative imaging for lung cancer detection and monitoring. This study of CT-detected lung nodules investigated the reproducibility of v…
Computed Tomography (CT)Texture ClassificationA New Benchmark Dataset for Texture Image Analysis and Surface Defect Detection
Texture analysis plays an important role in many image processing applications to describe the image content or objects. On the other hand, visual surface defect detection is a highly research field in the computer visio…
Defect DetectionTexture ClassificationColor Texture Classification Based on Proposed Impulse-Noise Resistant Color Local Binary Patterns and Significant Points Selection Algorithm
The main aim of this paper is to propose a color texture classification approach which uses color sensor information and texture features jointly. High accuracy, low noise sensitivity and low computational complexity are…
General ClassificationSensitivityTexture Classification3D Geometric salient patterns analysis on 3D meshes
Pattern analysis is a wide domain that has wide applicability in many fields. In fact, texture analysis is one of those fields, since the texture is defined as a set of repetitive or quasi-repetitive patterns. Despite it…
ClusteringTexture ClassificationMultiparametric Deep Learning and Radiomics for Tumor Grading and Treatment Response Assessment of Brain Cancer: Preliminary Results
Radiomics is an exciting new area of texture research for extracting quantitative and morphological characteristics of pathological tissue. However, to date, only single images have been used for texture analysis. We hav…
SpecificityTexture ClassificationProvably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade
This article presents a theory for constructing hierarchical networks in such a way that the networks are guaranteed to be provably scale covariant. We first present a general sufficiency argument for obtaining scale cov…
Texture ClassificationClustering Images by Unmasking - A New Baseline
We propose a novel agglomerative clustering method based on unmasking, a technique that was previously used for authorship verification of text documents and for abnormal event detection in videos. In order to join two c…
Authorship VerificationClusteringEvent DetectionHandwritten Digit Recognition+2Psychophysical vs. learnt texture representations in novelty detection
Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and origin…
Novelty DetectionTexture ClassificationTexture SynthesisPersistence Curves: A canonical framework for summarizing persistence diagrams
Persistence diagrams are one of the main tools in the field of Topological Data Analysis (TDA). They contain fruitful information about the shape of data. The use of machine learning algorithms on the space of persistenc…
BIG-bench Machine LearningTexture ClassificationTopological Data AnalysisShortest Paths in HSI Space for Color Texture Classification
Color texture representation is an important step in the task of texture classification. Shortest paths was used to extract color texture features from RGB and HSV color spaces. In this paper, we propose to use shortest …
ClassificationGeneral ClassificationTexture ClassificationDeep CNNs Meet Global Covariance Pooling: Better Representation and Generalization
Compared with global average pooling in existing deep convolutional neural networks (CNNs), global covariance pooling can capture richer statistics of deep features, having potential for improving representation and gene…
Fine-Grained Visual RecognitionGeneral ClassificationGPUImage Classification+2Texture image analysis and texture classification methods - A review
Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the …
ClassificationDefect DetectionGeneral Classificationimage-classification+4Boundary-Preserved Deep Denoising of the Stochastic Resonance Enhanced Multiphoton Images
As the rapid growth of high-speed and deep-tissue imaging in biomedical research, it is urgent to find a robust and effective denoising method to retain morphological features for further texture analysis and segmentatio…
DenoisingTexture ClassificationSegmentation of Skeletal Muscle in Thigh Dixon MRI Based on Texture Analysis
Segmentation of skeletal muscles in Magnetic Resonance Images (MRI) is essential for the study of muscle physiology and diagnosis of muscular pathologies. However, manual segmentation of large MRI volumes is a time-consu…
SegmentationTexture ClassificationProvably scale-covariant networks from oriented quasi quadrature measures in cascade
This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of comple…
Texture ClassificationBone Texture Analysis for Prediction of Incident Radio-graphic Hip Osteoarthritis Using Machine Learning: Data from the Cohort Hip and Cohort Knee (CHECK) study
Our aim was to assess the ability of radiography-based bone texture parameters in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) over a 10 years period. Pelvic radiographs from C…
Texture Classification