Papers Texture Classification
“Texture Classification” 태그가 달린 논문 206편 · 필터 해제
TextureWGAN: Texture Preserving WGAN with MLE Regularizer for Inverse Problems
Many algorithms and methods have been proposed for inverse problems particularly with the recent surge of interest in machine learning and deep learning methods. Among all proposed methods, the most popular and effective…
Image ReconstructionSSIMSuper-ResolutionTexture ClassificationDynamic texture analysis for detecting fake faces in video sequences
The creation of manipulated multimedia content involving human characters has reached in the last years unprecedented realism, calling for automated techniques to expose synthetically generated faces in images and videos…
Texture ClassificationC-CNN: Contourlet Convolutional Neural Networks
Extracting effective features is always a challenging problem for texture classification because of the uncertainty of scales and the clutter of textural patterns. For texture classification, spectral analysis is traditi…
ClassificationTexture ClassificationA cellular automata approach to local patterns for texture recognition
Texture recognition is one of the most important tasks in computer vision and, despite the recent success of learning-based approaches, there is still need for model-based solutions. This is especially the case when the …
Texture ClassificationLearning Local Complex Features using Randomized Neural Networks for Texture Analysis
Texture is a visual attribute largely used in many problems of image analysis. Currently, many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previ…
AttributeTexture ClassificationSelf-Supervised Learning of a Biologically-Inspired Visual Texture Model
We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabeled database of texture images. Inspired b…
Self-Supervised LearningTexture ClassificationHarvesting, Detecting, and Characterizing Liver Lesions from Large-scale Multi-phase CT Data via Deep Dynamic Texture Learning
Non-invasive radiological-based lesion characterization and identification, e.g., to differentiate cancer subtypes, has long been a major aim to enhance oncological diagnosis and treatment procedures. Here we study a spe…
Computed Tomography (CT)Texture ClassificationDeep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung
In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular…
ClassificationGeneral ClassificationSegmentationTexture ClassificationCo-occurrence Based Texture Synthesis
As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate. In this work, we turn to co-occurrence statistics, which have lon…
Generative Adversarial NetworkImage GenerationMORPHTexture Classification+1Modal features for image texture classification
Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accur…
ClassificationGeneral ClassificationTexture Classification3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to…
Computed Tomography (CT)Texture ClassificationSpectral Data Augmentation Techniques to quantify Lung Pathology from CT-images
Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity…
Data AugmentationTexture ClassificationDeep Transfer Learning for Texture Classification in Colorectal Cancer Histology
Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture an…
ClassificationDiagnosticGeneral ClassificationTexture Classification+1Introducing Anisotropic Minkowski Functionals for Local Structure Analysis and Prediction of Biomechanical Strength of Proximal Femur Specimens
Bone fragility and fracture caused by osteoporosis or injury are prevalent in adults over the age of 50 and can reduce their quality of life. Hence, predicting the biomechanical bone strength, specifically of the proxima…
Texture ClassificationLocal Rotation Invariance in 3D CNNs
Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the co…
Data AugmentationTexture ClassificationFabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, textile and clothing manufacturers have l…
Material RecognitionObject RecognitionScene UnderstandingTexture ClassificationTexture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale block pairs and utilize the intensity and …
ClassificationGeneral ClassificationTexture ClassificationHistogram Layers for Texture Analysis
We present a histogram layer for artificial neural networks (ANNs). An essential aspect of texture analysis is the extraction of features that describe the distribution of values in local spatial regions. The proposed hi…
Texture ClassificationDeep Learning Algorithms for Coronary Artery Plaque Characterisation from CCTA Scans
Analysing coronary artery plaque segments with respect to their functional significance and therefore their influence to patient management in a non-invasive setup is an important subject of current research. In this wor…
ManagementTexture ClassificationRotation Differential Invariants of Images Generated by Two Fundamental Differential Operators
In this paper, we design two fundamental differential operators for the derivation of rotation differential invariants of images. Each differential invariant obtained by using the new method can be expressed as a homogen…
Texture ClassificationVocal Bursts Valence Prediction