This paper proposes a new probabilistic non-extensive entropy feature for texture characterization, based on a Gaussian information measure. The highlights of the new entropy are that it is bounded by finite limits and that it is non additive in nature. The non additive property of the proposed entropy makes it useful for the representation of information content in the non-extensive systems containing some degree of regularity or correlation. The effectiveness of the proposed entropy in representing the correlated random variables is demonstrated by applying it for the texture classification problem since textures found in nature are random and at the same time contain some degree of correlation or regularity at some scale. The gray level co-occurrence probabilities (GLCP) are used for computing the entropy function. The experimental results indicate high degree of the classification accuracy. The performance of the new entropy function is found superior to other forms of entropy such as Shannon, Renyi, Tsallis and Pal and Pal entropies on comparison. Using the feature based polar interaction maps (FBIM) the proposed entropy is shown to be the best measure among the entropies compared for representing the correlated textures.
Separating an image into cartoon and texture components comes useful in image processing applications, such as image compression, image segmentation, image inpainting. Yves Meyer's influential cartoon texture decomposition model involves deriving an energy functional by choosing appropriate spaces and functionals. Minimizers of the derived energy functional are cartoon and texture components of an image. In this study, cartoon part of an image is separated, by reconstructing it from pixels of multi scale Total-Variation filtered versions of the original image which is sought to be decomposed i…
VRVis Center for Virtual Reality and Visualization（VRVis虚拟现实与可视化中心）；Braincon Handels-GmbH（Braincon贸易有限公司）；NOVA Medical School, Universidade Nova de Lisboa（新里斯本大学NOVA医学院）；Instituto de Medicina Molecular（分子医学研究所）；Centro Hospitalar Lisboa Ocidental（里斯本西部医疗中心）
The relationship between knee osteoarthritis progression and changes in tibial bone structure has long been recognized and various texture descriptors have been proposed to detect early osteoarthritis (OA) from radiographs. This work aims to investigate (1) femoral textures as an OA indicator and (2) the potential of entropy as a computationally efficient alternative to established texture descriptors. We design a robust semi-automatically placed layout for regions of interest (ROI), compute the Hurst coefficient and the entropy in each ROI, and employ statistical and machine learning methods …
Karnatak Arts, Science and Commerce College（卡纳塔克艺术、科学与商业学院）；INRIA Nancy-Grand Est（法国国家信息与自动化研究所南锡-大东部分部）；LORIA（洛林计算机科学及其应用研究所）
In this paper, we use statistical texture features for handwritten and printed text classification. We primarily aim for word level classification in south Indian scripts. Words are first extracted from the scanned document. For each extracted word, statistical texture features are computed such as mean, standard deviation, smoothness, moment, uniformity, entropy and local range including local entropy. These feature vectors are then used to classify words via k-NN classifier. We have validated the approach over several different datasets. Scripts like Kannada, Telugu, Malayalam and Hindi i.e.…
Non-Destructive Detection of Sub-Micron Imperceptible Scratches On Laser Chips Based On Consistent Texture Entropy Recursive Optimization Semi-Supervised Network
School of Automation, Central South University（中南大学自动化学院）
Centro de Investigaciones Ópticas (CONICET La Plata - CIC)（光学研究中心（CONICET La Plata - CIC））；Universidad Nacional de La Plata (UNLP)（拉普拉塔国立大学）；Universidade Estadual de Maringá（马林加州立大学）
The aim of this paper is to further explore the usefulness of the two-dimensional complexity-entropy causality plane as a texture image descriptor. A multiscale generalization is introduced in order to distinguish between different roughness features of images at small and large spatial scales. Numerically generated two-dimensional structures are initially considered for illustrating basic concepts in a controlled framework. Then, more realistic situations are studied. Obtained results allow us to confirm that intrinsic spatial correlations of images are successfully unveiled by implementing t…
Existing semantic segmentation works mainly focus on learning the contextual information in high-level semantic features with CNNs. In order to maintain a precise boundary, low-level texture features are directly skip-connected into the deeper layers. Nevertheless, texture features are not only about local structure, but also include global statistical knowledge of the input image. In this paper, we fully take advantages of the low-level texture features and propose a novel Statistical Texture Learning Network (STLNet) for semantic segmentation. For the first time, STLNet analyzes the distribu…
Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well a…
National University of Defense Technology（中国人民解放军国防科技大学）；University of Oulu（奥卢大学）；University of Waterloo（滑铁卢大学）；University of Maryland（马里兰大学）
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention. Since 2000, texture representations based on Bag of Words (BoW) and on Convolutional Neural Networks (CNNs) have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 200 major publications are cite…
Research on face spoofing detection has mainly been focused on analyzing the luminance of the face images, hence discarding the chrominance information which can be useful for discriminating fake faces from genuine ones. In this work, we propose a new face anti-spoofing method based on color texture analysis. We analyze the joint color-texture information from the luminance and the chrominance channels using a color local binary pattern descriptor. More specifically, the feature histograms are extracted from each image band separately. Extensive experiments on two benchmark datasets, namely CA…
University of Pavia（帕维亚大学）；University of Milano-Bicocca（米兰比可卡大学）
The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed …
Centre de mathématiques et de leurs applications（数学及其应用中心）；CNRS（法国国家科学研究中心）；ENS Paris-Saclay（巴黎萨克雷高等师范学院）；Université Paris-Saclay（巴黎萨克雷大学）；Institut Denis Poisson（德尼·泊松研究所）；Université d’Orléans（奥尔良大学）；Université de Tours（图尔大学）；Univ. Bordeaux（波尔多大学）；IMB（波尔多数学研究所）；Bordeaux INP（波尔多国立理工学院）
Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formulation on the image space for which the minimizers are assumed to be the solutions of the synthesis problem. In this paper we investigate, both theoretically and experimentally, another framework to deal with this problem using an alternate sampling/minimization scheme. First, we use results from information geometry to assess that our method yields a probability measure which has maximum entropy under some constrain…
Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain specific features. However, these approaches depend heavily on the richness of the texture bank, and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve generalization. In this paper, we presen…
Avinashilingam Institute for Home Science and Higher Education for Women（阿维纳希林加姆家政与高等教育女子学院）
Texture segmentation is the process of partitioning an image into regions with different textures containing a similar group of pixels. Detecting the discontinuity of the filter's output and their statistical properties help in segmenting and classifying a given image with different texture regions. In this proposed paper, chili x-ray image texture segmentation is performed by using Gabor filter. The texture segmented result obtained from Gabor filter fed into three texture filters, namely Entropy, Standard Deviation and Range filter. After performing texture analysis, features can be extracte…
Centre de Développement des Technologies Avancées（先进技术发展中心）；University of Oulu（奥卢大学）；Ecole Nationale Supèrieure d’Informatique（国立高等信息学学院）；Northwestern Polytechnical University（西北工业大学）
Automatic kinship verification using facial images is a relatively new and challenging research problem in computer vision. It consists in automatically predicting whether two persons have a biological kin relation by examining their facial attributes. While most of the existing works extract shallow handcrafted features from still face images, we approach this problem from spatio-temporal point of view and explore the use of both shallow texture features and deep features for characterizing faces. Promising results, especially those of deep features, are obtained on the benchmark UvA-NEMO Smi…
Ningbo University（宁波大学）；Ningbo Institute of Industrial Technology, Chinese Academy of Sciences（中国科学院宁波工业技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）
With the development of image recovery models,especially those based on adversarial and perceptual losses,the detailed texture portions of images are being recovered more naturally.However,these restored images are similar but not identical in detail texture to their reference images.With traditional image quality assessment methods,results with better subjective perceived quality often score lower in objective scoring.Assessment methods suffer from subjective and objective inconsistencies.This paper proposes a regional differential information entropy (RDIE) method for image quality assessmen…
University of Oulu（奥卢大学）；Shenzhen University（深圳大学）
In this paper, we propose a novel local feature, called Local Orientation Adaptive Descriptor (LOAD), to capture regional texture in an image. In LOAD, we proposed to define point description on an Adaptive Coordinate System (ACS), adopt a binary sequence descriptor to capture relationships between one point and its neighbors and use multi-scale strategy to enhance the discriminative power of the descriptor. The proposed LOAD enjoys not only discriminative power to capture the texture information, but also has strong robustness to illumination variation and image rotation. Extensive experiment…
Indian Statistical Institute（印度统计研究所）；National Institute of Technology, Rourkela（鲁尔凯拉国家技术学院）；Indian Institute of Information Technology, Sri City（斯里城印度信息技术学院）
Methods based on local image features have recently shown promise for texture classification tasks, especially in the presence of large intra-class variation due to illumination, scale, and viewpoint changes. Inspired by the theories of image structure analysis, this paper presents a simple, efficient, yet robust descriptor namely local jet pattern (LJP) for texture classification. In this approach, a jet space representation of a texture image is derived from a set of derivatives of Gaussian (DtGs) filter responses up to second order, so called local jet vectors (LJV), which also satisfy the …
South China University of Technology（华南理工大学）；Pazhou Lab（琶洲实验室）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所慈溪生物医学工程研究所）
Retinal image segmentation plays an important role in automatic disease diagnosis. This task is very challenging because the complex structure and texture information are mixed in a retinal image, and distinguishing the information is difficult. Existing methods handle texture and structure jointly, which may lead biased models toward recognizing textures and thus results in inferior segmentation performance. To address it, we propose a segmentation strategy that seeks to separate structure and texture components and significantly improve the performance. To this end, we design a structure-tex…
CNRS（法国国家科学研究中心）；ENS Paris-Saclay（巴黎-萨克雷高等师范学院）；Université Paris-Saclay（巴黎-萨克雷大学）；Université d’Orléans（奥尔良大学）；Université de Tours（图尔大学）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, macrocanonical models are given by Gibbs measures, using the maximum entropy principle. We study conditions under which this result extends to real-valued images. If these conditions hold, finding a macrocanonical model amounts to minimizing a convex function and sampling from an associated Gibbs measure. We analyze an algorithm which alternates betw…
Robert Bosch GmbH（罗伯特·博世公司）；Norwegian Open AI Lab（挪威开放人工智能实验室）；NTNU（挪威科技大学）
Ground texture based vehicle localization using feature-based methods is a promising approach to achieve infrastructure-free high-accuracy localization. In this paper, we provide the first extensive evaluation of available feature extraction methods for this task, using separately taken image pairs as well as synthetic transformations. We identify AKAZE, SURF and CenSurE as best performing keypoint detectors, and find pairings of CenSurE with the ORB, BRIEF and LATCH feature descriptors to achieve greatest success rates for incremental localization, while SIFT stands out when considering sever…
A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis
São Carlos Institute of Physics, University of São Paulo（圣保罗大学圣卡洛斯物理学院）；Institute of Mathematics and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Institute of Biosciences, Humanities and Exact Sciences, São Paulo State University（圣保罗州立大学生物科学、人文与精确科学研究所）；Ghent University（根特大学）
本文系统比较了21种预训练视觉Transformer在纹理特征提取上的表现，发现ViTs在鲁棒性、颜色/材料纹理和属性识别等任务中普遍优于CNNs和手工模型，并指出ViT-B with DINO、BeiTv2、Swin及EfficientFormer为有前景的模型。
Zhejiang University（浙江大学）；Shanghai Innovation Institute（上海创新研究院）；Westlake University（西湖大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Fudan University（复旦大学）
SkinFormer: Learning Statistical Texture Representation with Transformer for Skin Lesion Segmentation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Shandong Computer Science Center（山东省计算中心）；Qilu University of Technology（齐鲁工业大学）；Shandong Fundamental Research Center for Computer Science（山东计算机科学基础研究中心）；Beijing University of Posts and Telecommunications（北京邮电大学）
Pinscreen；University of Southern California（南加州大学）；USC Institute for Creative Technologies（南加州大学创意技术研究所）
We present a data-driven inference method that can synthesize a photorealistic texture map of a complete 3D face model given a partial 2D view of a person in the wild. After an initial estimation of shape and low-frequency albedo, we compute a high-frequency partial texture map, without the shading component, of the visible face area. To extract the fine appearance details from this incomplete input, we introduce a multi-scale detail analysis technique based on mid-layer feature correlations extracted from a deep convolutional neural network. We demonstrate that fitting a convex combination of…
