Dozens of textural features have been published, but their realistic validation for efficient recognition applications still needs to be discovered. Textural features are derived using various approaches. We present a benchmark that can be used to evaluate these features and categorize them based on their information efficiency. We propose how the features can be benchmarked and explain different ways of measuring their properties and performance. Most textural feature-extracting algorithms are only based on information extraction from monospectral images (gray-level). Apart from native multispectral algorithms, we generalize some of these originally monospectral features for hyperspectral textures in our illustrating examples.

We present a retrospective analysis of Czech anti-covid governmental measures’ effectiveness for an unusually long three years of observation. Numerous Czech government restrictive measures illustrate this analysis applied to three years of COVID-19 data from the first three COVID-19 cases detected on 1st March 2020 till March 2023. It illustrates the course from the dramatic combat of unknown illness to resignation to country-wide measures and placing COVID-19 into a category of common nuisances. Our analysis uses the derived adaptive recursive Bayesian stochastic multidimensional Covid model-based prediction of nine essential publicly available COVID-19 data series. The COVID-19 model enables us to differentiate between effective measures and solely nuisance or antagonistic provisions and their correct or wrong timing. Our COVID model allows us to predict vital covid statistics such as the number of hospitalized, deaths, or symptomatic individuals, which can serve for daily control of anti-covid measures and the necessary precautions and formulate recommendations to control future pandemics.

The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods. The benchmark verifies segmenter performance characteristics on potentially unlimited monospectral, multispectral, satellite, and bidirectional texture function (BTF) data using an extensive set of over forty prevalent criteria. It also enables us to test for noise robustness and scale, rotation, or illumination invariance. It can be used in other applications, such as feature selection, image compression, query by pictorial example, etc. The benchmark's functionalities are demonstrated in evaluating several examples of leading previously published unsupervised and supervised image segmentation algorithms. However, they are used to illustrate the benchmark functionality and not review the recent image segmentation state-of-the-art.

New similarity criteria capable of assessing spectral modelling plausibility of colour, bidirectional texture functions (BTF), and hyperspectral textures are presented. The criteria credibly compare the multi‐spectral pixel values of the textures. They simultaneously consider the pixels of similar values and their mutual ratios. It allows support of the optimal modelling or acquisition setup development by comparing the original data with its synthetic simulations. Analytical applications of the criteria can be spectral based texture retrieval or classification. The suggested criteria together with existing alternatives are extensively tested and compared on a wide variety of colour, BTF, and hyperspectral textures. The performance quality of the criteria is examined in a long series of thousands specially designed monotonically degrading experiments, where proposed ones outperform all tested alternatives.

This paper introduces a novel multidimensional statistical model for realistic modeling, enlargement, editing, and compression of the recent state-of-the-art bidirectional texture function (BTF) textural representation. The presented multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric random fields models. The primary purpose of our modeling texture approach is to reproduce, compress, and enlarge a given measured natural or artificial texture image so that ideally both natural and synthetic texture will be visually indiscernible for any observation or illumination directions. However, the model can be easily applied for BFT material texture editing as well. The CMRF model consists of several parametric sub-models each having different characteristics along with an underlying switching structure model which controls transitions between these sub models. The proposed model uses the non-parametric random field for distributing local texture models in the form of analytically solvable wide-sense BTF Markov representation for single regions among the fields of a mosaic approximated by the random field structure model. The non-parametric control field of BTF -CMRF is reiteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The local texture regions (not necessarily continuous) are represented by analytical BTF models modeled by the adaptive 3D causal auto-regressive (3DCAR) random field model which can be analytically estimated as well as synthesized. The visual quality of the resulting complex synthetic textures generally surpasses the outputs of the previously published simpler non-compound BTF -MRF models. The model allows reaching huge compression ratio incomparable with any standard image compression method.

Dynamic texture similarity ranking is a challenging and still unsolved problem. Evaluation of how well are various dynamic textures similar to humans perception view is extremely difficult even for static textures and requires tedious psychophysical experiments. Human perception principles are largely not understood yet and the dynamic texture perception is further complicated with a distinct way of perceiving spatial and temporal domains, which complicates any similarity criterion definition. We propose a novel dynamic texture criterion based on the Fourier transformation and properties of dynamic texture spatio-temporal frequencies. The presented criterion correlates well with performed psycho-physical tests while maintaining sufficient diversity and descriptiveness.

Visual texture fidelity evaluation is important but still unsolved problem. Evaluation of how well various texture models conform with human visual perception of their original measured pattern is required not only for assessing the visual dissimilarities between a model output and the original measured texture, but also for optimal settings of model parameters, for fair comparison of distinct models, or visual scene understanding. We propose a novel texture fidelity criterion based on the fully multi-spectral generative underlying Markovian texture model, which correlates well with human texture quality ranking verified on the texture fidelity benchmark.

Visual texture modeling based on multidimensional mathematical models is the prerequisite for both robust material recognition as well as for image restoration, compression or numerous physically correct virtual reality applications. A novel multispectral visual texture modeling method based on a descriptive, unusually complex, three-dimensional, spatial Gaussian mixture model is presented. Texture synthesis benefits from easy computation of arbitrary conditional distributions from the model. The model is inherently multispectral thus it does not suffer with the spectral quality compromises of the spectrally factorized alternative approaches. The model is especially well suited for multispectral textile textures and it can also describe the most advanced textural representation in the form of a bidirectional texture function (BTF).

Visualization helps us to understand single-label and multi-label classification problems. In this paper, we show several standard techniques for simultaneous visualization of samples, features and multi-classes on the basis of linear regression and matrix factorization. The experiment with two real-life multi-label datasets showed that such techniques are effective to know how labels are correlated to each other and how features are related to labels in a given multi-label classification problem.

The unsupervised color image segmentation competition is taking place in conjunction with the ICPR 2014 conference. It aims to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets. The results evaluation is based on the standard performance assessment methodology using the online web verification server. We present in this paper the top six preliminary results submitted to this contest till the ICPR paper submission deadline.

An efficient method for X-ray digital mammogram multi-view enhancement based on the underlying two-dimensional adaptive causal autoregressive texture model is presented. The method locally predicts breast tissue texture from multi-view mammograms and enhances breast tissue abnormalities, such as the sign of a developing cancer, using the estimated model prediction error. The mammo-gram enhancement is based on the cross-prediction error of mutually registered left and right breasts mammograms or on the single-view model prediction error if both breasts' mammograms are not available.

In this paper we introduce unique publicly available dense an isotropic BRDF data measurements. We use this dense data as a reference for performance evaluation of the proposed BRDF sparse angular sampling and interpolation approach. The method is based on sampling of BRDF subspaces at fixed elevations by means of several adaptively-represented, uniformly distributed, perpendicular slices. Although this proposed method requires only a sparse sampling of material, the interpolation provides a very accurate reconstruction, visually and computationally comparable to densely measured reference. Due to the simple slices measurement and method's robustness it allows for a highly accurate acquisition of BRDFs. This in comparison with standard uniform angular sampling, is considerably faster yet uses far less samples.

Multidimensional visual texture is the appropriate paradigm for physically correct material visual properties representation. The course will present recent advances in texture modelling methodology as applied in computer vision, pattern recognition, computer graphics, and virtual/augmented reality applications. Contrary to previous courses on material appearance, we will focus on materials whose nature allows the exploitation of texture modeling approaches. This topic is introduced in the wider and complete context of pattern recognition and image processing. It comprehends modeling of multi-spectral images and videos which can be accomplished either with multi-dimensional mathematical models or sophisticated sampling methods from the original measurements. The key aspects of the topic, i.e., different multi-dimensional data models with their corresponding benefits and drawbacks, optimal model selection, parameter estimation and model synthesis techniques, are discussed. These methods produce compact parametric sets that not only faithfully reproduce material appearance, but are also vital for visual scene analysis, e.g. texture segmentation, classification, and retrieval. Special attention is devoted to recent advanced trends towards Bidirectional Texture Function (BTF) modeling, used for materials that do not obey Lambertian law, and whose reflectance has non-trivial illumination and viewing direction dependency. BTFs represent the best known effectively applicable textural representation of most real-world materials' visual properties. The techniques covered include efficient Markov random field-based algorithms, intelligent sampling algorithms, spatially-varying reflectance models and challenges with their possible implementation on GPU. The course also deals with proper data measurement, visualization of texture models in virtual scenes, visual quality evaluation feedback, as well as description of key industrial and research applications. We will discuss options as to which type of material representation is appropriate for required application, what are its limits and possible modelling options, and what the biggest challenges in realistic modelling of materials are.

Illumination and view dependent texture provide ample information on the appearance of real materials at the cost of enormous data storage requirements. Hence, past research focused mainly on compression and modelling of these data, however, few papers have explicitly addressed the way in which humans perceive these compressed data. We analyzed human gaze information to determine appropriate texture statistics. These statistics were then exploited in a pilot illumination and view direction dependent data compression algorithm. Our results showed that taking into account local texture variance can increase compression of current methods more than twofold, while preserving original realistic appearance and allowing fast data reconstruction.

This paper describes a novel compound Markov random field model capable of realistic modelling of multispectral bidirectional texture function, which is currently the most advanced representation of visual properties of surface materials. The proposed compound Markov random field model combines a non-parametric control random field with analytically solvable wide sense Markov representation for single regions and thus allows to avoid demanding Markov Chain Monte Carlo methods for both parameters estimation and the compound random field synthesis.

In this paper we present a method for seamless enlargement and editing of intricate near-regular type of bidirectional texture function (BTF) which contains simultaneously both regular periodic and stochastic components. Such BTF textures cannot be convincingly synthesised using neither simple tiling nor using purely stochastic models. However these textures are ubiquitous in many man-made environments and also in some natural scenes. Thus they are required for their realistic appearance visualisation. The principle of the presented BTF-NR synthesis and editing method is to automatically separate periodic and random components from one or more input textures. Each of these components is subsequently independently modelled using its corresponding optimal method. The regular texture part is modelled using our roller method, while the random part is synthesised from its estimated exceptionally efficient Markov random field based representation. Both independently enlarged texture components from the original measured textures representing one (enlargement) or several (editing) materials are combined in the resulting synthetic near-regular texture.

A visual appearance of natural materials fundamentally depends on illumination conditions, which significantly complicates a real scene analysis. We propose textural features based on fast Markovian statistics, which are simultaneously invariant to illumination colour and robust to illumination direction. No knowledge of illumination conditions is required and a recognition is possible from a single training image per material. Material recognition is tested on the currently most realistic visual representation-Bidirectional Texture Function (BTF), using the Amsterdam Library of Textures (ALOT), which contains 250 natural materials acquired in different illumination conditions. Our proposed features significantly outperform several leading alternatives including Local Binary Patterns (LBP, LBP-HF) and Gabor features.

A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised segmenter. The segmentation algorithm starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the large illumination invariant benchmark from the Prague Segmentation Benchmark using 21 segmentation criteria and compares favourably with an alternative segmentation method.

We propose an illumination invariant and rotation insensitive texture representation based on a Markovian textural model. A texture is aligned with its dominant orientation and textural features are derived from fast analytical estimates of Markovian statistics. We do not require any knowledge of illumination direction or spectrum. This makes our method suitable for computer analysis of real scenes, where appearance of materials depends on their orientation towards the illumination source. Our method is tested on the most realistic visual representation of natural materials - the bidirectional texture function (BTF), using data from the CUReT database, where it outperforms the alternative leading illumination invariant Local Binary Patterns (LBP) and texton MR8 methods, respectively.

An ever-growing number of real-world computer vision applications require classification, segmentation, retrieval, or realistic rendering of genuine materials. However, the appearance of real materials dramatically changes with illumination and viewing variations. Thus, the only reliable representation of material visual properties requires capturing of its reflectance in as wide range of light and camera position combinations as possible. This is a principle of the recent most advanced texture representation, the Bidirectional Texture Function (BTF). Multispectral BTF is a seven-dimensional function that depends on view and illumination directions as well as on planar texture coordinates. BTF is typically obtained by measurement of thousands of images covering many combinations of illumination and viewing angles. However, the large size of such measurements has prohibited their practical exploitation in any sensible application until recently. During the last few years, the first BTF measurement, compression, modeling, and rendering methods have emerged. In this paper, we categorize, critically survey, and psychophysically compare such approaches, which were published in this newly arising and important computer vision and graphics area.

The use of illumination and view-dependent texture information is recently the best way to capture the appearance of real-world materials accurately. One example is the Bidirectional Texture Function. The main disadvantage of these data is their massive size. In this article, we employ perceptually-based methods to allow more efficient handling of these data. In the first step, we analyse different uniform resampling by means of a psychophysical study with 11 subjects, comparing original data with rendering of a uniformly resampled version over the hemisphere of illumination and view-dependent textural measurements. We have found that down-sampling in view and illumination azimuthal angles is less apparent than in elevation angles and that illumination directions can be down-sampled more than view directions without loss of visual accuracy. In the second step, we analyzed subjects gaze fixation during the experiment. The gaze analysis confirmed results from the experiment and revealed that subjects were fixating at locations aligned with direction of main gradient in rendered stimuli. As this gradient was mostly aligned with illumination gradient, we conclude that subjects were observing materials mainly in direction of illumination gradient. Our results provide interesting insights in human perception of real materials and show promising consequences for development of more efficient compression and rendering algorithms using these kind of massive data.

We propose a new approach to diagnostic evaluation of screening mammograms based on local statistical texture models. The local evaluation tool has the form of a multivariate probability density of gray levels in a suitably chosen search window. First, the density function in the form of Gaussian mixture is estimated from data obtained by scanning of the mammogram with the search window. Then we evaluate the estimated mixture at each position and display the corresponding log-likelihood value as a gray level at the window center. The resulting log-likelihood image closely correlates with the structural details of the original mammogram and emphasizes unusual places. We assume that, in parallel use, the log-likelihood image may provide additional information to facilitate the identification of malignant lesions as atypical locations of high novelty.

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The "fragmentation" step allows one to find the elementary textures of the model, while the "reconstruction" step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.

With significantly increasing number of archived movie sequences a need of their automatic indexation and annotation is raising. Robust and fast temporal segmentation of video sequences is one of the challenging research topics in this area. In this paper we propose a new temporal segmentation method of the video sequences based on PCA approach. Contrary to standard approaches based on histogram or motion field analysis the proposed method does not require any such a complex analysis. The method starts with sparse greyscale sampling and eigen-analysis of input sequence. A sum of absolute derivatives of temporal mixing coefficients of main eigen-images is then used as cuts detection feature, while dissolve transitions are detected by means of coefficients’ specific behaviour. The functionality of the method was successfully tested on number of sequences ranging from artificial set of similar dynamic textures to professional documentary movies. Although, the results may not be unexpected, we believe that proposed method provides novel, very fast and reliable way of movie cuts detection.

We present a multiscale unsupervised segmenter for automatic detection of potentially cancerous regions of interest containing fibroglandular tissue in digital screening mammography. The mammogram tissue textures are locally represented by four causal multispectral random field models recursively evaluated for each pixel and several scales. The segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous mammogram segments is reached. The performance of the presented method is verified on the Digital Database for Screening Mammography (DDSM) from the University of South Florida as well as extensively tested on the Prague Texture Segmentation Benchmark and compares favourably with several alternative unsupervised texture segmentation methods.

The Prague texture segmentation data-generator and benchmark is a web based (http://mosaic.utia.cas.cz) service designed to mutually compare and rank different texture segmenters, and to support new segmentation and classification methods development. The benchmark verifies their performance characteristics on monospectral, multispectral, bidirectional texture function (BTF) data and enables to test their noise robustness, scale, and rotation or illumination invariance. It can easily be used for other applications such as feature selection, image compression, and query by pictorial example, etc. The benchmark functionalities are demonstrated on five previously published image segmentation algorithms evaluation.

We propose textural features, which are invariant to illumination spectrum and extremely robust to illumination direction. They require only a single training image per texture and no knowledge of illumination direction or spectrum. Hence, these features are suitable for content-based image retrieval (CBIR) of realistic scenes with colour textured objects and variable illumination. The illumination invariants are derived from Markov random field based texture representations. Our illumination invariant features are favourably compared with frequented features in this area - the local binary patterns, steerable pyramid and Gabor textural features, respectively. The superiority of our new invariant features is demonstrated in the illumination invariant recognition of the most advanced representation for realistic real-world materials - bidirectional texture function (BTF) textures.

Bidirectional Texture Functions (BTF) are commonly thought to provide the most realistic perceptual experience of materials from rendered images. The key to providing efficient compression of BTFs is the decision as to how much of the data should be preserved. We use psychophysical experiments to show that this decision depends critically upon the material concerned. Furthermore, we develop a BTF derived metric that enables us to automatically set a material's compression parameters in such a way as to provide users with a predefined perceptual quality. We investigate the correlation of three different BTF metrics with psychophysically derived data. Eight materials were presented to eleven naive observers who were asked to judge the perceived quality of BTF renderings as the amount of preserved data was varied. The metric showing the highest correlation with the thresholds set by the observers was the mean variance of individual BTF images. This metric was then used to automatically determine the material-specific compression parameters used in a vector quantisation scheme. The results were successfully validated in an experiment with six additional materials and eighteen observers. We show that using the psychophysically reduced BTF data significantly improves performance of a PCA-based compression method. On average, we were able to increase the compression ratios, and decrease processing times, by a factor of four without any differences being perceived.

A novel model for unsupervised segmentation of texture images is presented. The image to be segmented is first discretized and then a hierarchical finite-state region-based model is automatically coupled with the data by means of a sequential optimization scheme, namely the texture fragmentation and reconstruction (TFR) algorithm. Both intra- and inter-texture interactions are modeled, by means of an underlying hierarchical finite-state model, and eventually the segmentation task is addressed in a completely unsupervised manner. The output is then a nested segmentation, so that the user may decide the scale at which the segmentation has to be provided. TFR is composed of two steps: the former focuses on the estimation of the states at the finest level of the hierarchy, and is associated with an image fragmentation, or over-segmentation; the latter deals with the reconstruction of the hierarchy representing the textural interaction at different scales.

The recent advanced representation for realistic real-world materials in virtual reality applications is the Bidirectional Texture Function (BTF) which describes rough texture appearance for varying illumination and viewing conditions. Such a function can be represented by thousands of measurements (images) per material sample. The resulting BTF size excludes its direct rendering in graphical applications and some compression of these huge BTF data spaces is obviously inevitable. In this paper we present a novel, fast probabilistic model-based algorithm for realistic BTF modeling allowing an extreme compression with the possibility of a fast hardware implementation. Its ultimate aim is to create a visual impression of the same material without a pixel-wise correspondence to the original measurements. The analytical step of the algorithm starts with a BTF space segmentation and a range map estimation by photometric stereo of the BTF surface, followed by the spectral and spatial factorization of selected sub-space color texture images. Single mono-spectral band-limited factors are independently modeled by their dedicated spatial probabilistic model. During rendering, the sub-space images of arbitrary size are synthesized and both color (possibly multi-spectral) and range information is combined in a bump-mapping filter according to the view and illumination directions. The presented model offers a huge BTF compression ratio unattainable by any alternative sampling-based BTF synthesis method. Simultaneously this model can be used to reconstruct missing parts of the BTF measurement space.

Presents a derivation of recursive square-root filters, which can update either a symmetric positive data gathering matrix or its inversion. These filters differ from usual recursive approaches by their ability not only to supply new data to these matrices but also to simultaneously remove data previously fed to these matrices during their build-up. A condition for stable data removal is proven. Efficient recursive statistics of the least square or the maximum pseudo-likelihood type can be built using these results as it is briefly demonstrated on the colour texture segmentation example.

This paper presents a derivation of a fast recursive filter for image restoration if degradation obeys a linear degradation model with the unknown possibly non-homogeneous non-causal point-spread function. The degraded image is assumed to follow a causal simultaneous regressive model and the point-spread function is estimated using the local least-square estimate.

An algorithm for fast automatic acquisition of virtual models of planar-faced textured objects from registered range and colour images is introduced. The algorithm consists of four main steps-range image segmentation, texture analysis, virtual model geometry inference and texture synthesis. An efficient and robust range image segmentation is based on the combination of range profile modelling and line-based region growing. The texture is modelled using a multi-resolution Markov random field based model.

A new algorithm for planar face segmentation in range images is introduced. The segmentation is based on a combination of recursive adaptive regression model prediction for detecting range image step discontinuities which are present at object face borders and of a region growing on surface lines. Border pixels are detected in two perpendicular directions and detection results are combined together. Two predictors in each direction use identical contextual information from the pixel neighbourhood and they mutually compete for the most optimal discontinuity defection.

The paper presents a hierarchical synchronization model for the description of the time relations and regimes necessary for the presentation of multimedia or animated data which have either natural or implied time dependencies. The model generalizes some previous multimedia synchronization models by unifying time and event based synchronization concepts and offering a consistent framework in which to handle dynamic media presentation functionality.

Abstract MADE is the acronym for the ESPRIT project 6307, whose aim is to develop an object oriented multimedia application development environment. As part of this project the MADE help system is designed to be a distributed hypermedia system with additional support for run‐time object monitoring and contextual help.

A new adaptive regression type of image destriping method is introduced to reconstruct failed lines in multispectral image data. The method uses available information from the failed pixel surrounding due to spectral and spatial correlation of multispectral data. The reconstruction is based on two competing adaptive regression models from which the locally optimal predictor is selected.

The authors present a hierarchical extension of the probabilistic relaxation method and demonstrate its application to the multiscale processing of edge information. The basic idea is to utilise interlevel constraints on the evolution of edge structure to locate consistent edge labellings at each descending level of a resolution pyramid. Information concerning the whereabouts of consistent edge structure is passed from one layer of the pyramid to another in the form of a labelled edge map. This information is combined with the raw edge data at the relevant level of the pyramid using a Bayesian extension of the probabilistic relaxation formula of A. Rosenfeld, R.A. Hummel and S.W. Zucker; the result is a fine resolution label interpretation. Since the consistent edge structures permitted at coarse resolution have a much greater spatial extent than those permitted at fine resolution, they provide more powerful constraints on edge connectivity. This means that noise contamination can be efficiently controlled without the use of excessive filtering and consequent band limitation of genuine high frequency image structure.>