Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Combining local regularity estimation and total variation optimization for scale-free texture segmentation

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures

Stabilized tetragonal zirconium oxide as a support for catalysts: evolution of the texture and structure on calcination in static air

Single-phase tetragonal zirconium oxides have been made by the incorporation of 5.4 mol-% of Y3+ or La3+ in ZrO2 to form solid solutions. The samples were prepared by controlled coprecipitation from aqueous solutions of the respective metal chlorides at room temperature and at a constant pH of 10, followed by calcination at 500°C (in the case of the Y3+ -doped sample) or 600°C (in the case of the La3+ -doped sample) to effectuate the crystallization into the tetragonal phase. The process of crystallization of the hydrous zirconia precursor was found to be retarded by the incorporation of Y3+ or La3+, the latter giving the greater effect. Upon crystallization, stabilized tetragonal samples were obtained with high specific surface areas (SBET ca. 88 m2 g¿1 for both the samples) and well-developed mesoporous textures but without any microporosity. Both the Y3+ - and the La3+ -alloyed ZrO2 samples were found to fully retain the tetragonal phase upon calcination over the entire range of temperatures studied (up to 900°C). The thermal stability of the texture of zirconia was found to be considerably improved, in comparison with the undoped monoclinic material, by the stabilization of the crystal structure in the defect tetragonal form. In particular, incorporation of 5.4 mol-% of La3+ resulted in a support material which had a remarkable thermal stability. It is shown that the improvements in the thermal stability are derived from a strong inhibition of the processes of crystallite growth and the accompanying intercrystallite sintering and thus of the process of mass transport; the mass transport probably occurs by a mechanism of surface diffusion

Texture analysis and Its applications in biomedical imaging: a survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This survey’s emphasis is in collecting and categorising over five decades of active research on texture analysis.Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this survey’s final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.Manuscript received February 3, 2021; revised June 23, 2021; accepted September 21, 2021. Date of publication September 27, 2021; date of current version January 24, 2022. This work was supported in part by the Portuguese Foundation for Science and Technology (FCT) under Grants PTDC/EMD-EMD/28039/2017, UIDB/04950/2020, PestUID/NEU/04539/2019, and CENTRO-01-0145-FEDER-000016 and by FEDER-COMPETE under Grant POCI-01-0145-FEDER-028039. (Corresponding author: Rui Bernardes.)info:eu-repo/semantics/publishedVersio

On-line quality control in polymer processing using hyperspectral imaging

L’industrie du plastique se tourne de plus en plus vers les matériaux composites afin d’économiser de la matière et/ou d’utiliser des matières premières à moindres coûts, tout en conservant de bonnes propriétés. L’impressionnante adaptabilité des matériaux composites provient du fait que le manufacturier peut modifier le choix des matériaux utilisés, la proportion selon laquelle ils sont mélangés, ainsi que la méthode de mise en œuvre utilisée. La principale difficulté associée au développement de ces matériaux est l’hétérogénéité de composition ou de structure, qui entraîne généralement des défaillances mécaniques. La qualité des prototypes est normalement mesurée en laboratoire, à partir de tests destructifs et de méthodes nécessitant la préparation des échantillons. La mesure en-ligne de la qualité permettrait une rétroaction quasi-immédiate sur les conditions d’opération des équipements, en plus d’être directement utilisable pour le contrôle de la qualité dans une situation de production industrielle. L’objectif de la recherche proposée consiste à développer un outil de contrôle de qualité pour la qualité des matériaux plastiques de tout genre. Quelques sondes de type proche infrarouge ou ultrasons existent présentement pour la mesure de la composition en-ligne, mais celles-ci ne fournissent qu’une valeur ponctuelle à chaque acquisition. Ce type de méthode est donc mal adapté pour identifier la distribution des caractéristiques de surface de la pièce (i.e. homogénéité, orientation, dispersion). Afin d’atteindre cet objectif, un système d’imagerie hyperspectrale est proposé. À l’aide de cet appareil, il est possible de balayer la surface de la pièce et d’obtenir une image hyperspectrale, c’est-à-dire une image formée de l’intensité lumineuse à des centaines de longueurs d’onde et ce, pour chaque pixel de l’image. L’application de méthodes chimiométriques permettent ensuite d’extraire les caractéristiques spatiales et spectrales de l’échantillon présentes dans ces images. Finalement, les méthodes de régression multivariée permettent d’établir un modèle liant les caractéristiques identifiées aux propriétés de la pièce. La construction d’un modèle mathématique forme donc l’outil d’analyse en-ligne de la qualité des pièces qui peut également prédire et optimiser les conditions de fabrication.The use of plastic composite materials has been increasing in recent years in order to reduce the amount of material used and/or use more economic materials, all of which without compromising the properties. The impressive adaptability of these composite materials comes from the fact that the manufacturer can choose the raw materials, the proportion in which they are blended as well as the processing conditions. However, these materials tend to suffer from heterogeneous compositions and structures, which lead to mechanical weaknesses. Product quality is generally measured in the laboratory, using destructive tests often requiring extensive sample preparation. On-line quality control would allow near-immediate feedback on the operating conditions and may be transferrable to an industrial production context. The proposed research consists of developing an on-line quality control tool adaptable to plastic materials of all types. A number of infrared and ultrasound probes presently exist for on-line composition estimation, but only provide single-point values at each acquisition. These methods are therefore less adapted for identifying the spatial distribution of a sample’s surface characteristics (e.g. homogeneity, orientation, dispersion). In order to achieve this objective, a hyperspectral imaging system is proposed. Using this tool, it is possible to scan the surface of a sample and obtain a hyperspectral image, that is to say an image in which each pixel captures the light intensity at hundreds of wavelengths. Chemometrics methods can then be applied to this image in order to extract the relevant spatial and spectral features. Finally, multivariate regression methods are used to build a model between these features and the properties of the sample. This mathematical model forms the backbone of an on-line quality assessment tool used to predict and optimize the operating conditions under which the samples are processed

Monitoring of a carbon anode paste manufacturing process using machine vision and latent variable methods

Le procédé de réduction électrolytique Hall-Héroult est utilisé pour la fabrication industrielle d’aluminium primaire. Ce procédé nécessite l’utilisation d'anodes de carbone. L’uniformité de la qualité de celles-ci est un paramètre très important pour assurer la stabilité et des performances optimales des cuves d’électrolyse. Malheureusement, les fabricants d'anodes sont actuellement confrontés à une augmentation de la variabilité des matières premières. Cette situation est due à une diminution de la disponibilité de matières premières de bonne qualité à faibles coûts. Pour compenser, les fabricants d'anodes doivent diversifier leur choix de fournisseurs, ce qui augmente la variabilité. Cependant, les usines ne sont pas préparées pour réagir à cette situation tout en maintenant une qualité d'anode stable. Cette situation est due, entre autres, à un manque de mesures quantitatives en temps réel de la qualité des anodes. Plusieurs exemples d’applications industrielles de vision numérique ont été présentés dans la littérature. Par conséquent, il existe une opportunité de développer un tel système pour obtenir une mesure non destructive et en temps réel de la qualité de la pâte d'anode. Le développement du capteur a été fait avec de la pâte et des anodes pressées à l'échelle laboratoire. Un ensemble de caractéristiques de texture d'images calculées à partir de la transformée en ondelettes discrète (DWT) et de matrices de cooccurrence de niveaux de gris (GLCM) ont été sélectionnées. Ces caractéristiques étaient sensibles aux variations dans la formulation et de la quantité de brai dans la pâte. Le capteur est aussi capable de détecter la quantité optimale de brai (OPD) pour différents cokes. Ensuite, la sensibilité et la robustesse du capteur ont été testées avec de la pâte industrielle. Finalement, les usines collectent déjà beaucoup de mesures de procédé en temps réel. Ces données peuvent être utilisées dans une stratégie de monitorage statistique pour détecter et investiguer des déviations de qualité. Une nouvelle méthode statistique multivariée par variables latentes PLS multi-blocs séquentiels (SMB-PLS) a été développée pour améliorer l'interprétation des données industrielles par rapport aux méthodes usuelles de PLS multi-blocs. Cette méthode a également été utilisée pour discuter de la pertinence d’utiliser les caractéristique d'image de la pâte à un modèle statistique pour la surveillance de la variabilité du procédé.The Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting relies on the consumption of carbon anodes. The quality and consistency of these anodes are very important for the stability and performance of the reduction cells. Unfortunately, the anode manufacturers currently face an increase in the raw material variability. This is due to the declining availability of high quality, low cost and consistent materials on the market forcing the anode manufacturers to diversify their suppliers. However, the anode plants are not prepared to compensate for this increase in variability and still maintain consistent anode quality. There is a lack of real-time quality monitoring and control of the baked anodes properties and the most important raw material and process parameters. Machine vision applications have been successful in many industrial applications. Therefore there is an opportunity to develop such a system to obtain a non destructive and online measurement of the anode paste quality. This sensor could then be used in a feedback/feedforward control strategy for attenuating the unmeasured raw material and process variations. The sensor development was performed using laboratory scale paste and pressed anodes. A set of image texture features computed from discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) methods were selected. These features could capture variations in formulation, pitch ratio in the paste and in pitch demand. The sensor was also found to be sensitive to the optimum pitch demand (OPD) of two different cokes. Then, the sensitivity and robustness of the sensor was tested using industrial paste. Finally, the anode plants already collect some real-time process measurement and off-line raw material and baked anode properties that can be used to monitor and troubleshoot process and quality deviations. A new sequential multi-block PLS (SMB-PLS) method was developed to improve the interpretation of complex industrial dataset compared to already available multi-block PLS methods. This method was also used to discuss the relevance of adding real-time paste image feature to a statistical model for monitoring of the process variability

Stochasticity: A Feature for the Structuring of Large and Heterogeneous Image Databases

International audienceThe paper addresses image feature characterization and the structuring of large and heterogeneous image databases through the stochasticity or randomness appearance. Measuring stochasticity involves finding suitable representations that can significantly reduce statistical dependencies of any order. Wavelet packet representations provide such a framework for a large class of stochastic processes through an appropriate dictionary of parametric models. From this dictionary and the Kolmogorov stochasticity index, the paper proposes semantic stochasticity templates upon wavelet packet sub-bands in order to provide high level classification and content-based image retrieval. The approach is shown to be relevant for texture images

Texture representation using wavelet filterbanks

Texture analysis is a fundamental issue in image analysis and computer vision. While considerable research has been carried out in the texture analysis domain, problems relating to texture representation have been addressed only partially and active research is continuing. The vast majority of algorithms for texture analysis make either an explicit or implicit assumption that all images are captured under the same measurement conditions, such as orientation and illumination. These assumptions are often unrealistic in many practical applications;This dissertation addresses the viewpoint-invariance problem in texture classification by introducing a rotated wavelet filterbank. The proposed filterbank, in conjunction with a standard wavelet filterbank, provides better freedom of orientation tuning for texture analysis. This allows one to obtain texture features that are invariant with respect to texture rotation and linear grayscale transformation. In this study, energy estimates of channel outputs that are commonly used as texture features in texture classification are transformed into a set of viewpoint-invariant features. Texture properties that have a physical connection with human perception are taken into account in the transformation of the energy estimates;Experiments using natural texture image sets that have been used for evaluating other successful approaches were conducted in order to facilitate comparison. We observe that the proposed feature set outperformed methods proposed by others in the past. A channel selection method is also proposed to minimize the computational complexity and improve performance in a texture segmentation algorithm. Results demonstrating the validity of the approach are presented using experimental ultrasound tendon images