Academic literature on the topic 'Brodatz texture'

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of LBPs from these particular textures. Eventually, feature vectors are constructed by making into play the auto-correlation that exists between binary patterns. The retrieval results of the proposed method are examined on different texture image databases viz Brodatz database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and small set of rotated Brodatz database (DB4), and shows a major improvement in terms of average retrieval rate as when weighed against with LBP histogram and some existing transform domain technique.

This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.

In this work, an effective method has been proposed for texture segmentation, which incorporates the best features of filter bank and statistical approaches. This technique combines the features of Gabor wavelets (filter based) and General Moments (statistical) approaches. The method has been successfully tested for various textures from Brodatz texture collection. The relative performance of this method against the conventional approaches has been analyzed using Fisher Criterion.

Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects.

Texture is an important feature that aids in identifying objects of interest or region of interest irrespective of the source of the image. In this paper, a novel and simple isopattern-based texture feature is introduced. Spatial gray scale dependencies represented by bit plane is analyzed for specific patterns and are accumulated in bins. These are scaled by half-normal weighting function to provide isopattern texture feature. The ability of this texture feature in capturing textural variations of the images despite the presence of illumination, scale and rotation is demonstrated by conducting texture analysis on Brodatz, OuTex texture datasets and its classification accuracy on Kylberg dataset. The results of these two experimentation indicate that the proposed textural feature picks variation in texture significantly and has a better texture classification accuracy of 98.26% when compared with the state-of-the-art features like Gabor, GLCM and LBP.

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

Abstract Computerized texture analysis characterizes spatial patterns of image intensity, which originate in the structure of tissues. However, a number of texture descriptors also depend on local average image intensity and/or contrast. This variations, known as image nonuniformity (inhomogeneity) artefacts often occur, e.g. in MRI. Their presence may lead to errors in tissue description. This unwanted effect is explained in this paper using statistical texture descriptors applied for MRI slices of a normal and fibrotic liver. To reduce the errors, correction of image spatial nonuniformity prior to texture analysis is performed. The issue of sensitivity of popular texture parameters to image nonuniformities is discussed. It is illustrated by classification examples of natural Brodatz textures, digitally modified to account for inhomogeneities – modeled as smooth variations of image intensity and contrast. A set of texture features is identified which represent certain immunity to image inhomogeneities.

Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.

Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5×5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches.

Résumé : En télédétection, la texture facilite l’identification des classes de surfaces sur des critères de similitude d’organisation spatiale des pixels. Les méthodes d’analyse texturale utilisées en télédétection et en traitement d’image en général sont principalement proposées pour extraire la texture dans une seule bande à la fois. Pour les images multispectrales, ceci revient à extraire la texture dans chaque bande spectrale séparément. Cette stratégie ignore la dépendance qui existe entre la texture des différentes bandes (texture inter-bande) qui peut être une source d’information additionnelle aux côtés de l’information texturale classique intra-bande. La prise en charge de la texture multibande (intra- et inter-bande) engendre une complexité calculatoire importante. Dans sa recherche de solution pour l’analyse de la texture multibande, ce projet de thèse revient vers les aspects fondamentaux de l’analyse de la texture, afin de proposer un modèle de texture qui possède intrinsèquement une complexité calculatoire réduite, et cela indépendamment de l’aspect multibande de la texture. Une solution pour la texture multibande est ensuite greffée sur ce nouveau modèle, de manière à lui permettre d’hériter de sa complexité calculatoire réduite. La première partie de ce projet de recherche introduit donc un nouveau modèle l’analyse de texture appelé modèle d’unité texturale compacte (en anglais : Compact Texture Unit, C-TU). Le C-TU prend comme point de départ le modèle de spectre de texture et propose une réduction significative de sa complexité. Cette réduction est atteinte en proposant une solution générale pour une codification de la texture avec la seule information d’occurrence, sans l’information structurelle. En prenant avantage de la grande efficacité calculatoire du modèle de C-TU développé, un nouvel indice qui analyse la texture multibande comme un ensemble indissociable d’interactions spatiales intra- et inter-bandes est proposé. Cet indice, dit C-TU multibande, utilise la notion de voisinage multibande afin de comparer le pixel central avec ses voisins dans la même bande et avec ceux des autres bandes spectrales. Ceci permet à l’indice de C-TU multibande d’extraire la texture de plusieurs bandes simultanément. Finalement, une nouvelle base de données de textures couleurs multibandes est proposée, pour une validation des méthodes texturales multibandes. Une série de tests visant principalement à évaluer la qualité discriminante des solutions proposées a été conduite. L’ensemble des résultats obtenus dont nous faisons rapport ici confirme que le modèle de C-TU proposé ainsi que sa version multibande sont des outils performants pour l’analyse de la texture en télédétection et en traitement d’images en général. Les tests ont également démontré que la nouvelle base de données de textures multibande possède toutes les caractéristiques nécessaires pour être utilisée en validation des méthodes de texture multibande. // Abstract : In multispectral images, texture is typically extracted independently in each band using existing grayscale texture methods. However, reducing texture of multispectral images into a set of independent grayscale texture ignores inter-band spatial interactions which can be a valuable source of information. The main obstacle for characterizing texture as intra- and inter-band spatial interactions is that the required calculations are cumbersome. In the first part of this PhD thesis, a new texture model named the Compact Texture Unit (C-TU) model was proposed. The C-TU model is a general solution for the texture spectrum model, in order to decrease its computational complexity. This simplification comes from the fact that the C-TU model characterizes texture using only statistical information, while the texture spectrum model uses both statistical and structural information. The proposed model was evaluated using a new monoband C-TU descriptor in the context of texture classification and image retrieval. Results showed that the monoband C-TU descriptor that uses the proposed C-TU model provides performances equivalent to those delivered by the texture spectrum model but with much more lower complexity. The calculation efficiency of the proposed C-TU model is exploited in the second part of this thesis in order to propose a new descriptor for multiband texture characterization. This descriptor, named multiband C-TU, extracts texture as a set of intra- and inter-band spatial interactions simultaneously. The multiband C-TU descriptor is very simple to extract and computationally efficient. The proposed descriptor was compared with three strategies commonly adopted in remote sensing. The first is extracting texture using panchromatic data; the second is extracting texture separately from few newbands obtained by principal components transform; and the third is extracting texture separately in each spectral band. These strategies were applied using cooccurrence matrix and monoband compact texture descriptors. For all experiments, the proposed descriptor provided the best results. In the last part of this thesis, a new color texture images database is developed, named Multiband Brodatz Texture database. Images from this database have two important characteristics. First, their chromatic content, even if it is rich, does not have discriminative value, yet it contributes to form texture. Second, their textural content is characterized by high intra- and inter-band variation. These two characteristics make this database ideal for multiband texture analysis without the influence of color information.

This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.

The exponential growth of digital image data has created a great demand for effective and efficient scheme and tools for browsing, indexing and retrieving images from a collection of large image databases. To address such a demand, this paper proposes a new content based image retrieval technique with orthogonal polynomials model. The proposed model extracts texture features that represent the dominant directions, gray level variations and frequency spectrum of the image under analysis and the resultant texture feature vector becomes rotation and scale invariant. A new distance measure in the frequency domain called Deansat is proposed as a similarity measure that uses the proposed feature vector for efficient image retrieval. The efficiency of the proposed retrieval technique is experimented with the standard Brodatz, USC-SIPI and VisTex databases and is compared with Discrete Cosine Transform (DCT), Tree Structured Wavelet Transform (TWT) and Gabor filter based retrieval schemes. The experimental results reveal that the proposed method outperforms well with less computational cost.