texture features

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Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Keywords—Ancient digitized document images, Texture, Mul- tiresolution, Noise, Enhancement, Non-local means, Superpixel. I. I NTRODUCTION In the context of the DIGIDOC project (Document Image diGitisation with Interactive DescriptiOn Capability) 1 , we are interested in simplifying and improving the archiving, pro- cessing, comparison, and indexing of ancient digitized books collected from the Gallica digital library 2 . Indeed, various and numerous problems may arise for automated analysis of an- cient document images. Their segmentation and/or characteri- zation has to be sophisticated due to their particularities, such as noise and degradation, and the great variability of the page layout: complicated layout, random alignment, overlapping ob- ject boundaries, and the superimposition of information layers (stamps, handwritten notes, noise, back-to-front interference, etc.). Due to the nature and specifications of such documents (cf. Figure 2(a)), existing approaches (e.g. XY-CUT [1]) based on a priori knowledge, such as the repetitiveness of document structure in a corpus, are not effective. Thus, in the context of ancient document images, various aspects of the texture features are investigated in order to assist the analysis of the images by characterizing a document image layout by a
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Gray-level discretization impacts reproducible MRI radiomics texture features

Gray-level discretization impacts reproducible MRI radiomics texture features

We used two independent and very different datasets for external validation, two pairs of readers independently delineating the datasets, computer simulations of manual delineation variability, two delineation softwares to get rid of the delineation software confounding factor, and two radiomics softwares for which the calculation method of gray-level co-occurrence matrices and other derived matrices was slightly different. The purpose was to determine whether our results applied to different data, quantification methods, or organs. The fixed bin size method of gray-level discretization consistently provided less variable results. There are currently no specific guidelines to address the choice of the best bin width or bin number. Available studies use bin numbers varying from 8 to 1000, as suggested by the IBSI [ 5 , 14 ], or bin widths from 1 to 75 [ 14 , 18 ]. This allows for differing ranges of signal intensity in ROIs, while still keeping the texture features informative and comparable between lesions. The ideal choice probably depends on the target lesions and the wish to enhance coarse or fine textures. We tested a large panel of bin widths and bin numbers to consider the largest possible choices and not limit our conclusions to a selected range of bin widths and/or numbers.
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Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

Efficient colour texture image retrieval by combination of colour and texture features in wavelet domain

Introduction: Content-based image retrieval (CBIR) is an active research field in pattern recognition and computer vision. Color and texture are two important features that are used in CBIR. Using the combination of both features provides better performance than that of color or texture alone. For example, in [1], red, green and blue (RGB) channels of color images are treated as three respective pseudo gray-level images and Gabor filters are applied on these three images to extract features. In our previous work [5], texture features are constructed from the AC coefficients of Discrete Cosine Transform (DCT) and color features are constructed from the DC coefficients. As wavelet is widely used as an efficient tool for extracting features, some researchers have presented image retrieval methods based on wavelet in recent years. So in [2], color features are represented by 2D histogram of CIE Lab chromaticity coordinates and texture features are extracted by using Discrete Wavelet Frames (DWF) analysis. In [3], RGB images were firstly transformed into HSV model. The color feature is represented by the autocorrelogram of wavelet coefficients extracted from Hue and Saturation components, and the first and second moments of the BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) for each subband of Value component is used as texture feature. A recent approach is presented in [4], in which, the wavelet coefficients in RGB color channels are modeled by multivariate Laplace distribution and Student-t distribution. Other than mentioned methods, this paper presents a new method for color texture image retrieval combining color and texture features in wavelet domain. This method constructs the color and texture features from the coefficients of some subbands of wavelet transform.
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COLOR TEXTURED IMAGE RETRIEVAL BY COMBINING TEXTURE AND COLOR FEATURES

COLOR TEXTURED IMAGE RETRIEVAL BY COMBINING TEXTURE AND COLOR FEATURES

Another aspect of analyzing color images is to process them separately, that means to transform the color image into luminance and chrominance components and color and texture feature are extracted separately. For example, in [5], RGB image was firstly transformed into HSV image. The autocorrelogram of wavelets coefficients extracted from Hue and Saturation components is used as color feature, and the first and second moments of the BDIP (block difference of inverse probabilities) and BVLC (block variation of local correlation coefficients) for each subband of Value component is used as texture feature. In [6], color feature is represented by 2D histogram of CIE Lab chromaticity coordinates and texture features are extracted using Discrete Wavelet Frames (DWF) analysis.
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Application of an Incremental SVM Algorithm for On-line Human Recognition from Video Surveillance Using Texture and Color Features

Application of an Incremental SVM Algorithm for On-line Human Recognition from Video Surveillance Using Texture and Color Features

Second, according to our specific application, the most ef- ficient feature set is determined. Since the analysis of three different parts of the body are more accurate than the one of the whole body, segmentation of each body into three parts (Head, Top, and Bottom) has been implemented firstly. Considering these three parts of each silhouette, color and texture features are extracted in the video sequences and the Wholeset database with 93 features is formed. Then three feature selection meth- ods are compared to reduce the feature space and obtain the optimal set. These feature selection methods are chosen to rep- resent the two different methods (filter and wrapper). Finally, four sets of database are obtained. The most satisfied result is based on CFS, which consists of 40 features for each image.
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Texture Analysis based Detection and Classification of Surface Features on Ageing Infrastructure Elements

Texture Analysis based Detection and Classification of Surface Features on Ageing Infrastructure Elements

generated for each pixel within the original image, I, where f indicates the index of the vector element, while a and b indicate the spatial coordinates of a pixel. The GLCM is a matrix of frequency values that combinations of pixel intensities appear in some specific spatial arrangement within an image or sub-image. In this paper, the GLCM is generated for a sub-image that is attained through a sliding window, SW, of size N-pixel x N-pixel with centre positioned at (a,b) at any stage of the convolution throughout the overall image. There are four texture features calculated from the GLCM, namely: homogeneity, contrast, entropy and Angular Second Moment (ASM). Some of these features relate to certain texture characteristics in the image such as homogeneity or contrast. Other features describe aspects such as image complexity or the transition of pixel intensity values. However, in spite of each of the aforementioned features containing some degree of information about the texture characteristics of the image, it is difficult to establish which textural trait is represented by each feature.
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Shape-based Invariant Texture Indexing

Shape-based Invariant Texture Indexing

3 Invariant Texture Descriptors The goal of this section is to define texture features that are both invariant to some geometric changes and discriminative enough. These features will be obtained from the shapes of the topographic map and it is there- fore quite natural to consider the classical invariant shape moments, whose definition is recalled in this sec- tion. Observe that such shape moments are already used for image registration in ( Monasse , 1999 ) and tex- ture recognition in ( Hamdan and Larson , 2002 ). How- ever, it is well known that these moments rapidly loose robustness as their order increases, so that only a small number of these can be used to analyze real world tex- tures. In order to enrich the proposed analysis, we take into account multi-scale shape dependencies on the to- pographic map. The resulting features are invariant to any local contrast change. Last, we suggest some con- trast information that can be extracted from the shapes and will allow to improve the discriminative power of the proposed analysis scheme while still being invariant to local affine contrast changes.
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Analyse de texture hyperspectrale par modélisation markovienne

Analyse de texture hyperspectrale par modélisation markovienne

Abstract: Texture analysis has been widely investigated in monospectral and multispectral imagery domain. In the same time, new image sensors with a large number of bands (more than 10) have been designed. They are able to provide images with both fine spectral and spatial sampling, called hyperspectral images. The aim of this work is to perform a joint texture analysis in both discrete spaces. To achieve this goal, we have a probabilistic vec- torial texture modeling, with Gauss-Markov Random Field. The MRF parameters allow for the characterisation of different hyperspectral textures. A likely application of this work be- ing the classification of urban areas, which are not well characterized by radiometry alone, we use these parameters as new features is a Maximum Likelihood classification algorithm. The results obtain on AVIRIS hyperspectral images show better classifications when using texture information.
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A speckle-texture image generator

A speckle-texture image generator

More effects can be achieved by modulating the texturing function with respect to the point location, eventually corrupting the point location by an other noise function. Such a technique can allow a better control of the speckle spots by using, for instance, a sine function with a corrupted phase. Pixar ∗ has successfully implemented this approach in its Renderman software for producing leopard texture, marble, etc.

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Synthèse de texture par décomposition parcimonieuse contrainte

Synthèse de texture par décomposition parcimonieuse contrainte

−1 u , où σ v désigne une permutation telle que les pixels de v soient ordonnés : v σ v (1) ≤ · · · ≤ v σ v (i) ≤ v σ v (i+1) ≤ . . . (8) Le terme de spectre mesure l’écart entre les spectres de u et de u 0. L’utilisation du spectre en synthèse de texture est détaillée dans [ 5 ]. Dans notre cas, il permet de conser- ver à la fois les très basses fréquences (non capturées par des patchs, trop petits) et les très hautes fréquences (at- ténuées par la décomposition des patchs).

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Texture alignment in simple shear

Texture alignment in simple shear

Computer simulations and specifically discrete element simulations [2] are an important tool for exploring the fundamental behaviours of granular flows, flow regimes, phase transitions, fluctuations etc. However computational require- ments set strong limitations to the size of discrete element models. The much simpler and (where applicable) much more efficient continuum models for granu- lar flows are valid when the typical grain size is much smaller than characteristic structural dimensions e.g. the outlet size in silo flows. Cosserat continuum the- ory [3] considers some of the salient features of the discrete microstructure (e.g. grain size, relative rotation between microstructure and the continuum) within the framework of a continuum theory. Such a theory fits between detailed dis- crete theories and the usual continuum theory.
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Texture alignement in simple shear

Texture alignement in simple shear

Computer simulations and specifically discrete element simulations [2] are an important tool for exploring the fundamental behaviours of granular flows, flow regimes, phase transitions, fluctuations etc. However computational require- ments set strong limitations to the size of discrete element models. The much simpler and (where applicable) much more efficient continuum models for granu- lar flows are valid when the typical grain size is much smaller than characteristic structural dimensions e.g. the outlet size in silo flows. Cosserat continuum the- ory [3] considers some of the salient features of the discrete microstructure (e.g. grain size, relative rotation between microstructure and the continuum) within the framework of a continuum theory. Such a theory fits between detailed dis- crete theories and the usual continuum theory.
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Excitations avec texture de spin et de pseudospin dans le graphène

Excitations avec texture de spin et de pseudospin dans le graphène

 , 1 ) (3.47d) So the ground state at ˜ ν = 1 has |S, 0 fully occupied and the three other levels are empty. When the filling factor increases to 1 .2, the extra electrons are added to |S, 1. Hence, the crystal phase contains the coherences between the valleys |K and |K  , and between the orbitals |0 and |1. Indeed, our numerical calculations show that the crystal contains orbital skyrmion texture in both valleys. In each valley, the orbital skyrmion texture is similar to that of Fig. 3.7 in real space and Fig. 3.8 in guiding center representation. The valley pseudo-spin textures are shown in Figs. 3.9 and 3.10. The valley pseudo-spin texture in orbital 0 is almost uniform. On the other hand, the valley pseudo-spin texture in orbital 1 indicates a Wigner crystal.
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Learned features versus engineered features for semantic video indexing

Learned features versus engineered features for semantic video indexing

Several late fusions of features of the same type were also considered. Figure 2 shows the performance of several types of learned features. The first entry is the combination of all engineered features showed as a baseline. We can immediately see that almost all semantic features perform similarly to or better than the baseline and therefore significantly better than any individ- ual engineered feature. We can also observe than combinations of semantic features perform even better. Considering the caffe output and internal layers, the best choice is fc6 which is very close to fc7. The final output layer is less good and fc5 is even less good. It is interesting to notice that the performance of the Xerox Fisher vector based semantic features is very close to the performance of the final output of the pre-trained caffe network while both use very similar training data (ILSVRC10 and ILSVRC12 respectively). The features corresponding to the conceptual feedback perform better but the Xerox semantic features were included in their production.
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Local texture synthesis: A static texture coding algorithm fully compatible with HEVC

Local texture synthesis: A static texture coding algorithm fully compatible with HEVC

This paper presents a new algorithm, called Local Texture Synthesis (LTS), for static textures coding. This algorithm bridges the gap between texture synthesis and video coding in the sense that the synthesis is done during the encoding process such that the decoder can independently decode the bitstream. The objective of LTS is to achieve higher bitrate saving using texture synthesis without a need to modify the existing coding standard (HEVC decoder). The algorithm is generic regarding the encoder type and the texture synthesizer. For the purpose of experimental comparison, we present one instance of this algorithm using Markov Random Fields based texture synthesizer.
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Synthèse de texture par décomposition parcimonieuse contrainte

Synthèse de texture par décomposition parcimonieuse contrainte

Gabriel.Peyre@ceremade.dauphine.fr Résumé – Cet article aborde la synthèse de texture par une approche variationnelle. À partir d’un échantillon de la texture à synthétiser, nous définissons une énergie non-convexe composée de termes de pénalité associés à des contraintes statistiques. Ces contraintes portent sur l’histogramme et le spectre de l’image, ainsi que sur une décomposition de ses patchs dans un dictionnaire appris au préalable. Le problème de synthèse est alors transformé en un problème de minimisation, pour lequel nous proposons un algorithme. Les minima locaux ainsi obtenus sont de nouvelles synthèses de la texture originale. Des expériences numériques illustrent les résultats de cette approche.
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Listening to features

Listening to features

task of inverting songs from the Million Song Dataset. What makes this task harder is twofold. First, that features are irregularly spaced in the temporal domain according to an onset-based segmentation. Second the exact method used to compute these features is unknown, although the features for new audio can be computed using their API as a black-box. In this paper, we detail these difficulties and present a framework to nonetheless attempting such synthesis by concatenating audio samples from a training dataset, whose features have been computed beforehand. Samples are
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Optimized Tile-Based Texture Synthesis

Optimized Tile-Based Texture Synthesis

6 C ONCLUSION AND F UTURE W ORK We have presented a novel optimization-based technique for tile- based texture synthesis. Our results for both texture synthesis and image tiling are comparable to state-of-the-arts. We define a pat- tern repetitive principle that allows us to derive new ω-tile sets from the existing one. An optimized sample patches selection algorithm based on GA is used to improve the quality of the whole tile set. This framework is also fit for quality improvement of Wang-tile based texture synthesis [4]. Our technique can be nicely applied in the environment where real-time texture synthesis is needed, such as 3D games and real-time virtual reality systems, while the local region-growing methods such as image quilting, graph-cut and tex- ture optimization are not applicable (need seconds or minutes to generate an image).
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Extraction de caractéristiques de texture pour la classification d'images satellites

Extraction de caractéristiques de texture pour la classification d'images satellites

gris. La figure 11 met en évidence les différents extremums locaux des trois textures de la figure 10. Nous verrons plus loin ce qu’est exactement un extremum local, néanmoins nous pouvons déjà dire qu’un pixel est un minimum local (respectivement maximum local) si les niveaux de gris de ses voisins sont tous supérieurs (respectivement tous inférieurs) au sien. Dans la figure 11, le voisinage utilisé est composé des 8 voisins d’un pixel. Les minimums locaux sont représentés en noir et les maximums locaux sont en blanc. Il apparaît clairement que cette simple information des extremums locaux permet de mettre en évidence certaines caractéristiques des textures originales. Dans le premier cas, la disposition spatiale des pixels noirs et blancs permet de retrouver la structure directionnelle de départ. L’aspect aléatoire de la troisième texture est caractérisé par le fait qu’il y a un grand nombre de pixels extremums noirs à côté de pixels blancs, répartis sur toute l’image. Il est plus difficile de retrouver la structure de la deuxième texture juste avec l’image des extremums. Par contre, le nombre et la disposition de ces derniers dans l’image permettent de la distinguer totalement des deux autres.
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Perception de la texture en bouche : une approche biomimétique

Perception de la texture en bouche : une approche biomimétique

Finally, interestingly, the use of fluorescent microbeads as markers enables measurements of optically opaque liquids such as dairy food products. Our system has indeed been successfully used to probe rheological prop- erties of yogurts and chocolate mousses (not shown). As a model dairy, we have used a mixture of milk and xanthan that exhibits, as many food products, a shear thinning behavior. We show that in this case the de- flections of papillae are weakly dependent on the shear rate. From a biological point of view, our measurements thus imply that the sensory input, which is most likely related to the bending of papillae, is weakly dependent on the in-mouth shear stress. Texture perception of food products appears therefore robust across varying chew- ing conditions. Such a feature echoes tactile perception mechanisms in other contexts. In human tactile digital perception for instance, it is known that such robustness (to variations in the finger/object frictional properties for example...) provides the human hand the ability to maintain a stable grasp at all times [33]. Similarly, for rodents who use their facial whiskers to detect objects in their immediate vicinity, the whiskers vibrations elicited upon contact provide a detection mechanism that is robust over a large range of exploration conditions [34]. The present work suggests that the in-mouth texture perception of food products that have shear thinning rheological properties is equally robust to variations in the exploratory chewing conditions.
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