Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods. (C) 2011 Elsevier Ltd. All rights reserved.

Fractal descriptors based on Fourier spectrum applied to texture analysis

This work proposes the development and study of a novel technique lot the generation of fractal descriptors used in texture analysis. The novel descriptors are obtained from a multiscale transform applied to the Fourier technique of fractal dimension calculus. The power spectrum of the Fourier transform of the image is plotted against the frequency in a log-log scale and a multiscale transform is applied to this curve. The obtained values are taken as the fractal descriptors of the image. The validation of the proposal is performed by the use of the descriptors for the classification of a dataset of texture images whose real classes are previously known. The classification precision is compared to other fractal descriptors known in the literature. The results confirm the efficiency of the proposed method. (C) 2012 Elsevier B.V. All rights reserved.

Using the Hard, Randy, and Rittler Test to Evaluate Color Vision in Capuchins (Cebus libidinosus)

The identification of color vision types in primates is fundamental to understanding the evolution and biological function of color perception. The Hard, Randy, and Rittler (HRR) pseudoisochromatic test categorizes human color vision types successfully. Here we provide an experimental setup to employ HRR in a nonhuman primate, the capuchin (Cebus libidinosus), a platyrrhine with polymorphic color vision. The HRR test consists of plates with a matrix composed of gray circles that vary in size and brightness. Differently colored circles form a geometric shape (X, O, or Delta) that is discriminated visually from the gray background pattern. The ability to identify these shapes determines the type of dyschromatopsy (deficiency in color vision). We tested six capuchins in their own cages under natural sunlight. The subjects chose between two HRR plates in each trial: one with the gray pattern only and the other with a colored shape, presented on the left or right side at random. We presented the test 40 times and calculated the 95 % confidence limits for chance performance based on the binomial test. We also genotyped all subjects for exons 3 and 5 of the X-linked opsin genes. The HRR test diagnosed two subjects as protan dichromats (missing or defective L-cone), three as deutan dichromats (missing or defective M-cone), and one female as trichromat. Genetic analysis supported the behavioral data for all subjects. These findings show that the HRR test can be applied to diagnose color vision in nonhuman primates.

EDGE-PRESERVING SPECKLE TEXTURE REMOVAL BY INTERFERENCE-BASED SPECKLE FILTERING FOLLOWED BY ANISOTROPIC DIFFUSION

Ultrasonography has an inherent noise pattern, called speckle, which is known to hamper object recognition for both humans and computers. Speckle noise is produced by the mutual interference of a set of scattered wavefronts. Depending on the phase of the wavefronts, the interference may be constructive or destructive, which results in brighter or darker pixels, respectively. We propose a filter that minimizes noise fluctuation while simultaneously preserving local gray level information. It is based on steps to attenuate the destructive and constructive interference present in ultrasound images. This filter, called interference-based speckle filter followed by anisotropic diffusion (ISFAD), was developed to remove speckle texture from B-mode ultrasound images, while preserving the edges and the gray level of the region. The ISFAD performance was compared with 10 other filters. The evaluation was based on their application to images simulated by Field II (developed by Jensen et al.) and the proposed filter presented the greatest structural similarity, 0.95. Functional improvement of the segmentation task was also measured, comparing rates of true positive, false positive and accuracy. Using three different segmentation techniques, ISFAD also presented the best accuracy rate (greater than 90% for structures with well-defined borders). (E-mail: fernando.okara@gmail.com) (C) 2012 World Federation for Ultrasound in Medicine & Biology.

The effects of extrusion conditions and the addition of volatile compounds and flavour enhancers to corn grits on the retention of the volatile compounds and texture of the extrudates

Corn grits that were supplemented with isovaleraldehyde, ethyl butyrate, butyric acid and flavour enhancers were extruded under different processing conditions. Volatile compounds retained in the extrudates were isolated by dynamic headspace and analysed using gas chromatographymass spectrometry. The expansion ratio, density and cut force to break down the extrudates were evaluated and aroma intensity was assessed using a multisample difference test. Butyric acid showed the greatest retention (96.4%), regardless of the extrusion conditions. All compounds were better retained when samples were extruded at 20% feed moisture and 90 degrees C processing temperature (2.981.0%), conditions that also resulted in greater aromatic intensity (moderate to moderate-strong intensity). The addition of volatile compounds reduced the expansion ratio and cut force, whereas the addition of flavour enhancers increased the expansion ratio but reduced ethyl butyrate and butyric acid retention.

Structure and anomalous solubility for hard spheres in an associating lattice gas model

In this paper we investigate the solubility of a hard-sphere gas in a solvent modeled as an associating lattice gas. The solution phase diagram for solute at 5% is compared with the phase diagram of the original solute free model. Model properties are investigated both through Monte Carlo simulations and a cluster approximation. The model solubility is computed via simulations and is shown to exhibit a minimum as a function of temperature. The line of minimum solubility (TmS) coincides with the line of maximum density (TMD) for different solvent chemical potentials, in accordance with the literature on continuous realistic models and on the "cavity" picture. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4743635]

Texture analysis of high resolution MRI allows discrimination between febrile and afebrile initial precipitating injury in mesial temporal sclerosis

A computational pipeline combining texture analysis and pattern classification algorithms was developed for investigating associations between high-resolution MRI features and histological data. This methodology was tested in the study of dentate gyrus images of sclerotic hippocampi resected from refractory epilepsy patients. Images were acquired using a simple surface coil in a 3.0T MRI scanner. All specimens were subsequently submitted to histological semiquantitative evaluation. The computational pipeline was applied for classifying pixels according to: a) dentate gyrus histological parameters and b) patients' febrile or afebrile initial precipitating insult history. The pipeline results for febrile and afebrile patients achieved 70% classification accuracy, with 78% sensitivity and 80% specificity [area under the reader observer characteristics (ROC) curve: 0.89]. The analysis of the histological data alone was not sufficient to achieve significant power to separate febrile and afebrile groups. Interesting enough, the results from our approach did not show significant correlation with histological parameters (which per se were not enough to classify patient groups). These results showed the potential of adding computational texture analysis together with classification methods for detecting subtle MRI signal differences, a method sufficient to provide good clinical classification. A wide range of applications of this pipeline can also be used in other areas of medical imaging. Magn Reson Med, 2012. (c) 2012 Wiley Periodicals, Inc.

Influence of milk type and addition of passion fruit peel powder on fermentation kinetics, texture profile and bacterial viability in probiotic yoghurts

The effect of the addition of passion fruit peel powder (PFPP) on the fermentation kinetics and texture parameters, post-acidification and bacteria counts of probiotic yoghurts made with two milk types were evaluated during 28 days of storage at 4 degrees C. Milks were fermented by Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus (CY340), and one strain of probiotic bacteria: Lactobacillus acidophilus (L10 and NCFM), Bifidobacterium animalis subsp. lactis (8104 and HN019). The addition of PFPP reduced significantly fermentation time of skim milk co-fermented by the strains L10, NCFM and HN019. At the end of 28-day shelf-life, counts of B. lactis Bl04 were about 1 Log CFU mL(-1) higher in whole yoghurt fermented with PFPP regarding its control but, in general, the addition of PFPP had less influence on counts than the milk type itself. The titratable acidity in yoghurts with PFPP was significantly higher than in their respective controls, and in skim yoghurts higher than in the whole ones. The PFPP increased firmness, consistency (except for the NCFM strain of L acidophilus) and cohesiveness of all skim yoghurts. The results point out the suitability of using passion fruit by-product in the formulation of both skim and whole probiotic yoghurts. (C) 2012 Elsevier Ltd. All rights reserved.

Hard Thermal Loops in the n-Dimensional phi(3) Theory

We derive a closed-form result for the leading thermal contributions which appear in the n-dimensional I center dot (3) theory at high temperature. These contributions become local only in the long wavelength and in the static limits, being given by different expressions in these two limits.

Influence of adenotonsillectomy on hard palate dimensions

Objective: To evaluate hard palate width and height in mouth-breathing children pre- and post-adenotonsillectomy. Methods: We evaluated 44 children in the 3-6 year age bracket, using dental study casts in order to determine palatal height, intercanine width, and intermolar width. The children were divided into two groups: nasal breathing (n = 15) and mouth breathing (n = 29). The children in the latter group underwent adenotonsillectomy. The study casts were obtained prior to adenotonsillectomy, designated time point 1(11), at 13 months after adenotonsillectomy (T2), and at 28 months after adenotonsillectomy (13). Similar periods of observation were obtained for nasal breathing children. Results: At T1, there was a significantly lower intercanine width in mouth breathing children; intermolar width and palate height were similar between groups. After surgery, there was a significant increase in all the analyzed parameters in both groups, probably due to facial growth. Instead, the increase in intercanine width was substantially more prominent in mouth breathing children than in nasal breathing children, and the former difference failed in significance after the procedure. Conclusions: There were no significant differences between the nasal-breathing and mouth-breathing children in terms of intermolar width and palatal height prior to or after tonsillectomy. Although intercanine width was initially narrower in the mouth-breathing children, it showed normalization after the surgical procedure. These results confirm that the restoration of nasal breathing is central to proper occlusal development. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

Texture descriptor based on partially self-avoiding deterministic walker on networks

Texture image analysis is an important field of investigation that has attracted the attention from computer vision community in the last decades. In this paper, a novel approach for texture image analysis is proposed by using a combination of graph theory and partially self-avoiding deterministic walks. From the image, we build a regular graph where each vertex represents a pixel and it is connected to neighboring pixels (pixels whose spatial distance is less than a given radius). Transformations on the regular graph are applied to emphasize different image features. To characterize the transformed graphs, partially self-avoiding deterministic walks are performed to compose the feature vector. Experimental results on three databases indicate that the proposed method significantly improves correct classification rate compared to the state-of-the-art, e.g. from 89.37% (original tourist walk) to 94.32% on the Brodatz database, from 84.86% (Gabor filter) to 85.07% on the Vistex database and from 92.60% (original tourist walk) to 98.00% on the plant leaves database. In view of these results, it is expected that this method could provide good results in other applications such as texture synthesis and texture segmentation. (C) 2012 Elsevier Ltd. All rights reserved.

Texture analysis of cold rolled and annealed aluminum alloy produced by twin-roll casting

A 7.4 mm thick strip of 3003 aluminum alloy produced by the industrial twin-roll casting (TRC) process was homogenized at 500 °C for 12 hours, after which it was cold rolled in two conditions: 1) to reduce the strip's thickness by 67%, and 2) to reduce it by 91%. The alloy was annealed at 400 °C for 1 hour in both conditions. The results revealed that a rotated cube texture, the {001}<110> component, predominated in the as-cast condition and was transformed into brass, copper and S type textures during the cold rolling process. There was practically no difference between the deformation textures at the two thickness reductions.

Texture analysis by multi-resolution fractal descriptors

This work proposes a novel texture descriptor based on fractal theory. The method is based on the Bouligand- Minkowski descriptors. We decompose the original image recursively into four equal parts. In each recursion step, we estimate the average and the deviation of the Bouligand-Minkowski descriptors computed over each part. Thus, we extract entropy features from both average and deviation. The proposed descriptors are provided by concatenating such measures. The method is tested in a classification experiment under well known datasets, that is, Brodatz and Vistex. The results demonstrate that the novel technique achieves better results than classical and state-of-the-art texture descriptors, such as Local Binary Patterns, Gabor-wavelets and co-occurrence matrix.

Combining fractal and deterministic walkers for texture analysis and classification

In this paper,we present a novel texture analysis method based on deterministic partially self-avoiding walks and fractal dimension theory. After finding the attractors of the image (set of pixels) using deterministic partially self-avoiding walks, they are dilated in direction to the whole image by adding pixels according to their relevance. The relevance of each pixel is calculated as the shortest path between the pixel and the pixels that belongs to the attractors. The proposed texture analysis method is demonstrated to outperform popular and state-of-the-art methods (e.g. Fourier descriptors, occurrence matrix, Gabor filter and local binary patterns) as well as deterministic tourist walk method and recent fractal methods using well-known texture image datasets.

Dynamic texture segmentation based on deterministic partially self-avoiding walks

Recently there has been a considerable interest in dynamic textures due to the explosive growth of multimedia databases. In addition, dynamic texture appears in a wide range of videos, which makes it very important in applications concerning to model physical phenomena. Thus, dynamic textures have emerged as a new field of investigation that extends the static or spatial textures to the spatio-temporal domain. In this paper, we propose a novel approach for dynamic texture segmentation based on automata theory and k-means algorithm. In this approach, a feature vector is extracted for each pixel by applying deterministic partially self-avoiding walks on three orthogonal planes of the video. Then, these feature vectors are clustered by the well-known k-means algorithm. Although the k-means algorithm has shown interesting results, it only ensures its convergence to a local minimum, which affects the final result of segmentation. In order to overcome this drawback, we compare six methods of initialization of the k-means. The experimental results have demonstrated the effectiveness of our proposed approach compared to the state-of-the-art segmentation methods.