Abstract. Texture classification is one of the basic images processing tasks. In this paper we present somenumericalcharacteristics to the images analysis and processing. It can be used at the solving of images classification problems, their recognition, problems of remote sounding, biomedical images analysis, geological researches.
The numerical simulations have someof the experimental observed characteristicsof the malonic acid-phenol BZ oscillating reaction, like an induction time enlargement and an increasing oscillatory reaction time as the initial phenol concentration increases. Also, the burst firing appears, and it is the most interesting result (inset in Figure 3). This qualitative agreement, between the experiments and the numerical simulations, is in favor of the hypothesized phenol-quinone redox cycle. However, it is necessary to confirm these ideas, in future works, by determining the experimental rate constant values, and by including, or deleting, some reactions. Also, a chromatographic and electron paramagnetic resonance (EPR) spectroscopy studies would be particularly useful to find the specific intermediaries.
independently in the wake, usually leading to a non-cylindrical structure (Sarmast et al. 2014, Whale et al. 2000). Different behavior occurs with three blades, as shown in Figure 9, where the wake structure is closest to a helical cylinder, even for hydrokinetic turbines near to the free surface. This kind of wake structure has been widely considered in the vortex theory which takes into account the Kutta-Joukowsky theorem to express the forces acting on a blade as a function of the bound circulation (Okulov and Sørensen 2008). For this reason, when the number of blades is increased, the wake geometry tends to helical vortex behavior. Concerning the deformity caused by the proximity between the top surface, it is noted that a disparities between X and Z direction are more prominent in the far wake ( Y 3 > D ). In addition, due to the free-slip condition on the top surface X bounds has lower TKE levels. In this sense, using as a parameter the difference of behavior in TKE and mean axial velocity between X and Z directions, it can be said that the spatial distortion caused by the river is mainly pronounced on the far wake, as in the near wake those quantities present a similar pattern and in the far wake there are some directional divergence.
Content based image retrieval (CBIR) is the task of searching digital images from a large database based on the extraction of features, such as color, texture and shape of the image. Most of the research in CBIR has been carried out with complete queries which were present in the database. This paper investigates utility of CBIR techniques for retrieval of incomplete and distorted queries. Studies were made in two categories of the query: first is complete and second is incomplete. The query image is considered to be distorted or incomplete image if it has some missing information, some undesirable objects, blurring, noise due to disturbance at the time ofimage acquisition etc. Color (hue, saturation and value (HSV) color space model) and shape (moment invariants and Fourier descriptor) features are used to represent the image. The algorithm was tested on database consisting of 1875 images. The results show that retrieval accuracy of incomplete queries is highly increased by fusing color and shape features giving precision of 79.87%. MATLAB ® 7.01 and its image processing toolbox have been used to implement the algorithm.
On Christmas 2011, a greeting card circulated on the Internet, wishing a happy new year to the friends of Inke Atelier. The image, released by electronic address, reproduced a watercolor painting on paper made by the artist Ricardo Inke, featuring a view of the city of Ouro Preto, more particularly a landscape of mountainous horizon, highlighting the Church of São Francisco de Assis. The parvis is at the bottom of the painting, while the temple is located on a small elevation, flanked by alleys and a few houses, in a perspective slightly moved to the right of the observer, from which one can see the imposing façade and some lateral walls leaving room for the rising mountains under the sky of Minas Gerais. In the background, the Itacolomi state park. It is a digital image that allows to glimpse or imagine the textureof the paper on which the watercolor colors partially conceal the lines of the original pencil sketch.
methods the computational cost is less and also provides satisfactory results comparable with that of the pixel-wise segmentation methods even if the objects are not segmented correctly. Some other CBIR systems   extract salient points (also known as interest points)  , which are locations in an image where there is a significant variation with respect to a chosen image feature. In salient point based methods, feature vector is created for each salient point and the selection of the number of salient points is very important. These representations enable a retrieval method to have a representation of different local regions of the image, and thus these images can be searched based on their local characteristics.
In this section, the methodology of using association rules for textureimage classification is presented. Texture images appear similar and there exist small variations in the structure and intensity values of the corresponding pixels. Thus, the gray-level textures pose some challenging problems for the use of association rule approach to capture local image attributes. Visually similar textures produce of the itemsets appearing in transaction get discarded because they do not satisfy the support criterion. This means that common attributes presented in images can not be recognized by the mining algorithm because the transactions of a specific pixels that belong to a given texture region do not have many items in common. The association rules based textureimage classification procedure which is proposed in this study is composed of 2 steps.
In the next step the arrays ofimage IDs (A, B and C) are sorted based on the number of appearances (ranking) ofimage IDs in each array. The number of repeating image IDs in the appropriate group of features indicates the similarity to the query image by various criteria. Because of a number of the coordinates and their variance there is a problem with the dominance of certain characteristics when the Euclidean distance between feature vectors is used as the only criterion for similarity in the initial search. This problem is overcome with the indication of similarity by various criteria in the proposed algorithm. After sorting, N2 = 15 image files with the highest number of appearances are selected from each group of features (A,B and C), and moved with a preserved rank order to a new set of arrays COLOR,TEXTURE and SHAPE respectively, as it is shown in Fig. 2.
A color image is a digital image that includes color information for each pixel. Although millions of colors can be defined in computer system, the colors that can be named by users are limited. Here a color image is taken as the input image and the input image shows how the pixel varies. First of all the image is segmented and each region is characterized by their texture and color. Here we are using the average HSV value as the color feature of a region. The average HSV value is then converted to a semantic color name i.e., the input image is converted to HSV space. Hue, saturation, and value (HSV) color spaces are often used by artists. "Hue" is what we normally think of as color. It is the attribute of a color by which we give it a name such as "red" or "blue". "Value" is another word for "lightness," the attribute of a color that makes it seem equivalent to some shade of gray between black and white. Saturation is a measure of how different a color appears from a gray of the same lightness. The hue value is uniformly quantized in to 10 base colors, red, orange, yellow, green, aqua, aquamarine, and blue, violet, purple and magenta from the HSV space we are taking H matrix. The value of H matrix varies from 0-255; from these values we are normalizing the output to 0 to 1.
The computational models were developed with the intention of predicting the velocity and temperature fields in the reduced-scale model above described. All CFD codes tested stands on the statement that all thermo-fluid problems are governed by the aforementioned principles of conservation. For the self-programmed code (CLIMA 3D), all the numerical techniques described for the solution of the exposed mathematical models were programmed in FORTRAN. In the following lines it will be presented and compared the generic characteristicsof each code. It is important to expose (Table 7) that the versions and acquisition date of the codes tested were quite different. Thus, the confidence of the user increases with the practice in each code. This experience is dependent of the acquisition date. So, the code CFX will present higher uncertainty in the numerical predictions, although its friendliness allows the quick development of computational simulations. Due to the deepest knowledge of the other codes, both PHOENICS and FLUENT secure some certainty on the numerical results.
Tables 1 and 2 present a quantitative evaluation of the segmentation results of Figures 4(a) and 5(a), respectively. The NPR index selects a value in the interval [ 0 , 1 ] . A score of 0 indicates that each pixel pair in the test image has an opposite relationship as every pair in the ground truth segmentations, whereas a score of 1 indicates that each pixel pair in the test image has the same relationship as every pair in the ground truth segmentations. The GCE index produces really valuable output in the range of [ 0 , 1 ] , where zero signifies no error. The VOI metric is nonnegative, with lower values indicating greater similarity. A comparison of Tables 1 and 2 shows that the uneven illumination, background, and texture cause several errors in areas similar to the rice planthopper color. The results illustrate that the OTSU method and FCM method with gray information can achieve lower segmentation performance. The proposed method and traditional MRF method achieved good results because the MRF model can better describe image features within a probabilistic framework. In addition, the combination of complementary color features with texturecharacteristics by the proposed algorithm can better describe the complexities of the acquisition environment of rice field; thus, the algorithm has better overall segmentation performance compared with the MRF algorithm.
Many objects in an image can be distinguished solely by their textures without any other information. There is no universal definition oftexture. Texture may consist ofsome basic primitives, and may also describe the structural arrangement of a region and the relationship of the surrounding regions [7,17,20]. In our approach we have used the statistic texture features using gray-level co-occurrence matrix (GLCM).So we developed a technique which captures color and texture features of sub-blocks of the image. For each sub-block cumulative histogram and statistic texture features using GLCM are determined. In  an integrated matching procedure based on MSHP principle is used to find the matching between query and target image. The target and query images are matched with integrated matching scheme based on MSHP principle. Weighted Euclidean distance is used as a similarity measure in retrieving similar images.
Currently, there are various kinds ofimage encryption techniques, including image-scrambling-based techniques, data- processing-based techniques, key-based encryption techniques, etc. Some algorithms are based on certain transformation rules. For instance, Shyu used random grids to accomplish the encryption of secret gray-scale and color images . Some algorithms are proposed according to the characteristicsof the image itself, such as Yuen’s proposal of a chaos-based joint image compression and encryption algorithm using discrete cosine transformation (DCT) and Secure Hash Algorithm-1 (SHA-1) . Combining the encryption with other data processing technologies, Hermassi introduced a new scheme based on joint compression and encryption using the Huffman code . Among the algorithms
Similarity comparison over a set of high-dimensional vectors is a slow operation and has become an obstacle for scaling up the content based approach to image retrieval. In our solution, we have adopted a scheme of locality sensitive hashing which guarantees a sub-linear searching time on the visual comparison. The image selection by the semantic comparison also helps to reduce the size of the data set for the hashing, which further shortens the time spent on the visual comparison. By such a two-staged retrieving strategy, time spent on the content based comparison can be confined within a user-tolerant range. From the experimental results, the longest query time was about 6 ms (see Table I) for the collection of 1514 images. So we believe that the solution has the potential to be scaled up to suit large image collections. Furthermore, the developed idea can be applied to other identification problems, such as the macromodeling issue in the signal integrity study .
During this period, there were numerous tours of musical bands, orchestras such the concerts of cords quartet “Transilvania”, of the folk music formation “Ciocârlia” and the participation in 2003 of the Helsinki Symphonic Orchestra at the “George Enescu Festival” where compositions by the most important composers from two countries, Jan Sibelius and George Enescu, were performed. Another musical event dedicated to George Enescu took place in 2007, in Turku, at the Philharmonic Concert Hall, on the occasion of the anniversary of 125 years from the birth of the Romanian composer
Neuro-Fuzzy edge detection was used widely in this last decades. In  authors propose an ANFIS model with 8-inputs and 1-output Neuro-Fuzzy system based in first-order-Sugano system. 2 triangular type membership functions are used for each input, and the output has a constant membership function. 256 rules were used with just one output. Authors use a Grid partition method on the contrary of subtractive clustering method. The Gray level image is used to detect edges, they firstly binarize the image, and then the binary image disintegrates to 3x3 windows and generates a set of the image pattern. The edge patterns in binary images were classified into 32 categories. Training the ANFIS on this category (patterns) classify the blank elements in each 3x3window in white pixels (value: 1) and dark pixels (value: 0).
A first serie of measurements was concerned with the assessment of the actual vibratory responses of the slab under forced excitation. Accelerometers were glued on the slab at several critical locations, close to the hoppers feet and near the slab corners. Measurements were performed by step-increasing the running speed of the motor and for simplicity, one hopper only was operating at a time. For each velocity, ten accelerations signals were averaged in the frequency domain and recorded on a Spectral Dynamics digital acquisition board (Model SigLab 20-42). Fig. 1 displays the measured acceleration power spectra for one hopper. Although the slab response remains rather small for low frequencies of excitation, there is a net increase of the slab vibratory reponse in the frequency range between 12 and 18 Hz. From the vibrational standpoint, this typically illustrates a resonance effect of the flexible slab which responds to the forcing of the motor according to its natural modes of vibration. Therefore the objective of this work is to address the resonance issue without making structural modifications on the slab, nor serious changes to the vibrating equipment.
There are a variety of fusion rules that have been reported as valid image fusion processes. Someof the popular fusion techniques based on statistical analysis of the images that used in our test is mean, and Principle Component Analysis (PCA). Assuming that images are collected simultaneously with accurate registration, images can be fused element wise, taking the mean values. PCA is an orthogonal linear transformation technique that transforms the multidimensional data sets to lower dimensions for image analysis without much loss of information content. The new coordinate system obtained by PCA transformation is such that the greatest variance by any projection of the data lies in the first coordinate (principle component).the second greater variance on the second coordinate, so on. We use the popular wavelet based approach to find the decomposition coefficients for image fusion. The wavelet based method is available as the image fusion tool in the wavelet toolbox, which is used for fusing various registered images of the same size. The principle ofimage fusion using wavelets is to merge the wavelet decompositions of the two original images using fusion methods. 
The observed macrobenthic richness of the Budi Lagoon was similar to that described for other coastal lakes, lagoons, and estuaries (STONER; ACEVEDO, 1990; BERTRÁN et al., 2001; SFRISO et al., 2001; PEQUEÑO et al., 2010; FIERRO et al., 2014), showing relatively low density and high dominance (MISTRI et al., 2001; BERTRÁN et al., 2013). The Budi Lagoon macrobenthos increased in abundance in the autumn, similar to indings in other soft-bottom ecosystems, such as the estuary of the Berg River, South Africa (KALEJTA; HOCKEY, 1991) and the Cádiz Bay (ARIAS; DRAKE, 1994), where abundance was greatest in the spring, closely followed by autumn. These patterns were also comparable to those of rivers near the study area, with FIERRO et al. (2015) reporting the highest abundance of macroinvertebrates in the summer-autumn. This tendency could represent transitional periods related to increased primary production in the summer and the increased precipitation, decreased salinity, and increased re-suspension of deposited materials as a consequence of forceful winds in the winter. The increases in abundance may also be related to reproductive and recruitment periods, which normally occur in the summer and autumn (GALLARDO, 1993; CONTRERAS et al., 2003). Notably, the diversity and species richness values recorded in these seasons mainly corresponded to immature insects.
The linear reduction in the parasitism of S. frugiperda caterpillars with increasing host density shows that there is an upper limit to the level of parasitism that C. flavicincta females can exert. This can be explained by egg depletion. In addition, parasitism was analyzed as a percentage and not as the numbers parasitized (Figure 3). Another possible explanation is limitations due to the handling time, which prevent a parasitoid from attacking all of the available hosts. For other parasitoids, this was explained by the reduced defensive abilities of the parasitized caterpillars, which made them more susceptible to cannibalism at higher densities (BRODEUR; BOIVIN, 2004). A reduction in the parasitism rates with increasing host density has also been reported in Trichogramma evanescens Westwood (Hymenoptera: Trichogrammatidae) parasitizing Lepidoptera eggs (AYVAZ et al., 2008). Others factors besides cannibalism may be involved in the reduction of parasitism at high host densities. Host defenses against natural enemies may be more efficient when the hosts are at present higher densities. Caterpillars of Lasiocampidae and Nymphalidae feed gregariously during their initial instars, providing a behavioral protection against natural enemies (DESPLAND; HUU, 2007; INOUYE; JOHNSON, 2005). In addition, C. flavicincta females may lay fewer eggs per S. frugiperda caterpillar when the host is at lower density, facilitating the ability of the caterpillars to mount immunological defenses, such as encapsulating the recently laid parasitoid eggs (BRODEUR; BOIVIN, 2004); however, the lowest density tested was 10 hosts per vial, which is still quite high.