Approaches for lane extraction considering color, gray intensity andtexture segmenta- tion of the pavement have also been suggested. Some use neural networks for classification (Fernandez-Maloigne & Bonnet, 1995), othres combine the features vector for each pixel (Jeong & Nedevschi, 2005), and some employ the covariance matrix of intensity changes in the image (J. Zhang & Nagel, 1994; Thorpe, Hebert, Kanade, & Shafer, 1988). However, to the best of our knowledge there are no approaches that consider the use of Gabor filters, Gauss Markov Random Fields and mean-shift clustering for road recognition. Evaluating and adopting these approaches is one of the contributions of this work.
the class the sample belongs to, is obtained using a SVM system. Both the DNMF and SVM algorithms have as an output the distances of the sample under examination from each of the six classes (facial expressions). Fusion of the distances obtained from DNMF and SVMs applications is attempted using a RBF NN system. The experiments performed using the Cohn-Kanade database indicate a recognition accuracy of 98,2% when recognizing the six basic facial expressions. The novelty of this method lies in the combination of both textureand geometrical information for facial expression recognition.
A decision to use the three visible and the near-infrared band from the Thematic Mapper sensor was based in the statement by Haralick et al. (1973) and Haralick (1979) about the inextricable relationship between tone andtexture in any image. Tonal properties influence texture measurements results. Since different bands in a Landsat image exhibit varying reflectance (tonal) values, texture indices would vary accordingly with band selection. Given the current state of most space-based Earth observation programs and the increasing availability of multi-spectral and hyper-spectral images from different sensors, we considered that a discrimi- nation of at least the spectral regions of interest among the blue, green, red, and infrared dominions would facilitate future studies of fire proneness based on local homogeneity measures. Bands TM 5 and TM 7 (shortwave infrared and mid- infrared) were not used because water contents were relatively variable from year to year and seasonally in the area (for instance, 1991 was a wet year with precipitation a bit over 600 mm, 1995 was a dry year with a bit less than 400 mm). Because the Mediterranean vegetation in the area is well adapted to normal weather conditions, spectral signatures did vary very little between years, especially in bands TM 1 (blue), TM 2 (green), and TM 3 (red). An analysis using four dates (1984, 1991, 1995, and 1998) in Vega-García (2003) exhibited slight changes with yearly conditions by bands TM 4 (near-infrared), TM 5 (shortwave infrared), and
— Bela Julesz, 1981, [19(317)] In computer science, the aims in studying texture are various: understanding aspects of human vision, image recognition for different purposes, such as automatic analysis of medical images or aerial photographs for crop identification or military purposes, etc., robot vision, and also as part of other computer tasks such as image segmentation for shape analysis. The methodologies of analysis are usually classified in two kinds: statistical based on statistics and theory of probability, and struc- tural or syntactical based on language theory [22(2–3)]. The first one starts from the assumption that the generation of textures is stochastic and the distribution is by probability, leading usually to textures of random aspect. The second methodology assumes that textures are composed of primitive elements and the image is formed according to placements rules, being generally suited for textures of geometric and regular aspect [7(25)].
The large difference between the ’IE - Bulk density’ relationships developed for different textural classes indicates that the IE computed for different triplets has the potential to reflect the effect of soil texture on particle packing in soils. The theoretical analysis of Assouline and Rouault (1997) and Martín et al. (2017b) shows that the pore space arrangement can be related to the type of distribution of particles sizes. The IE parameter is related to packing but cannot reflect aggregation that is characteristic for soils where fine particles are present in substantial amounts. We note that when IE was computed using the seven texture 5
We find an example of this situation in Figure 6. Image a is the original texture provided for the “Buddha” 3D model, whose mesh is shown in wireframe at its left, and b is another texture atlas man- ually created with the same net color information but resized and spatially relocated. Images a and b could never be compared with usual image subtraction techniques, but with our method we can check that the ITEM-measured error between them is, in fact, null (except for the intrinsic imprecision of floating point operations). On the other hand, we have image c, which is a reduced quality texture for the same 3D mesh. Again, this image could never be compared directly with neither a nor b, but thanks to ITEM we can measure the difference between them, which is 15.42 if we use bi- linear interpolation and 14.84 with nearest neighbor replication. Although being able to compare two texture atlases with totally dif- ferent masks is one of ITEM’s most important and innovative fea- tures, the results of such a comparison may not be as clarifying as the ones presented below. The following results illustrate the com- parison of texture atlases that have been compressed for their lighter transmission, specially for online 3D applications. In this scenario, it could seem that the new atlas generation is not as useful as before but, thanks to it, triangles are presented proportionally to their real magnitude in the 3D mesh. This way, the distortion added by the compression scheme is evaluated considering its real impact in the textured 3D model.
Total tree density is high and varies significantly between landforms. Although the non- parametric statistics did not show that differences were related to the land dynamics described, the DCA showed a clear link between tree density and the more stable landforms. By the same token, although the direct correlation analysis of total tree density and soil texture was not statistically significant, the multivariate analysis showed landforms and tree density were associated to per cent of sand. Direct, simple correlations between vegetation variables and physical determinants may be difficult to find since there are many explanatory variables and they are not independent. In this context, the results of the multivariate analysis are of greater importance. There are alternative explanations to the lack of significance in the direct correlation analyses between soil textureand tree density. It may be that the change in species dominance occurring along the texture- geomorphological gradient introduces a compensatory effect. In addition, the fact that soil sampling and vegetation sampling were conducted independently may have foiled any direct correlation.
Finally, other approaches introduce some robust cost functions in the cam- era motion estimation, hence obtaining appropriate weights that reduces the impact of wrong measurements. A first group proposes several modifications of the well-known extended Kalman filter (EKF) in order to increase the ro- bustness of their systems against outliers and noisy measurements. In  authors propose a robust EKF filter to deal with outliers in real-time, by down-weighting the samples with more probability of being outliers, for which they learn the system dynamics thus avoiding manual parameter tuning. In  the previous approach was generalized and extended by introducing efficient smoothing and filtering modifications for dealing with data corrupted with non-Gaussian and heavy-tailed noise. The previous work was also extended in , where authors proposed to introduce a structured variational approx- imation with a more robust and flexible behavior, and yet introducing only a little increment in the computational complexity. Another group of techniques model directly the error distribution, and then perform a robust non-linear least-squares minimization of these errors. In , Kerl et al. perform robust odometry estimation for RGB-D cameras by minimizing the photometric error between two consecutive frames. They argue that their dense RGB-D residuals can be better explained with Student’s t-distributions, for which they derive a probabilistic formulation including a robust sensor model based on real world data. Recently, the work in  proposes a generic self-tuning M-estimator which iteratively estimates the parameters of the residual distribution, thus removing the necessity of manually set such parameters. However, this method needs to compute the importance weights for each iteration of the least-squares prob- lem, hence being computationally expensive for small problems as the one we address here.
The intensification of agriculture, with a gradual degradation of Argentina’s soil, has increased erosion processes and loss of fertility. The aim of this dissertation was to analyze different soil parameters, either alone or combined, to serve as sensitive indicators in the assessment of different management practices on sites with different climate and soil conditions. The study sites are located in Bengolea and Monte Buey (Córdoba), Pergamino (Buenos Aires) and Viale (Entre Rios). Good agricultural practices, Poor agricultural practices and natural environment (reference) at each site were evaluated. During the period from September 2009 to September 2012 soil sampling was performed in the months of February and September, in which it was determined: total organic carbon (COT), total nitrogen, pH and available phosphorous. A series of physical and chemical methods of fractionation to obtain organic fractions with different characteristics and cycling time was applied. Also a number of physical properties related to the size and distribution of pores, water availability and their relationship to soil management practices were analyzed. COT levels in AN decreased from the East (28 g kg -1 in Viale) to West (14 g kg -1 in Bengolea) following the rainfall andtexture gradient of sites. Relationships between different labile organic fractions with COT in AN showed differences among sites, reflecting different dynamics according to the characteristics of the environment in different localities. COT levels in 0-20 cm depths of agricultural soils were 6-42% lower than AN where soybean frequency was one of the main factors causing these declines. In general, the labile organic fractions showed differential sensitivity. Those with higher lability (COP g and
Rehydration and diffusion cereals processes were measured by MRI at different times and using two different kinds of milk, varying their fat level. Several images were obtained. A combination of textural analysis (based on the analysis of histograms) and segmentation methods (in order to understand the rehydration level of each variety of cereals) were performed.
Wu et al.  used morphological, easy to extract features with PNN to recognize plant species. This study also produced the Flavia dataset , mentioned in many other studies. In total, 12 feature types were extracted. First the image was converted to gray scale from RGB, then a boundary enhancement was applied by a Laplacian filter of 3 × 3 spatial mask in order to get the margin of the leaf. Over the extracted margin, the leaf diameter, length, width, area, perimeter, smooth factor, aspect ratio, form factor, rectangularity, narrow factor, and morphological opening were calculated. By applying PNN after reducing dimensionality with PCA, 90% precision was reported. Beghin et al.  proposed a feature fusion technique using Gabor Filters for edge andtexture feature extraction. Their data used fresh leaf images, not dry species. The reported precision was in the range of 56.2% and 85.93%, using 10 fold cross validation. The dataset had 9 species and 15 sample images per species. The classification method used was PNN.
This approach makes an adaptive sampling of an image by means of general triangulation. High frequency regions require more samples than low frequency ones. Progressive refinement is possible by adding more samples. Darsa et al  make a Delaunay triangulation and fill every triangle with a constant colour resulting from the average of dominant colours of the triangle. Hoppe  presents progressive meshes, an approach to store and transmit large-scale meshes. Assigning a plane colour to each triangle of a meshing square represents images. Certain et al  extend Lounsbery work  applying multiresolution Wavelet analysis to a triangulated surface with connectivity. They capture geometry and colour independently. 2.3. Texture Synthesis and Generation
Image stitching techniques focus on solving the misalignment problems by finding correspondences in areas where there is a potential visible seam, and distorting iteratively the images to be stitched until all the correspondences have been aligned. This is the basic idea behind the approach proposed by Gal et al. [GWO*10], where a labelling system is used to perform these local geometric transformations. Their results are correct, but their iterative system can be very time-consuming. Moreover, when the texture applied has many high-frequency components and the geometry presents errors, the system is unable to form a seamless montage. A simi- lar approach is the one proposed by Aganj et al. [AMK10], where the deformations applied to the input images come from estimat- ing some displacement vectors by finding feature correspondences among the images. Another interesting approach is the one pro- posed by Lemptisky and Ivanov [LI07] to solve the problem using a Markov random field mosaicing system, and a seam levelling technique specially adapted to manifold 3D meshes. Although their results are also good, and most of the artefacts are successfully re- moved, some seams are still visible when small details are present in different images. These methods are initially conceived to han- dle misalignments, where they have a good performance. However, they need to add a global colour correction stage to compensate for different colour balance in different input images, which can lead to visual colour seams in case of extreme lighting conditions. Moreover, these techniques introduce some distortion in the images which can be too severe in some cases, mainly in images with many
ABSTRACT: Environmental characteristics of the Northern Platform of the Paria Peninsula (PP) and the Paria Gulf (GP) are caused by water flow and sediment of the Orinoco river, the ocean currents that move along the eastern coast of South America transporting a large quantity of sediment from the Amazon river, the tidal action, waves and the current regime of the continental shelf including coastal upwelling induced by trade winds. All these factors affect biogeochemical processes in sediments that have different sources and conditions of transport, sedimentation and preservation. Accordingly, it raises as fundamental objective of this paper to describe some environmental parameters of bottom water and some geochemical characteristics such as texture, mineralogy and organic carbon content (Corg), sulfur (S), total phosphorus (TP) and total nitrogen (TN) from surface sediments of the study region. The texture was performed according to the modified pipette method and mineralogy by X-ray diffraction. The Corg was determined by dry combustion technique after acid attack, S by EA-ICP, while the PT and NT by the method of V ALDERRAMA (1981). Iso-concentration maps for the distribution of these parameters on the continental shelf north of the Peninsula (PP) and Gulf of Paria (GP) were prepared. In general, the sediment has a texture or grain size of sandy-loam and sandy-silty type, mineralogical composition consists of quartz, muscovite, kaolinite, calcite for some PP stations and zircon for GP, making mineral distinctive environments sedimentation and sediment source. The average concentrations were Corg 1.53%, S 0.23%, PT 0.04% and NT 0.03%. Ocean currents and coastal upwelling patterns may influence the spatial distribution of these variables. There is an effect on the production, distribution and sedimentation of OM in the study area, caused by the discharge of the Orinoco and Amazon River through the flow of Guyana, which directly impacts surface waters and sediments of the Paria Gulf. These results allow distinguishing two environments of sedimentation with suspended matter inputs from different sources. The PP surface sediments with typical characteristics of coastal marine sediment with input from autochthonous organic matter, resulting from primary productivity that are influenced by the phenomenon of coastal upwelling. There is greater variability in the GP, indicating different sources of sediment that result in a mixture, confirming the contributions of the Orinoco and Amazon River. The GP provides marine environments, transitional and continental. In the other hand, the PP only provides marine environments that lead to differentiation in composition, texture, mineralogy and distribution between the two regions. Sea currents and the source of the sediment may be the most important factors controlling the spatial distribution of sediments and the elements considered in the region.
An essential part of maximizing the performance of our parallel implementation is the GPU memory management (Fig. 4). Kernel functions can read and write in the GPU device global memory using a controller integrated into the GPU. Each thread block has a limited shared memory space (16 to 48 KB) which can be read and written only inside of the block, and is 10 times faster than accesses to global memory . The texture is a read-only cache, which is basically a reference to a CUDA array and contains information on how the array should be interpreted. On the other hand, the unified memory defines a managed memory space in which all CPU and GPU processors see a single coherent memory image with a common address space. Note that for using the unified memory it is not necessary to transfer data from CPU memory to GPU global memory and vice-versa.
Vapnik introduced Support Vector Machines (SVMs) in the late 1970s on the foundation of statistical learning theory . The basic implementation deals with two- class problems in which data are separated by a hyperplane defined by a number of support vectors. This hyperplane separates the positive from the negative examples, maximizing the distance between the boundary and the nearest data point in each class; the nearest data points are used to define the margins, known as support vectors . These classifiers have also proven to be exceptionally efficient in classification problems of high dimensionality [24, 25], because of their ability to generalize in high-dimensional spaces, such as the ones spanned by texture patterns. SVMs use different non-linear kernel functions, like polynomial, sigmoid and radial basis func- tions, to map the training samples from the input spaces into a higher-dimensional feature space through a mapping function .
Authors have proposed methodologies to improve the descripted stage classification with good results, but presenting the drawback of requiring a complex task for the study operator (generally the technician or the image specialist) . Considering this drawback, in this work we propose a preliminary design and implementation of a software tool to contribute to the rotator cuff muscle assessment, in order to make this task relatively easy, reproducible and reliable. We propose MRI texture descriptors to discriminate tissues, including muscles having different stages of fatty infiltration.