33 resultados para Facial Object Based Method


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Determining the genetic structure of tropical bird populations is important for assessing potential genetic effects of future habitat fragmentation and for testing hypotheses about evolutionary mechanisms promoting diversification. Here we used 10 microsatellite DNA loci to describe levels of genetic differentiation for five populations of the lek-mating blue manakin (Chiroxiphia caudata), sampled along a 414-km transect within the largest remaining continuous tract of the highly endangered Atlantic Forest habitat in southeast Brazil. We found small but significant levels of differentiation between most populations. F-ST values varied from 0.0 to 0.023 (overall F-ST = 0.012) that conformed to a strong isolation by distance relationship, suggesting that observed levels of differentiation are a result of migration-drift equilibrium. N(e)m values estimated using a coalescent-based method were small (<= 2 migrants per generation) and close to the minimum level required to maintain genetic similarity between populations. An implication of these results is that if future habitat fragmentation reduces dispersal between populations to even a small extent, then individual populations may undergo a loss of genetic diversity due to an increase in the relative importance of drift, since inbreeding effective population sizes are relatively small (N-e similar to 1000). Our findings also demonstrate that population structuring can occur in a tropical bird in continuous habitat in the absence of geographical barriers possibly due to behavioural features of the species.

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Background. IGF2 and H19 are reciprocal imprinted genes with paternal and maternal monoallelic expression, respectively. This is interesting, because IGF2 is known as a growth factor, and H19 encodes a RNA with putative tumor suppressor action. Furthermore, IGF2 and H19 are linked genes located on chromosome 11p15.5, a common site of loss of heterozygosity in human cancers.Methods. We performed an allelic-typing assay using a PCR-RFLP-based method for identification of heterozygous Informative cases in head and neck squamous cell carcinomas. Tumoral total RNA was extracted from each of the heterozygotes and further studied by RT-PCR analysis.Results. We detected the expression of the IGF2 gene in 10 of 10 informative cases. Two cases exhibited LOI of the IGF2 gene as evidenced by biallelic expression, and in another case, LOH was coupled with monoallelic expression of this growth factor. LOI for the H19 gene was observed in 1 of 14 informative samples analyzed. In this case, we also detected parallel mono-allelic expression of the IGF2 gene. Down-regulation of the H19 gene was observed in 10 of 14 cases.Conclusion. These findings support the hypothesis that H19 may be a tumor suppressor gene involved In head and neck carcinogenesis. Furthermore, our data showed that genetic and epigenetic chances at 11p15.5 could lead to abnormal expression of imprinted genes in HNSCC. (C) 2001 John Wiley & Sons, Inc.

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This paper presents a novel, fast and accurate appearance-based method for infrared face recognition. By introducing the Optimum-Path Forest classifier, our objective is to get good recognition rates and effectively reduce the computational effort. The feature extraction procedure is carried out by PCA, and the results are compared to two other well known supervised learning classifiers; Artificial Neural Networks and Support Vector Machines. The achieved performance asserts the promise of the proposed framework. ©2009 IEEE.

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In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature. © 2009 IEEE.

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The growth of large cities is usually accelerated and disorganized, which causes social, economical and infrastructural conflicts and frequently, occupation in illegal areas. For a better administration of these areas, the public manager needs information about their location. This information can be obtained through land utilization and land cover maps, where orbital images of remote sensing are used as one of the most traditional sources of data. In this context, the present work tested the applicability of the object-based classification to categorize two slum areas, taking into account the structure of the streets, size of the huts, distance between the houses, among other parameters. These area combinations of physical aspects were analyzed using the image IKONOS II and the software eCognition. Slum areas tend to be, to the contrary of the planned areas, disarranged, with narrow streets, small houses built with a variety of materials and without definition of blocks. The results of land cover classification for slum areas are encouraging because they are accurate and little ambiguous in the classification process. Thus, it would allow its utilization by urban managers.

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This paper aims at extracting street centerlines from previously isolated street regions by using the image of laser scanning intensity. In this image, streets are easily identified, since they manifest as dark, elongate ribbons contrasting with background objects. The intensity image is segmented by using the region growing technique, which generates regions representing the streets. From these regions, the street centerlines are extracted in two manners. The first one is through the Steger lines detection method combined with a line length thresholding by which lines being shorter than a minimum length are removed. The other manner is by combining the skeletonization method of regions based on the Medial Axis Transform and with a pruning process to eliminate as much as possible the ramifications. Experiments showed that the Steger-based method provided results better than the method based on skeletonization.

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Identification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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The metropolitan region of São Paulo is the most populous of the country, this happens because of its great importance in the national economy and the job opportunities that are offered to the population. These factors result in intense population growth and urban expansion, reaching some non-habitable places of the metropolis, as areas of pipelines, which are very important for the transportation of natural gas, oil and its derivatives. Before the population growth of the region, these sites were unoccupied, do not presenting problems for the population. However, with the disorderly occupation is generated great anthropogenic pressure on the pipeline stitches, causing risks to people who are around them. Therefore it is extremely important to monitor the strip of pipelines through products and techniques of remote sensing and geoprocessing, enabling, through high spatial resolution images, identification of objects or phenomena that occur on Earth's surface that can alter the functioning and safety of pipelines. Therefore, this study aims to monitor a stretch of the area of the pipeline mesh GASPAL/OSVAT and Capuava Refinery (RECAP), located on the outskirts of the metropolitan area of São Paulo in the city of Mauá, who suffer great human pressure, proving thus the techniques of remote sensing and geographic information system (GIS) as effective tools for monitoring phenomena occurred in urban areas of great complexity. The monitoring was done by object-based classification applied in orbital images Ikonos II and RapidEye, of high spatial resolution and, image processing, detection of objects, segmentation, classification and editing were developed through the eCognition and ArcGis softwares. To determine the statistical accuracy of the mapping of the land cover of the stretch of pipeline in Maua, the results were analyzed by error matrix... (Complete abstract click electronic access below)

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The use of physical characteristics for human identification is known as biometrics. Among the many biometrics traits available, the fingerprint is the most widely used. The fingerprint identification is based on the impression patterns, as the pattern of ridges and minutiae, characteristics of first and second levels respectively. The current identification systems use these two levels of fingerprint features due to the low cost of the sensors. However, the recent advances in sensor technology, became possible to use third level features present within the ridges, such as the perspiration pores. Recent studies show that the use of third-level features can increase security and fraud protection in biometric systems, since they are difficult to reproduce. In addition, recent researches have also focused on multibiometrics recognition due to its many advantages. The goal of this research project was to apply fusion techniques for fingerprint recognition in order to combine minutia, ridges and pore-based methods and, thus, provide more robust biometrics recognition systems, and also to develop an automated fingerprint identification system using these three methods of recognition. We evaluated isotropic-based and adaptive-based automatic pore extraction methods, and the fusion of pore-based method with the identification methods based on minutiae and ridges. The experiments were performed on the public database PolyUHRF and showed a reduction of approximately 16% in the EER compared to the best results obtained by the methods individually

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)