10 resultados para graph matching algorithms

em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"


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This paper describes two solutions for systematic measurement of surface elevation that can be used for both profile and surface reconstructions for quantitative fractography case studies. The first one is developed under Khoros graphical interface environment. It consists of an adaption of the almost classical area matching algorithm, that is based on cross-correlation operations, to the well-known method of parallax measurements from stereo pairs. A normalization function was created to avoid false cross-correlation peaks, driving to the true window best matching solution at each region analyzed on both stereo projections. Some limitations to the use of scanning electron microscopy and the types of surface patterns are also discussed. The second algorithm is based on a spatial correlation function. This solution is implemented under the NIH Image macro programming, combining a good representation for low contrast regions and many improvements on overall user interface and performance. Its advantages and limitations are also presented.

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This article introduces the software program called EthoSeq, which is designed to extract probabilistic behavioral sequences (tree-generated sequences, or TGSs) from observational data and to prepare a TGS-species matrix for phylogenetic analysis. The program uses Graph Theory algorithms to automatically detect behavioral patterns within the observational sessions. It includes filtering tools to adjust the search procedure to user-specified statistical needs. Preliminary analyses of data sets, such as grooming sequences in birds and foraging tactics in spiders, uncover a large number of TGSs which together yield single phylogenetic trees. An example of the use of the program is our analysis of felid grooming sequences, in which we have obtained 1,386 felid grooming TGSs for seven species, resulting in a single phylogeny. These results show that behavior is definitely useful in phylogenetic analysis. EthoSeq simplifies and automates such analyses, uncovers much of the hidden patterns of long behavioral sequences, and prepares this data for further analysis with standard phylogenetic programs. We hope it will encourage many empirical studies on the evolution of behavior.

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Genetic variation within and among accessions of the genus Arachis representing sections Extranervosae, Caulorrhizae, Heteranthae, and Triseminatae was evaluated using RFLP and RAPD markers. RAPD markers revealed a higher level of genetic diversity than did RFLP markers, both within and among the species evaluated. Phenograms based on various band-matching algorithms revealed three major clusters of similarity among the sections evaluated. The first group included the species from section Extranervosae, the second group consisted of sections Triseminatae, Caulorrhizae, and Heteranthae, and the third group consisted of one accession of Arachis hypogaea, which had been included as a representative of section Arachis. The phenograms obtained from the RAPD and RFLP data were similar but not identical. Arachis pietrarellii, assayed only by RAPD, showed a high degree of genetic similarity with Arachis villosulicarpa. This observation supported the hypothesis that these two species are closely related. It was also shown that accession V 7786, previously considered to be Arachis sp. aff. pietrarellii, and assayed using both RFLPs and RAPDs, was possibly a new species from section Extranervosae, but very distinct from A. pietrarellii.

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Pós-graduação em Ciências Cartográficas - FCT

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

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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

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In this paper, the concept of Matching Parallelepiped (MP) is presented. It is shown that the volume of the MP can be used as an additional measure of `distance' between a pair of candidate points in a matching algorithm by Relaxation Labeling (RL). The volume of the MP is related with the Epipolar Geometry and the use of this measure works as an epipolar constraint in a RL process, decreasing the efforts in the matching algorithm since it is not necessary to explicitly determine the equations of the epipolar lines and to compute the distance of a candidate point to each epipolar line. As at the beginning of the process the Relative Orientation (RO) parameters are unknown, a initial matching based on gradient, intensities and correlation is obtained. Based on this set of labeled points the RO is determined and the epipolar constraint included in the algorithm. The obtained results shown that the proposed approach is suitable to determine feature-point matching with simultaneous estimation of camera orientation parameters even for the cases where the pair of optical axes are not parallel.

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Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

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In this paper we deal with the problem of boosting the Optimum-Path Forest (OPF) clustering approach using evolutionary-based optimization techniques. As the OPF classifier performs an exhaustive search to find out the size of sample's neighborhood that allows it to reach the minimum graph cut as a quality measure, we compared several optimization techniques that can obtain close graph cut values to the ones obtained by brute force. Experiments in two public datasets in the context of unsupervised network intrusion detection have showed the evolutionary optimization techniques can find suitable values for the neighborhood faster than the exhaustive search. Additionally, we have showed that it is not necessary to employ many agents for such task, since the neighborhood size is defined by discrete values, with constrain the set of possible solution to a few ones.