Feature selection through gravitational search algorithm

In this paper we deal with the problem of feature selection by introducing a new approach based on Gravitational Search Algorithm (GSA). The proposed algorithm combines the optimization behavior of GSA together with the speed of Optimum-Path Forest (OPF) classifier in order to provide a fast and accurate framework for feature selection. Experiments on datasets obtained from a wide range of applications, such as vowel recognition, image classification and fraud detection in power distribution systems are conducted in order to asses the robustness of the proposed technique against Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and a Particle Swarm Optimization (PSO)-based algorithm for feature selection.

Classificação automática de padrões projetados por um sistema de luz estruturada

One of the main problems in Computer Vision and Close Range Digital Photogrammetry is 3D reconstruction. 3D reconstruction with structured light is one of the existing techniques and which still has several problems, one of them the identification or classification of the projected targets. Approaching this problem is the goal of this paper. An area based method called template matching was used for target classification. This method performs detection of area similarity by correlation, which measures the similarity between the reference and search windows, using a suitable correlation function. In this paper the modified cross covariance function was used, which presented the best results. A strategy was developed for adaptative resampling of the patterns, which solved the problem of deformation of the targets due to object surface inclination. Experiments with simulated and real data were performed in order to assess the efficiency of the proposed methodology for target detection. The results showed that the proposed classification strategy works properly, identifying 98% of targets in plane surfaces and 93% in oblique surfaces.

Application of ARX neural networks to model the rate of penetration of petroleum wells drilling

Bit performance prediction has been a challenging problem for the petroleum industry. It is essential in cost reduction associated with well planning and drilling performance prediction, especially when rigs leasing rates tend to follow the projects-demand and barrel-price rises. A methodology to model and predict one of the drilling bit performance evaluator, the Rate of Penetration (ROP), is presented herein. As the parameters affecting the ROP are complex and their relationship not easily modeled, the application of a Neural Network is suggested. In the present work, a dynamic neural network, based on the Auto-Regressive with Extra Input Signals model, or ARX model, is used to approach the ROP modeling problem. The network was applied to a real oil offshore field data set, consisted of information from seven wells drilled with an equal-diameter bit.

Análise da incerteza na representação de classes de cobertura do solo urbano resultantes da aplicação de uma rede neural artificial

The great diversity of materials that characterizes the urban environment determines a structure of mixed classes in a classification of multiespectral images. In that sense, it is important to define an appropriate classification system using a non parametric classifier, that allows incorporating non spectral (such as texture) data to the process. They also allow analyzing the uncertainty associated to each class from the output alues of the network calculated in relation to each class. Considering these properties, an experiment was carried out. This experiment consisted in the application of an Artificial Neural Network aiming at the classification of the urban land cover of Presidente Prudente and the analysis of the uncertainty in the representation of the mapped thematic classes. The results showed that it is possible to discriminate the variations in the urban land cover through the application of an Artificial Neural Network. It was also possible to visualize the spatial variation of the uncertainty in the attribution of classes of urban land cover from the generated representations. The class characterized by a defined pattern as intermediary related to the impermeability of the urban soil presented larger ambiguity degree and, therefore, larger mixture.

Environment mapping for mobile robots navigation using hierarchical neural network and omnivision

Autonomous robots must be able to learn and maintain models of their environments. In this context, the present work considers techniques for the classification and extraction of features from images in joined with artificial neural networks in order to use them in the system of mapping and localization of the mobile robot of Laboratory of Automation and Evolutive Computer (LACE). To do this, the robot uses a sensorial system composed for ultrasound sensors and a catadioptric vision system formed by a camera and a conical mirror. The mapping system is composed by three modules. Two of them will be presented in this paper: the classifier and the characterizer module. The first module uses a hierarchical neural network to do the classification; the second uses techiniques of extraction of attributes of images and recognition of invariant patterns extracted from the places images set. The neural network of the classifier module is structured in two layers, reason and intuition, and is trained to classify each place explored for the robot amongst four predefine classes. The final result of the exploration is the construction of a topological map of the explored environment. Results gotten through the simulation of the both modules of the mapping system will be presented in this paper. © 2008 IEEE.

Análise temporal do uso do solo e comparação entre os índices de vegetação ndvi e savi no município de Santa Cruz do Rio Pardo - SP usando imagens landsat-5

This work aims to analyze the land use evolution in the city of Santa Cruz do Rio Pardo - SP through supervised classification of Landsat-5 TM satellite images according to the maximum likelihood (Maxlike), as well as verifying the mapping accuracy through Kappa index, comparing NDVI and SAVI vegetation indexes in different adjustment factors for the canopy substrate and determining the vegetal coverage percentage in all methods used on 2007, May 26 th; 2009, January 7 th and 2009, April 29 th. The Maxlike classification showed several spatial changes in land use over the study period. The most appropriated vegetation indexes were NDVI and SAVI - 0,25 factor, which showed similar values of vegetal coverage percentage, but discrepant from the inferred value for Maxlike classification.

Proposta de atualização de cadastro urbano a partir de detecção de alterações em imagens QUICK BIRD tomadas em diferentes épocas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uma análise da infestação por plantas aquáticas utilizando imagens multiescala e redes neurais artificiais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geoestística e sensoriamento remoto na classificação de imagens em áreas cultivadas com citros

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sensoriamento remoto e geoprocessamento aplicado ao estudo temporal do uso da terra e na comparação entre classificação não supervisionada e análise visual

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Detecção de regiões de via integrando dados de varredura a LASER aerotransportado e imagens de alta resolução

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Classificação de cobertura do solo utilizando árvores de decisão e sensoriamento remoto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Nature-Inspired Framework for Hyperspectral Band Selection

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

Aplicação de técnicas de geoprocessamento para a avaliação da expansão urbana no municípo de Duque de Caxias (RJ) como subsídio ao monitoramento de dutos: trecho da faixa de dutos Rio de Janeiro - Belo Horizonte

Different forms of human pressure may occur in the pipeline ranges, due to the large extensions and various configurations of land use, which can pass through the pipelines. Due to the dynamics of these pressures, it is necessary to monitor temporal changes of land use and cover the surface. Under this theme, appears as extremely important to use products and techniques of remote sensing, as they allow the identification of objects of the land surface that may compromise the security and monitoring of the pipeline, and allows the extraction of information conditions on land use at different periods of time. Based on the above, this paper aims to examine in a temporal approach, the process of urban expansion in the municipality of Duque de Caxias, located on the outskirts of the metropolitan area of the state of Rio de Janeiro, as well as settlement patterns characteristic of areas that the changes occurred in the period 1987 to 2010. We used the technique of visual analysis to perform the change detection and the technique of image classification, aimed at monitoring human pressure over a stretch of track pipeline Rio de Janeiro - Belo Horizonte, located in the state of Rio de Janeiro. The stages of work involved the characterization of the study area, urban sprawl and the existing settlement patterns, through the analysis of bibliographic data. The processing of Landsat 5 images and the application of the technique of change detection were performed in three scenes for the years 1987, 1998 and 2010, while the classification process was performed on the image RapidEye for the year 2010. Can be noted an increase in urban area of approximately 22.38% and the change of land cover from natural to built. This growth is concentrated outside to the area of direct influence of the duct, occurring in the area of indirect influence of the enterprise. Regarding the settlement patterns of growth areas, it was observed that these are predominantly

Fisiografia e uso da terra de uma bacia hidrográfica em área de afloramento do sistema aquífero Guarani (SAG): o caso do Ribeirão do Jacú, Tejupá/SP (UGRHI-14)SP

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA