19 resultados para Collinearity
em Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho"
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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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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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In this work a method is proposed to allow the indirect orientation of images using photogrammetric control extracted through integration of data derived from Photogrammetry and Light Detection and Ranging (LiDAR) system. The photogrammetric control is obtained by using an inverse photogrammetric model, which allows the projection of image space straight lines onto the object space. This mathematical model is developed based on the intersection between the collinearity-based straight line and a DSM of region, derived from LiDAR data. The mathematical model used in the indirect orientation of the image is known as the model of equivalent t planes. This mathematical model is based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The goal of this work is to verify the quality, efficiency and potential of photogrammetric control straight lines obtained with proposed method applied to the indirect orientation of images. The quality of generated photogrammetric control was statistically available and the results showed that proposed method is promising and it has potential for the indirect orientation of images.
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Leptospirosis is a globally distributed emerging zoonosis. Dogs are commonly affected and although other serovars can cause canine leptospirosis, Leptospira interrogans serovar Canicola is primary found in these animals. A retrospective study was conducted using a database of 1195 dogs tested for Leptospira infection from 2003 to 2010 at the Laboratory of Zoonosis Diagnosis at the Veterinary Hospital of São Paulo State University (UNESP) in Botucatu, São Paulo state, Brazil. The seroprevalence of infected dogs was 20.08% (240/1195), and the most prevalent serovars were Canicola (6.7%), Copenhageni (5.0%), Icterohaemorrhagiae (2.9%), Autumnalis (2.9%), Pyrogenes (2.8%), Pomona (2.0%), Hardjo (2.0%), Australis (1.8%), Bratislava (1.6%), Cynopteri (1.4%), Grippotyphosa (1.3%) and Djasiman (1.0%). By univariate analysis, the variables age and breed were not statistically related to the infection, while gender and season were. The effects of gender were also noticeable related to serovars Australis, Canicola and Hardjo. In multivariate analysis, the level of significance (p-value) of season was suppressed by gender, indicating possible collinearity between those two variables.
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Neste artigo é proposto um método semiautomático para extração de rodovias combinando um estereopar de imagens aéreas de baixa resolução com um poliedro gerado a partir de um modelo digital do terreno (MDT). O problema é formulado no espaço-objeto através de uma função objetivo que modela o objeto 'rodovia' como uma curva suave e pertencente a uma superfície poliédrica. A função objetivo proposta depende também de informações radiométricas, que são acessadas no espaço-imagem via relação de colinearidade entre pontos da rodovia no espaço-objeto e os correspondentes nos espaços imagem do estereopar. A linha poligonal que melhor modela a rodovia selecionada é obtida por otimização no espaço-objeto da função objetivo, tendo por base o algoritmo de programação dinâmica. O processo de otimização é iterativo e dependente do fornecimento por um operador de uma aproximação inicial para a rodovia selecionada. Os resultados obtidos mostraram que o método é robusto frente a anomalias existentes ao longo das rodovias, tais como obstruções causadas por sombras e árvores.
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Geometric accuracy of a close-range photogrammetric system is assessed in this paper considering surface reconstruction with structured light as its main purpose. The system is based on an off-the-shelf digital camera and a pattern projector. The mathematical model for reconstruction is based on the parametric equation of the projected straight line combined with collinearity equations. A sequential approach for system calibration was developed and is presented. Results obtained from real data are also presented and discussed. Experiments with real data using a prototype have indicated 0.5mm of accuracy in height determination and 0.2mm in the XY plane considering an application where the object was 1630mm distant from the camera.
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This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. © Pleiades Publishing, Inc. 2006.
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The aim of this paper is to present a model for orientation of pushbroom sensors that allows estimating the polynomial coefficients describing the trajectory of the platform, using linear features as ground control. Considering that pushbroom image acquisition is not instantaneous, six EOP (Exterior Orientation Parameters) for each scanned line must be estimated. The sensor position and attitude parameters are modeled with a time dependent polynomial. The relationship between object and image space is established through a mathematical model based on the equivalence between the vector normal to the projection plane in the image space and to the vector normal to the rotated projection plane in the object space. The equivalence property between planes was adapted to consider the pushbroom geometry. Some experiments with simulated data corresponding to CBERS scene (China-Brazil Earth Resource Satellite) were accomplished in order to test the developed model using straight lines. Moreover, experiments with points ground with the model based on collinearity equations adapted to the pushbroom geometry were also accomplished. The obtained results showed that the proposed model can be used to estimate the EOP of pushbroom images with suitable accuracy.
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This study aimed to determine the best auxiliary trait for indirect selection of soybean grain yield, through path analysis and in avoidance of the adverse effects of multicollinearity and expected response. Seventy-nine F5 soybean genotypes from the cross FT-Cometa x Bossier were used. The populations were distributed on the field was the families inserted with replicated controls. Primary and secondary traits of grain yield were evaluated in four phenotypically superior plants per family. The traits number of pods, height and number of nodes were considered as the most important, showing the best combination of direct effect and genotypic correlation. The number of pods achieved the highest expected gain through the estimation method based on the selection differential. On the other hand, plant height, by the method based on selection intensity, was not a good indicator of the most productive plants.
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The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China-Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced. © 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)