49 resultados para Computer Vision and Robotics (Autonomous Systems)

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Inferences about leaf anatomical characteristics had largely been made by manually measuring diverse leaf regions, such as cuticle, epidermis and parenchyma to evaluate differences caused by environmental variables. Here we tested an approach for data acquisition and analysis in ecological quantitative leaf anatomy studies based on computer vision and pattern recognition methods. A case study was conducted on Gochnatia polymorpha (Less.) Cabrera (Asteraceae), a Neotropical savanna tree species that has high phenotypic plasticity. We obtained digital images of cross-sections of its leaves developed under different light conditions (sun vs. shade), different seasons (dry vs. wet) and in different soil types (oxysoil vs. hydromorphic soil), and analyzed several visual attributes, such as color, texture and tissues thickness in a perpendicular plane from microscopic images. The experimental results demonstrated that computational analysis is capable of distinguishing anatomical alterations in microscope images obtained from individuals growing in different environmental conditions. The methods presented here offer an alternative way to determine leaf anatomical differences. © 2013 Elsevier B.V.

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This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

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This work presents the Petri net-based modeling of an autonomous robot's navigation system used for the application of supplies in agriculture. The model was developed theoretically and implemented through the CPNTools software. It simulates the behavior of the robot, capturing environmental characteristics by means of sensors, making appropriate decisions, and forwarding them to the corresponding actuators. By exciting the model using CPNTools it is possible to simulate situations that the robot might undergo, without the need to expose it to real potentially dangerous situations. ©2009 IEEE.

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

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The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

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Augmented Reality (AR) systems which use optical tracking with fiducial marker for registration have had an important role in popularizing this technology, since only a personal computer with a conventional webcam is required. However, in most these applications, the virtual elements are shown only in the foreground a real element does not occlude a virtual one. The method presented enables AR environments based on fiducial markers to support mutual occlusion between a real element and many virtual ones, according to the elements position (depth) in the environment. © 2012 IEEE.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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This article describes the use of Artificial Intelligence (IA) techniques applied in cells of a manufacturing system. Machine Vision was used to identify pieces and their positions of two different products to be assembled in the same productive line. This information is given as input for an IA planner embedded in the manufacturing system. Therefore, initial and final states are sent automatically to the planner capable to generate assembly plans for a robotic cell, in real time.

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The effects of agricultural-pastoral and tillage practices on soil microbial populations and activities have not been systematically investigated. The effect of no-tillage (NT), no-tillage agricultural-pastoral integrated systems (NT-I) and conventional tillage (CT) at soil depths of 0-10, 10-20 and 20-30 cm on the microbial populations (bacteria and fungi), biomass-C, potential nitrification, urease and protease activities, total organic matter and total N contents were investigated. The crops used were soybean (in NT, NT-I and CT systems), corn (in NT and NT-I systems) and Tanner grass (Brachiaria sp.) (in NT-I system); a forest system was used as a control. Urease and protease activities, biomass-C and the content of organic matter and total N were higher (p < 0.05) in the forest soil than the other soils. Potential nitrification was significantly higher in the NT-I system in comparison with the other systems. Bacteria numbers were similar in all systems. Fungi counts were similar in the CT and forest, but both were higher than in NT. All of these variables were dependent on the organic matter content and decreased (p < 0.05) from the upper soil layer to the deeper soil layers. These results indicate that the no-tillage agricultural-pasture-integrated systems may be useful for soil conservation.

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In upland rice, the reduced grain yield that originates from the moisture stress period is related to the plant's tolerance of water deficiency and may vary between cultivars. The purpose of the work presented here was to evaluate the performance of upland rice cultivars in both rainfed and sprinkler-irrigated systems. A split-plot scheme with 8 replicates in a randomised block design was used. The plots were composed of 2 cropping systems (rainfed and sprinkler-irrigated) and the split-plot consisted of 2 cultivars (IAC 201 and Carajas) which are suggested for cultivation in upland ecosystems. Carajas had a greater number of panicles per square metre, higher spikelet fertility and grain mass, and, consequently, a higher grain yield than IAC 201 regardless of cropping system. IAC 201 was more sensitive to water deficiency than Carajas. Sprinkler irrigation improved spikelet fertility, grain mass and upland rice grain yield. Even when cultivars with a higher tolerance of water deficiency are used, a sprinkler-irrigated system may be a viable method to increase upland rice yield throughout most of the Brazilian Cerrado.

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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)