Fototriangulação com parâmetros adicionais para câmaras digitais: uma avaliação experimental

Considering the growing use of digital cameras in Photogrammetric projects, especially in aerial survey, this paper presents tests and analyses of bundle block adjustment with additional parameters, using different mathematical models, and blocks of images acquired by the SAAPI digital acquisition system. Three blocks of images were processed by the LPS (Leica Photogrammetry Suite) software, in which five groups of additional parameters (AP) can be used: Bauer, Jacobsen, Ebner, Brown and Lens distortion. These AP's models were employed in the bundle block adjustment, and the results were analyzed based on the accuracy of the checking points and on the changes in these additional parameters. The obtained results showed that the Lens Distortion model allowed the best results.

Arquitetura com elevada taxa de processamento e reduzida largura de banda de memória para a estimação de movimento em vídeos digitais

Nowadays several electronics devices support digital videos. Some examples of these devices are cellphones, digital cameras, video cameras and digital televisions. However, raw videos present a huge amount of data, millions of bits, for their representation as the way they were captured. To store them in its primary form it would be necessary a huge amount of disk space and a huge bandwidth to allow the transmission of these data. The video compression becomes essential to make possible information storage and transmission. Motion Estimation is a technique used in the video coder that explores the temporal redundancy present in video sequences to reduce the amount of data necessary to represent the information. This work presents a hardware architecture of a motion estimation module for high resolution videos according to H.264/AVC standard. The H.264/AVC is the most advanced video coder standard, with several new features which allow it to achieve high compression rates. The architecture presented in this work was developed to provide a high data reuse. The data reuse schema adopted reduces the bandwidth required to execute motion estimation. The motion estimation is the task responsible for the largest share of the gains obtained with the H.264/AVC standard so this module is essential for final video coder performance. This work is included in Rede H.264 project which aims to develop Brazilian technology for Brazilian System of Digital Television

Performance comparisons of the kicking of stationary and rolling balls in a futsal context

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Sincronismo de dispositivos para o georreferenciamento de imagens digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Do men and women use similar adaptive locomotion to clear static and dynamic obstacles?

Purpose. The purpose of this study was to analyze the influence of gender on the adaptive locomotion in the clearance of obstacles. Specifically, it was evaluated if there are differences in the space-temporal parameters between male and female in the clearance of and dynamic obstacles moving at both slow and fast speeds. Basic procedures. Five young male adults and five young female adults took part in this study. The task was performed in three conditions: static obstacle and dynamic obstacle - clearance perpendicular to the participant's trajectory at slow speed (1.07 m/s) and at fast speed (1.71 m/s). The trials were recorded by two digital cameras and spatial-temporal information was obtained. Main findings. The dynamic obstacle conditions required more visual inspection. The results showed different adaptive locomotion between the sexes. The distinct gait patterns were evidenced for the spatial and temporal variables and cadence in the three conditions. Conclusions. The women presented a more conservative behavior, which was evidenced by the increase of the length in the penultimate step and in the toe clearance.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

Applying machine learning based on multiscale classifiers to detect remote phenology patterns in Cerrado savanna trees

Plant phenology is one of the most reliable indicators of species responses to global climate change, motivating the development of new technologies for phenological monitoring. Digital cameras or near remote systems have been efficiently applied as multi-channel imaging sensors, where leaf color information is extracted from the RGB (Red, Green, and Blue) color channels, and the changes in green levels are used to infer leafing patterns of plant species. In this scenario, texture information is a great ally for image analysis that has been little used in phenology studies. We monitored leaf-changing patterns of Cerrado savanna vegetation by taking daily digital images. We extract RGB channels from the digital images and correlate them with phenological changes. Additionally, we benefit from the inclusion of textural metrics for quantifying spatial heterogeneity. Our first goals are: (1) to test if color change information is able to characterize the phenological pattern of a group of species; (2) to test if the temporal variation in image texture is useful to distinguish plant species; and (3) to test if individuals from the same species may be automatically identified using digital images. In this paper, we present a machine learning approach based on multiscale classifiers to detect phenological patterns in the digital images. Our results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; (2) different plant species present a different behavior with respect to the color change information; and (3) texture variation along temporal images is promising information for capturing phenological patterns. Based on those results, we suggest that individuals from the same species and functional group might be identified using digital images, and introduce a new tool to help phenology experts in the identification of new individuals from the same species in the image and their location on the ground. © 2013 Elsevier B.V. All rights reserved.

Ajuste radiométrico de imagens aéreas digitais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Um Modelo para calibração de câmaras digitais baseado em linhas retas

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Estudo e implementação de técnicas de sincronismo de dispositivos para o georreferenciamento de imagens digitais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Geração automática de modelos digitais de superfícies utilizando múltiplas imagens

Pós-graduação em Ciências Cartográficas - FCT

Fotolivro: processos e procedimentos artísticos

Pós-graduação em Artes - IA

Manipulação remota de um braço mecânico (Scorbot ER - III) utilizando a rede mundial de computadores

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Padrão de coordenação do pular corda: um estudo desenvolvimental

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)