Desenvolvimento de Módulo de Reconhecimento Facial para Sistema de Informação Acadêmica / Development of Facial Recognition Module for Academic Information System

O reconhecimento facial é uma técnica de biometria baseado na extração das características da face humana buscando nos detalhes as diferenças entre as pessoas. Aplicando a identificação facial em sistemas acadêmicos é possível garantir mais agilidade e segurança. A proposta tem como objetivo concreto o estudo, a definição do modelo e a implementação de um protótipo de identicação facial que auxilia no atendimento a comunidade acadêmica e tem aplicação tanto na Universidade de Brasília quanto em outras universidades. O protótipo visa automatizar o atendimento nas secretarias auxiliando todas as entidades envolvidas, mas principalmente os alunos e funcionários

Design and implementation of an artificial neural network applied to finger bad-positioning detection on touchless multiview fingerprints devices

This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations

Avaliação da Gestão Ambiental no Setor Hoteleiro: Um Estudo nos Hotéis do Extremo Norte Brasileiro

A pesquisa apresentada neste artigo objetivou verificar o gerenciamento ambiental nos hotéis do Município de Boa Vista - RR. Procedeu-se à fundamentação teórica para embasar a pesquisa exploratória, a qual foi aprofundada com pesquisa de campo, que utilizou como instrumento de coleta de dados um questionário estruturado, aplicado junto aos gestores e proprietários dos 12 hotéis existentes na referida região. Mediante tratamento estatístico e análise dos resultados, observou-se, na maioria dos hotéis que não há uma preocupação por parte dos gestores e funcionários no que diz respeito à gestão ambiental. A falta de qualificação dos recursos humanos acerca da educação ambiental e a gestão de resíduos sólidos gerados em hotéis apontam para a carência de informações que possibilitem atitudes mais assertivas, o que contribuiria em muito para o desenvolvimento regional sustentável

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rastreamento visual de objetos utilizando métodos de similaridade de regiões e filtagem estocástica

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2009.Inicialmente são apresentadas a importância e as aplicações que envolvem o processo de rastreamento visual de objetos. O problema de rastreamento visual pode ser definido como um processo de estimação em conjunto com técnicas de processamento de imagens. Os principais métodos que realizam rastreamento visual encontrados na literatura são discutidos. O problema tratado aqui consiste em rastreamento automático de um dado objeto que aparece em uma sequência de imagens obtida por um sistema de visão computacional. Neste trabalho propõem-se métodos para a realização desta tarefa baseados na similaridade de regiões, o window-matching (WM) method. Este método baseia-se na obtenção de regiões de similaridade em função do padrão da cor cinza de uma janela em torno de um ponto de interesse. Discutem-se também as principais formas de medição da similaridade e a escolha pela função soma do quadrado das diferenças (SSD) é também justificada. Em adição, discutem-se os fatores e parâmetros que afetam o bom desempenho do método tais como: tipo de movimento realizado, oclusões, variação do tamanho da janela, mudanças de iluminação, etc. Desenvolveu-se e implementou-se então um algoritmo de rastreamento (WM) baseado na similaridade de regiões que utiliza a função SSD. O algoritmo foi então aplicado a diversas situações de rastreamento. Observou-se que, para certas aplicações, o algoritmo WM não acompanhava o objeto rastreado. Então, como o rastreamento pode ser tratado como sendo um problema de estimação, introduziu-se um procedimento recursivo para estimação ótima a partir das medidas produzidas pelo algoritmo WM. No processo dinâmico de rastreamento, o vetor de estado a ser estimado consiste dos vetores de posição e velocidade 2D do ponto de interesse, sendo o vetor de medição dado pelos vetores correspondentes fornecidos pelo algoritmoWM. O método leva agora em consideração as características estocásticas do processo de rastreamento (ruídos intrínseco e de medida) e a estimação ótima é realizada pelo filtro de Kalman, que estimará posição e velocidade e as incertezas correspondentes. Um novo algoritmo integrando esta filtragem estocástica (WM+K) foi desenvolvido e implementado. Observou-se que a filtragem estocástica realmente melhora o desempenho do rastreamento. Na procura por aumentar mais ainda a robustez do algoritmo e a sua convergência adicionou-se uma busca heurística nas soluções baseada na otimização seguindo o agrupamento de partículas. Desenvolveu-se assim o algoritmo WM+K+PSO que além de maior robustez produziu trajetórias de rastreamento mais suaves. __________________________________________________________________________________ ABSTRACTInitially the importance and applications on which object visual tracking is involved are presented. Visual tracking problem can be stated as an estimation process acting together with digital image processing techniques. The different methods found in the literature for solving this tracking problem are discussed. The problem being dealt here consists of automatic tracking of a given object appearing in a sequence of images captured by a computer vision system. This work proposes methods to perform this task that are based on the windowmatching (WM) techniques. These techniques are based on obtaining similar regions in terms of the gray level pattern of a window around a point of interest. The option for these techniques are justified and the main hypotheses are discussed. The different ways of measuring similarity are also discussed and the choice of the sum of square differences (SSD) as a similarity cost function is also justified. A discussion follows of situations that affect the tracking results, as type of motion, occlusions, window size variations, illumination changes, etc. An object tracking algorithm (WM) based on regions of similarity as measured by the SSD cost function is developed and implemented. The algorithm is then applied for tracking objects in different situations. It was observed, for certain applications, that the WM algorithm failed to track the object. Then, as tracking can be considered an estimation problem, a recursive procedure for optimal estimation from measurements generated by the WM algorithm. In the tracking dynamical process the state vector consist of the 2D position and velocity coordinates of the point of interest, being the measurement vector the corresponding output from the WM algorithm. The new method now takes into account the stochastic properties of the tracking process (intrinsic and measurement noise) and the optimal estimation is performed by the Kalman filter, being the output estimates of the position and velocity and the corresponding uncertainties. A new algorithm integrating this stochastic filtering (WM+K) is developed and implemented. Indeed the stochastic filtering improves the tracking performance and succeeds where the WM fails. Further procedures to increase the robustness and convergence of the tracking algorithm were pursued. Introducing a heuristic search based on Particle Swarm Optimization allowed to obtain smooth tracking trajectories

Using Synthetic Images for Deep Learning Recognition Process on Automatic License Plate Recognition

International audienceThe Automatic License Plate Recognition has been the subject of several studies, given its applicability in real world situations (e.g. toll collection, identification of vehicles in parking lots or even for safety issues in vehicle control that cross borders between countries). In this work, we propose an analysis of the influence to retraining a plate recognition model and a deep neural network for object detection, using synthetic plates image databases from the Brazilian licence plates. The proposed data set uses variations of rotation, size and noise to evaluate the robustness. Thus, the influence of the use of synthetic plates images on the accuracy of systems responsible for locating real plates, segmenting the characters and recognizing them was evaluated and in the tests performed there was an increase in accuracy (considering a system trained with real plates) of three stages: character segmentation, letter recognition and number recognition (2.54%, 1.09% and 2, 49% respectively). It stands out the accuracy of 62.47% (in the number recognition step) obtained by a neural network trained exclusively with synthetic data and tested on real plates

NemaNet : a convolutional neural network model for identification of soybean nematodes

Phytoparasitic nematodes (or phytonematodes) are causing severe damage to crops and generating large-scale economic losses worldwide. In soybean crops, annual losses are estimated at 10.6% of the world production. Besides, the identification of these species through microscopic analysis by an expert with taxonomic knowledge is often laborious, time-consuming, and susceptible to failure. From this perspective, robust and automatic approaches are necessary for identifying phytonematodes that are capable of providing correct diagnoses for the classification of species and subsidizing of all control and prevention measures. This work presents a new public data set called NemaDataset containing 3063 microscopic images from five nematode species with the most significant damage relevance for the soybean crop. Additionally, we propose a new Convolutional Neural Network (CNN) model defined as NemaNet and present a comparative assessment with thirteen popular models of CNNs, all of them representing state-of-the art classification and recognition. The general average was calculated for each model, on a from-scratch training; the NemaNet model reached 96.76% accuracy, while the best evaluation fold reached 98.04%. When training with transfer learning was performed, the average accuracy reached 98.82%. The best evaluation fold reached 99.35%, and overall accuracy improvements of over 6.83% and 4.1%, for from-scratch and transfer learning training, respectively, compared to other popular models were achieved