65 research outputs found
Central Florida Future, Vol. 37 No. 33, December 16, 2004
What I want for the holidays...A peek at the gifts Future writers hope to find under the treehttps://stars.library.ucf.edu/centralfloridafuture/2797/thumbnail.jp
Biologically motivated keypoint detection for RGB-D data
With the emerging interest in active vision, computer vision researchers have been increasingly
concerned with the mechanisms of attention. Therefore, several visual attention
computational models inspired by the human visual system, have been developed, aiming at
the detection of regions of interest in images.
This thesis is focused on selective visual attention, which provides a mechanism for the
brain to focus computational resources on an object at a time, guided by low-level image properties
(Bottom-Up attention). The task of recognizing objects in different locations is achieved
by focusing on different locations, one at a time. Given the computational requirements of the
models proposed, the research in this area has been mainly of theoretical interest. More recently,
psychologists, neurobiologists and engineers have developed cooperation's and this has
resulted in considerable benefits. The first objective of this doctoral work is to bring together
concepts and ideas from these different research areas, providing a study of the biological research
on human visual system and a discussion of the interdisciplinary knowledge in this area, as
well as the state-of-art on computational models of visual attention (bottom-up). Normally, the
visual attention is referred by engineers as saliency: when people fix their look in a particular
region of the image, that's because that region is salient. In this research work, saliency methods
are presented based on their classification (biological plausible, computational or hybrid)
and in a chronological order.
A few salient structures can be used for applications like object registration, retrieval or
data simplification, being possible to consider these few salient structures as keypoints when
aiming at performing object recognition. Generally, object recognition algorithms use a large
number of descriptors extracted in a dense set of points, which comes along with very high computational
cost, preventing real-time processing. To avoid the problem of the computational
complexity required, the features have to be extracted from a small set of points, usually called
keypoints. The use of keypoint-based detectors allows the reduction of the processing time and
the redundancy in the data. Local descriptors extracted from images have been extensively
reported in the computer vision literature. Since there is a large set of keypoint detectors, this
suggests the need of a comparative evaluation between them. In this way, we propose to do a
description of 2D and 3D keypoint detectors, 3D descriptors and an evaluation of existing 3D keypoint
detectors in a public available point cloud library with 3D real objects. The invariance of
the 3D keypoint detectors was evaluated according to rotations, scale changes and translations.
This evaluation reports the robustness of a particular detector for changes of point-of-view and
the criteria used are the absolute and the relative repeatability rate. In our experiments, the
method that achieved better repeatability rate was the ISS3D method.
The analysis of the human visual system and saliency maps detectors with biological inspiration
led to the idea of making an extension for a keypoint detector based on the color
information in the retina. Such proposal produced a 2D keypoint detector inspired by the behavior
of the early visual system. Our method is a color extension of the BIMP keypoint detector,
where we include both color and intensity channels of an image: color information is included
in a biological plausible way and multi-scale image features are combined into a single keypoints
map. This detector is compared against state-of-art detectors and found particularly
well-suited for tasks such as category and object recognition. The recognition process is performed
by comparing the extracted 3D descriptors in the locations indicated by the keypoints after mapping the 2D keypoints locations to the 3D space. The evaluation allowed us to obtain
the best pair keypoint detector/descriptor on a RGB-D object dataset. Using our keypoint detector
and the SHOTCOLOR descriptor a good category recognition rate and object recognition
rate were obtained, and it is with the PFHRGB descriptor that we obtain the best results.
A 3D recognition system involves the choice of keypoint detector and descriptor. A new
method for the detection of 3D keypoints on point clouds is presented and a benchmarking is
performed between each pair of 3D keypoint detector and 3D descriptor to evaluate their performance
on object and category recognition. These evaluations are done in a public database
of real 3D objects. Our keypoint detector is inspired by the behavior and neural architecture
of the primate visual system: the 3D keypoints are extracted based on a bottom-up 3D saliency
map, which is a map that encodes the saliency of objects in the visual environment. The saliency
map is determined by computing conspicuity maps (a combination across different modalities)
of the orientation, intensity and color information, in a bottom-up and in a purely stimulusdriven
manner. These three conspicuity maps are fused into a 3D saliency map and, finally, the
focus of attention (or "keypoint location") is sequentially directed to the most salient points in
this map. Inhibiting this location automatically allows the system to attend to the next most
salient location. The main conclusions are: with a similar average number of keypoints, our 3D
keypoint detector outperforms the other eight 3D keypoint detectors evaluated by achiving the
best result in 32 of the evaluated metrics in the category and object recognition experiments,
when the second best detector only obtained the best result in 8 of these metrics. The unique
drawback is the computational time, since BIK-BUS is slower than the other detectors. Given
that differences are big in terms of recognition performance, size and time requirements, the
selection of the keypoint detector and descriptor has to be matched to the desired task and we
give some directions to facilitate this choice. After proposing the 3D keypoint detector, the research focused on a robust detection and
tracking method for 3D objects by using keypoint information in a particle filter. This method
consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation
is made to remove all the background information, reducing the number of points for
further processing. In the initialization, we use a keypoint detector with biological inspiration.
The information of the object that we want to follow is given by the extracted keypoints. The
particle filter does the tracking of the keypoints, so with that we can predict where the keypoints
will be in the next frame. In a recognition system, one of the problems is the computational cost
of keypoint detectors with this we intend to solve this problem. The experiments with PFBIKTracking
method are done indoors in an office/home environment, where personal robots are
expected to operate. The Tracking Error evaluates the stability of the general tracking method.
We also quantitatively evaluate this method using a "Tracking Error". Our evaluation is done by
the computation of the keypoint and particle centroid. Comparing our system that the tracking
method which exists in the Point Cloud Library, we archive better results, with a much smaller
number of points and computational time. Our method is faster and more robust to occlusion
when compared to the OpenniTracker.Com o interesse emergente na visão ativa, os investigadores de visão computacional têm
estado cada vez mais preocupados com os mecanismos de atenção. Por isso, uma série de
modelos computacionais de atenção visual, inspirado no sistema visual humano, têm sido desenvolvidos.
Esses modelos têm como objetivo detetar regiões de interesse nas imagens.
Esta tese está focada na atenção visual seletiva, que fornece um mecanismo para que
o cérebro concentre os recursos computacionais num objeto de cada vez, guiado pelas propriedades
de baixo nÃvel da imagem (atenção Bottom-Up). A tarefa de reconhecimento de
objetos em diferentes locais é conseguida através da concentração em diferentes locais, um
de cada vez. Dados os requisitos computacionais dos modelos propostos, a investigação nesta
área tem sido principalmente de interesse teórico. Mais recentemente, psicólogos, neurobiólogos
e engenheiros desenvolveram cooperações e isso resultou em benefÃcios consideráveis. No
inÃcio deste trabalho, o objetivo é reunir os conceitos e ideias a partir dessas diferentes áreas
de investigação. Desta forma, é fornecido o estudo sobre a investigação da biologia do sistema
visual humano e uma discussão sobre o conhecimento interdisciplinar da matéria, bem como
um estado de arte dos modelos computacionais de atenção visual (bottom-up). Normalmente,
a atenção visual é denominada pelos engenheiros como saliência, se as pessoas fixam o olhar
numa determinada região da imagem é porque esta região é saliente. Neste trabalho de investigação,
os métodos saliência são apresentados em função da sua classificação (biologicamente
plausÃvel, computacional ou hÃbrido) e numa ordem cronológica.
Algumas estruturas salientes podem ser usadas, em vez do objeto todo, em aplicações
tais como registo de objetos, recuperação ou simplificação de dados. É possÃvel considerar
estas poucas estruturas salientes como pontos-chave, com o objetivo de executar o reconhecimento
de objetos. De um modo geral, os algoritmos de reconhecimento de objetos utilizam um
grande número de descritores extraÃdos num denso conjunto de pontos. Com isso, estes têm um
custo computacional muito elevado, impedindo que o processamento seja realizado em tempo
real. A fim de evitar o problema da complexidade computacional requerido, as caracterÃsticas
devem ser extraÃdas a partir de um pequeno conjunto de pontos, geralmente chamados pontoschave.
O uso de detetores de pontos-chave permite a redução do tempo de processamento e a
quantidade de redundância dos dados. Os descritores locais extraÃdos a partir das imagens têm
sido amplamente reportados na literatura de visão por computador. Uma vez que existe um
grande conjunto de detetores de pontos-chave, sugere a necessidade de uma avaliação comparativa
entre eles. Desta forma, propomos a fazer uma descrição dos detetores de pontos-chave
2D e 3D, dos descritores 3D e uma avaliação dos detetores de pontos-chave 3D existentes numa
biblioteca de pública disponÃvel e com objetos 3D reais. A invariância dos detetores de pontoschave
3D foi avaliada de acordo com variações nas rotações, mudanças de escala e translações.
Essa avaliação retrata a robustez de um determinado detetor no que diz respeito às mudanças
de ponto-de-vista e os critérios utilizados são as taxas de repetibilidade absoluta e relativa. Nas
experiências realizadas, o método que apresentou melhor taxa de repetibilidade foi o método
ISS3D.
Com a análise do sistema visual humano e dos detetores de mapas de saliência com inspiração
biológica, surgiu a ideia de se fazer uma extensão para um detetor de ponto-chave
com base na informação de cor na retina. A proposta produziu um detetor de ponto-chave 2D
inspirado pelo comportamento do sistema visual. O nosso método é uma extensão com base na cor do detetor de ponto-chave BIMP, onde se incluem os canais de cor e de intensidade de
uma imagem. A informação de cor é incluÃda de forma biológica plausÃvel e as caracterÃsticas
multi-escala da imagem são combinadas num único mapas de pontos-chave. Este detetor
é comparado com os detetores de estado-da-arte e é particularmente adequado para tarefas
como o reconhecimento de categorias e de objetos. O processo de reconhecimento é realizado
comparando os descritores 3D extraÃdos nos locais indicados pelos pontos-chave. Para isso, as
localizações do pontos-chave 2D têm de ser convertido para o espaço 3D. Isto foi possÃvel porque
o conjunto de dados usado contém a localização de cada ponto de no espaço 2D e 3D. A avaliação
permitiu-nos obter o melhor par detetor de ponto-chave/descritor num RGB-D object dataset.
Usando o nosso detetor de ponto-chave e o descritor SHOTCOLOR, obtemos uma noa taxa de
reconhecimento de categorias e para o reconhecimento de objetos é com o descritor PFHRGB
que obtemos os melhores resultados.
Um sistema de reconhecimento 3D envolve a escolha de detetor de ponto-chave e descritor,
por isso é apresentado um novo método para a deteção de pontos-chave em nuvens de
pontos 3D e uma análise comparativa é realizada entre cada par de detetor de ponto-chave
3D e descritor 3D para avaliar o desempenho no reconhecimento de categorias e de objetos.
Estas avaliações são feitas numa base de dados pública de objetos 3D reais. O nosso detetor
de ponto-chave é inspirado no comportamento e na arquitetura neural do sistema visual dos
primatas. Os pontos-chave 3D são extraÃdas com base num mapa de saliências 3D bottom-up,
ou seja, um mapa que codifica a saliência dos objetos no ambiente visual. O mapa de saliência
é determinada pelo cálculo dos mapas de conspicuidade (uma combinação entre diferentes
modalidades) da orientação, intensidade e informações de cor de forma bottom-up e puramente
orientada para o estÃmulo. Estes três mapas de conspicuidade são fundidos num mapa de saliência
3D e, finalmente, o foco de atenção (ou "localização do ponto-chave") está sequencialmente
direcionado para os pontos mais salientes deste mapa. Inibir este local permite que o sistema
automaticamente orientado para próximo local mais saliente. As principais conclusões são: com
um número médio similar de pontos-chave, o nosso detetor de ponto-chave 3D supera os outros
oito detetores de pontos-chave 3D avaliados, obtendo o melhor resultado em 32 das métricas
avaliadas nas experiências do reconhecimento das categorias e dos objetos, quando o segundo
melhor detetor obteve apenas o melhor resultado em 8 dessas métricas. A única desvantagem
é o tempo computacional, uma vez que BIK-BUS é mais lento do que os outros detetores. Dado
que existem grandes diferenças em termos de desempenho no reconhecimento, de tamanho
e de tempo, a seleção do detetor de ponto-chave e descritor tem de ser interligada com a
tarefa desejada e nós damos algumas orientações para facilitar esta escolha neste trabalho de
investigação.
Depois de propor um detetor de ponto-chave 3D, a investigação incidiu sobre um método
robusto de deteção e tracking de objetos 3D usando as informações dos pontos-chave num filtro
de partÃculas. Este método consiste em três etapas distintas: Segmentação, Inicialização do
Tracking e Tracking. A segmentação é feita de modo a remover toda a informação de fundo,
a fim de reduzir o número de pontos para processamento futuro. Na inicialização, usamos um
detetor de ponto-chave com inspiração biológica. A informação do objeto que queremos seguir
é dada pelos pontos-chave extraÃdos. O filtro de partÃculas faz o acompanhamento dos pontoschave,
de modo a se poder prever onde os pontos-chave estarão no próximo frame. As experiências
com método PFBIK-Tracking são feitas no interior, num ambiente de escritório/casa, onde
se espera que robôs pessoais possam operar. Também avaliado quantitativamente este método
utilizando um "Tracking Error". A avaliação passa pelo cálculo das centróides dos pontos-chave e
das partÃculas. Comparando o nosso sistema com o método de tracking que existe na biblioteca usada no desenvolvimento, nós obtemos melhores resultados, com um número muito menor de
pontos e custo computacional. O nosso método é mais rápido e mais robusto em termos de
oclusão, quando comparado com o OpenniTracker
Non-destructive quality control of carbon anodes using modal analysis, acousto-ultrasonic and latent variable methods
La performance des cuves d’électrolyse utilisées dans la production d’aluminium primaire par le procédé Hall-Héroult est fortement influencée par la qualité des anodes de carbone. Celles-ci sont de plus en plus variables en raison de la qualité décroissante des matières premières (coke et braie) et des changements de fournisseurs qui deviennent de plus en plus fréquents afin de réduire le coût d’achat et de rencontrer les spécifications des usines. En effet, les défauts des anodes, tels les fissures, les pores et les hétérogénéités, causés par cette variabilité, doivent être détectés le plus tôt possible afin d’éviter d’utiliser des anodes défectueuses dans les cuves et/ou d’apporter des ajustements au niveau du procédé de fabrication des anodes. Cependant, les fabricants d’anodes ne sont pas préparés pour réagir à cette situation afin de maintenir une qualité d'anode stable. Par conséquent, il devient prioritaire de développer des techniques permettant d’inspecter le volume complet de chaque anode individuelle afin d’améliorer le contrôle de la qualité des anodes et de compenser la variabilité provenant des matières premières. Un système d’inspection basé sur les techniques d’analyse modale et d’acousto-ultrasonique est proposé pour contrôler la qualité des anodes de manière rapide et non destructive. Les données massives (modes de vibration et signaux acoustiques) ont été analysées à l'aide de méthodes statistiques à variables latentes, telles que l'Analyse en Composantes Principales (ACP) et la Projection sur les Structures Latentes (PSL), afin de regrouper les anodes testées en fonction de leurs signatures vibratoires et acousto-ultrasoniques. Le système d'inspection a été premièrement investigué sur des tranches d'anodes industrielles et ensuite testé sur plusieurs anodes pleine grandeur produites sous différentes conditions à l’usine de Alcoa Deschambault au Québec (ADQ). La méthode proposée a permis de distinguer les anodes saines de celles contenant des défauts ainsi que d’identifier le type et la sévérité des défauts, et de les localiser. La méthode acousto-ultrasonique a été validée qualitativement par la tomographie à rayon-X, pour les analyses des tranches d’anodes. Pour les tests réalisés sur les blocs d’anode, la validation a été réalisée au moyen de photos recueillies après avoir coupé certaines anodes parmi celles testées.The performance of the Hall-Héroult electrolysis reduction process used for the industrial aluminium smelting is strongly influenced by the quality of carbon anodes, particularly by the presence of defects in their internal structure, such as cracks, pores and heterogeneities. This is partly due to the decreasing quality and increasing variability of the raw materials available on the market as well as the frequent suppliers changes made in order to meet the smelter’s specifications and to reduce purchasing costs. However, the anode producers are not prepared to cope with these variations and in order to maintain consistent anode quality. Consequently, it becomes a priority to develop alternative methods for inspecting each anode block to improve quality control and maintain consistent anode quality in spite of the variability of incoming raw materials.A rapid and non-destructive inspection system for anode quality control is proposed based on modal analysis and acousto-ultrasonic techniques. The large set of vibration and acousto-ultrasonic data collected from baked anode materials was analyzed using multivariate latent variable methods, such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), in order to cluster the tested anodes based on vibration and their acousto-ultrasonic signatures. The inspection system was investigated first using slices collected from industrial anodes and then on several full size anodes produced under different conditions at the Alcoa Deschambault in Québec (ADQ). It is shown that the proposed method allows discriminating defect-free anodes from those containing various types of defects. In addition, the acousto-ultrasonic features obtained in different frequency ranges were found to be sensitive to the defects severities and were able to locate them in anode blocks. The acousto-ultrasonic method was validated qualitatively using X-ray computed tomography, when studying the anode slices. The results obtained on the full size anode blocks were validated by means of images collected after cutting some tested anodes
Technical Readiness and Gaps Analysis of Commercial Optical Materials and Measurement Systems for Advanced Small Modular Reactors
This report intends to support Department of Energy’s Office of Nuclear Energy (DOE-NE) Nuclear Energy Research and Development Roadmap and industry stakeholders by evaluating optical-based instrumentation and control (I&C) concepts for advanced small modular reactor (AdvSMR) applications. These advanced designs will require innovative thinking in terms of engineering approaches, materials integration, and I&C concepts to realize their eventual viability and deployability. The primary goals of this report include: 1. Establish preliminary I&C needs, performance requirements, and possible gaps for AdvSMR designs based on best available published design data. 2. Document commercial off-the-shelf (COTS) optical sensors, components, and materials in terms of their technical readiness to support essential AdvSMR in-vessel I&C systems. 3. Identify technology gaps by comparing the in-vessel monitoring requirements and environmental constraints to COTS optical sensor and materials performance specifications. 4. Outline a future research, development, and demonstration (RD&D) program plan that addresses these gaps and develops optical-based I&C systems that enhance the viability of future AdvSMR designs. The development of clean, affordable, safe, and proliferation-resistant nuclear power is a key goal that is documented in the Nuclear Energy Research and Development Roadmap. This roadmap outlines RD&D activities intended to overcome technical, economic, and other barriers, which currently limit advances in nuclear energy. These activities will ensure that nuclear energy remains a viable component to this nation’s energy security
A MULTI-STAGE DECISION SUPPORT MODEL FOR COORDINATED SUSTAINABLE PRODUCT AND SUPPLY CHAIN DESIGN
In this research, a decision support model for coordinating sustainable product and supply chain design decisions is developed using a multi-stage hierarchical approach. The model evaluates alternate product designs and their corresponding supply chain configurations to identify the best product design and the corresponding supply chain configuration that maximizes the economic, environmental and societal benefits. The model considers a total life-cycle approach and incorporates closed-loop flow among multiple product lifecycles. In the first stage, a mixed integer linear programming model is developed to select for each product design an optimal supply chain configuration that maximizes the profit. In the subsequent stages, the economic, environmental and societal multiple life-cycle analysis models are developed which assess the economic, environment and the societal performance of each product design and its optimal supply chain configuration to identify the best product design with highest sustainability benefits.
The decision support model is applied for an example problem to illustrate the procedure for identifying the best sustainable design. Later, the model is applied for a real-time refrigerator case to identify the best refrigerator design that maximizes economic, environmental and societal benefits. Further, sensitivity analysis is performed on the optimization model to study the closed-loop supply chain behavior under various situations. The results indicated that both product and supply chain design criteria significantly influence the performance of the supply chain. The results provided insights into closed-loop supply chain models and their behavior under various situations. Decision support models such as above can help a company identify the best designs that bring highest sustainability benefits, can provide a manager with holistic view and the impact of their design decisions on the supply chain performance and also provide areas for improvement
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