15 research outputs found
Detection of dirt impairments from archived film sequences : survey and evaluations
Film dirt is the most commonly encountered artifact in archive restoration applications. Since dirt usually appears as a temporally impulsive event, motion-compensated interframe processing is widely applied for its detection. However, motion-compensated prediction requires a high degree of complexity and can be unreliable when motion estimation fails. Consequently, many techniques using spatial or spatiotemporal filtering without motion were also been proposed as alternatives. A comprehensive survey and evaluation of existing methods is presented, in which both qualitative and quantitative performances are compared in terms of accuracy, robustness, and complexity. After analyzing these algorithms and identifying their limitations, we conclude with guidance in choosing from these algorithms and promising directions for future research
Hyperspectral, thermal and LiDAR remote sensing for red band needle blight detection in pine plantation forests
PhD ThesisClimate change indirectly affects the distribution and abundance of forest insect pests and
pathogens, as well as the severity of tree diseases. Red band needle blight is a disease
which has a particularly significant economic impact on pine plantation forests
worldwide, affecting diameter and height growth. Monitoring its spread and intensity is
complicated by the fact that the diseased trees are often only visible from aircraft in the
advanced stages of the epidemic. There is therefore a need for a more robust method to
map the extent and severity of the disease. This thesis examined the use of a range of
remote sensing techniques and instrumentation, including thermography, hyperspectral
imaging and laser scanning, for the identification of tree stress symptoms caused by the
onset of red band needle blight. Three study plots, located in a plantation forest within
the Loch Lomond and the Trossachs National Park that exhibited a range of red band
needle blight infection levels, were established and surveyed. Airborne hyperspectral and
LiDAR data were acquired for two Lodgepole pine stands, whilst for one Scots pine stand,
airborne LiDAR and Unmanned Aerial Vehicle-borne (UAV-borne) thermal imagery
were acquired alongside leaf spectroscopic measurements. Analysis of the acquired data
demonstrated the potential for the use of thermographic, hyperspectral and LiDAR
sensors for detection of red band needle blight-induced changes in pine trees. The three
datasets were sensitive to different disease symptoms, i.e. thermography to alterations in
transpiration, LiDAR to defoliation, and hyperspectral imagery to changes in leaf
biochemical properties. The combination of the sensors could therefore enhance the
ability to diagnose the infection.Natural Environment Research Council (NERC) for funding
this PhD program (studentship award 1368552) and providing access to specialist
equipment through a Field Spectroscopy Facility loan (710.114). I would like to thank
NERC Airborne Research Facility for providing airborne data (grant: GB 14-04) that
made the PhD a challenge, to say the least. My sincere gratitude goes to the Douglas
Bomford Trust for providing additional funds, which allowed for completion of the
UAV-borne part of this research
Estudo e análise de Redes Neurais Convolucionais Profundas na identificação de doenças em plantas por imagens
Tese (doutorado) — Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2022.Rede Neurais Convolucionais (CNNs), demonstram um potencial para tarefas relacionadas à Visão Computacional. A característica de maior destaque das CNNs é sua capacidade de explorar a correlação espacial ou temporal nos dados. Assim, várias melhorias na metodologia e arquitetura de aprendizagem das redes foram realizadas para tornar as CNNs escaláveis para problemas grandes, heterogêneos, complexos e multiclasses. A agricultura delimita um escopo de problemas desafiadores, que carecem de tecnologias para proporcionar maior incremento na produção agrícola, principalmente em relação ao enfrentamento de doenças. As doenças de plantas são consideradas um dos principais fatores que influenciam a produção de alimentos, e a sua identificação é primordialmente realizada por técnicas manuais ou por microscopia, oque aumenta o tempo de diagnóstico e as possibilidades de erro. Soluções automatizadas de identificação de doenças de plantas, usando imagens e aprendizado de máquina, em especial as CNNs, têm proporcionado avanços significativos. Entretanto, a maioria das abordagens possui baixa capacidade de classificação, tendo como agravante as infestações simultâneas por diferentes patógenos e as confusões sintomáticas causadas por fatores abióticos. Assim, o objetivo deste trabalho é analisar e avaliar as arquiteturas CNNs, explorando potencialidades e prospectando novos arranjos de arquitetura para classificar doenças de plantas e identificar patógenos. A abordagem fez uso de uma estratégia de customização, na qual redes operativas independentes ou blocos convolucionais são integradas em um único modelo para capturar um conjunto mais variado de características. A NEMANeté um resultado relevante desta abordagem de customização de CNNs para classificação de fitonematoides em imagens microscópicas. O mo-delo alcançou a melhor taxa de acurácia atingindo 99,35%, possibilitando melhorias gerais de precisão superiores a 6,83% e 4,1%, para treinamento com inicialização dos pesos e para transferência de aprendizagem, em comparação com outras arquiteturas avaliadas. Os resultados demonstraram que a customização de arquiteturas CNNs é uma abordagem promissora para o aumento de ganhos em termo de acurácia, convergência das redes e tamanho dos modelos.Convolutional Neural Networks (CNNs) demonstrate a potential for computer vision tasks.The most prominent feature of CNNs is their ability to explore spatial or temporal correlationin the data. Thus, several improvements in the methodology and architecture of learning of thenetworks were made to make the CNNs scalable for large, heterogeneous, complex, and multi-class problems. Agriculture delimits a scope of challenging problems, which lack technologiesto increase agricultural production, especially about coping with diseases. Plant diseases areconsidered one of the main factors that influence food production, and their identification is pri-marily performed by manual techniques or microscopy, which increases the time of diagnosisand the possibility of errors. Using imaging and machine learning, especially CNNs, automatedplant disease identification solutions have provided significant advances. However, most appro-aches have low classification capacity, with simultaneous infestations by different pathogensand symptomatic confusion caused by abiotic factors as an aggravating factor. Thus, this workaims to analyze and evaluate CNN architectures, exploring potentialities and prospecting newarchitectural arrangements to classify plant diseases and identify pathogens. The approach useda customization strategy, in which independent operative networks or convolutional blocks areintegrated into a single model to capture a more varied set of characteristics. TheNEMANetis arelevant result of this CNN customization approach for the classification of phytonematodes inmicroscopic images. The model achieved the best accuracy rate reaching 99.35%, enabling ove-rall accuracy improvements greater than 6.83% and 4.1%, for weight initialization training andlearning transfer, compared to other evaluated architectures. The results showed that the custo-mization of CNN architectures is a promising approach to increase gains in terms of accuracy,the convergence of networks, and the size of the model
Commissioning and First Science Results of the Desert Fireball Network: a Global-Scale Automated Survey for Large Meteoroid Impacts
This thesis explores the first results from the Desert Fireball Network, a distributed global observatory designed to characterise fireballs caused by meteoroid impacts. To deal with the >50 terabytes of data influx per week, innovative data reduction techniques have been developed. The science topics investigated in this work include airbursts caused by large meteoroids impacting the Earth's atmosphere, the recovery of a meteorite and its orbital history, and the structure of a meteor shower
Social work with airports passengers
Social work at the airport is in to offer to passengers social services. The main
methodological position is that people are under stress, which characterized by a
particular set of characteristics in appearance and behavior. In such circumstances
passenger attracts in his actions some attention. Only person whom he trusts can help him
with the documents or psychologically