94 research outputs found
Character Recognition
Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field
Adaptive Methods for Robust Document Image Understanding
A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy
Very High Resolution (VHR) Satellite Imagery: Processing and Applications
Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing
Optimisation of microfluidic experiments for model calibration of a synthetic promoter in S. cerevisiae
This thesis explores, implements, and examines the methods to improve the
efficiency of model calibration experiments for synthetic biological circuits in
three aspects: experimental technique, optimal experimental design (OED),
and automatic experiment abnormality screening (AEAS). Moreover, to obtain
a specific benchmark that provides clear-cut evidence of the utility, an
integrated synthetic orthogonal promoter in yeast (S. cerevisiae) and a
corresponded model is selected as the experiment object.
This work first focuses on the “wet-lab” part of the experiment. It verifies the
theoretical benefit of adopting microfluidic technique by carrying out a series
of in-vivo experiments on a developed automatic microfluidic experimental
platform. Statistical analysis shows that compared to the models calibrated
with flow-cytometry data (a representative traditional experimental technique),
the models based on microfluidic data of the same experiment time give
significantly more accurate behaviour predictions of never-encountered stimuli
patterns. In other words, compare to flow-cytometry experiments, microfluidics
can obtain models of the required prediction accuracy within less experiment
time.
The next aspect is to optimise the “dry-lab” part, i.e., the design of experiments
and data processing. Previous works have proven that the informativeness of
experiments can be improved by optimising the input design (OID). However,
the amount of work and the time cost of the current OID approach rise
dramatically with large and complex synthetic networks and mathematical
models. To address this problem, this thesis introduces the parameter
clustering analysis and visualisation (PCAV) to speed up the OID by narrowing
down the parameters of interest. For the first time, this thesis proposes a
parameter clustering algorithm based on the Fisher information matrix
(FIMPC). Practices with in-silico experiments on the benchmarking promoter
show that PCAV reduces the complexity of OID and provides a new way to
explore the connections between parameters. Moreover, the analysis shows
that experiments with FIMPC-based OID lead to significantly more accurate
parameter estimations than the current OID approach.
Automatic abnormality screening is the third aspect. For microfluidic
experiments, the current identification of invalid microfluidic experiments is
carried out by visual checks of the microscope images by experts after the
experiments. To improve the automation level and robustness of this quality
control process, this work develops an automatic experiment abnormality
screening (AEAS) system supported by convolutional neural networks (CNNs).
The system learns the features of six abnormal experiment conditions from
images taken in actual microfluidic experiments and achieves identification
within seconds in the application. The training and validation of six
representative CNNs of different network depths and design strategies show
that some shallow CNNs can already diagnose abnormal conditions with the
desired accuracy. Moreover, to improve the training convergence of deep
CNNs with small data sets, this thesis proposes a levelled-training method and
improves the chance of convergence from 30% to 90%.
With a benchmark of a synthetic promoter model in yeast, this thesis optimises
model calibration experiments in three aspects to achieve a more efficient
procedure: experimental technique, optimal experimental design (OED), and
automatic experiment abnormality screening (AEAS). In this study, the
efficiency of model calibration experiments for the benchmarking model can
be improved by: adopting microfluidics technology, applying CAVP parameter
analysis and FIMPC-based OID, and setting up an AEAS system supported
by CNN. These contributions have the potential to be exploited for designing
more efficient in-vivo experiments for model calibration in similar studies
Sistemas automáticos de informação e segurança para apoio na condução de veículos
Doutoramento em Engenharia MecânicaO objeto principal desta tese é o estudo de algoritmos de processamento
e representação automáticos de dados, em particular de informação
obtida por sensores montados a bordo de veículos (2D e
3D), com aplicação em contexto de sistemas de apoio à condução.
O trabalho foca alguns dos problemas que, quer os sistemas de condução
automática (AD), quer os sistemas avançados de apoio à condução
(ADAS), enfrentam hoje em dia. O documento é composto por
duas partes. A primeira descreve o projeto, construção e desenvolvimento
de três protótipos robóticos, incluindo pormenores associados
aos sensores montados a bordo dos robôs, algoritmos e arquitecturas
de software. Estes robôs foram utilizados como plataformas de ensaios
para testar e validar as técnicas propostas. Para além disso, participaram
em várias competições de condução autónoma tendo obtido
muito bons resultados. A segunda parte deste documento apresenta
vários algoritmos empregues na geração de representações intermédias
de dados sensoriais. Estes podem ser utilizados para melhorar
técnicas já existentes de reconhecimento de padrões, deteção ou navegação,
e por este meio contribuir para futuras aplicações no âmbito dos
AD ou ADAS. Dado que os veículos autónomos contêm uma grande
quantidade de sensores de diferentes naturezas, representações intermédias
são particularmente adequadas, pois podem lidar com problemas
relacionados com as diversas naturezas dos dados (2D, 3D, fotométrica,
etc.), com o carácter assíncrono dos dados (multiplos sensores
a enviar dados a diferentes frequências), ou com o alinhamento
dos dados (problemas de calibração, diferentes sensores a disponibilizar
diferentes medições para um mesmo objeto). Neste âmbito,
são propostas novas técnicas para a computação de uma representação
multi-câmara multi-modal de transformação de perspectiva inversa,
para a execução de correcção de côr entre imagens de forma a
obter mosaicos de qualidade, ou para a geração de uma representação
de cena baseada em primitivas poligonais, capaz de lidar com grandes
quantidades de dados 3D e 2D, tendo inclusivamente a capacidade
de refinar a representação à medida que novos dados sensoriais são
recebidos.The main object of this thesis is the study of algorithms for automatic information
processing and representation, in particular information provided
by onboard sensors (2D and 3D), to be used in the context of
driving assistance. The work focuses on some of the problems facing
todays Autonomous Driving (AD) systems and Advanced Drivers Assistance
Systems (ADAS). The document is composed of two parts.
The first part describes the design, construction and development of
three robotic prototypes, including remarks about onboard sensors, algorithms
and software architectures. These robots were used as test
beds for testing and validating the developed techniques; additionally,
they have participated in several autonomous driving competitions with
very good results. The second part of this document presents several
algorithms for generating intermediate representations of the raw
sensor data. They can be used to enhance existing pattern recognition,
detection or navigation techniques, and may thus benefit future
AD or ADAS applications. Since vehicles often contain a large amount
of sensors of different natures, intermediate representations are particularly
advantageous; they can be used for tackling problems related
with the diverse nature of the data (2D, 3D, photometric, etc.), with the
asynchrony of the data (multiple sensors streaming data at different
frequencies), or with the alignment of the data (calibration issues, different
sensors providing different measurements of the same object).
Within this scope, novel techniques are proposed for computing a multicamera
multi-modal inverse perspective mapping representation, executing
color correction between images for obtaining quality mosaics, or
to produce a scene representation based on polygonal primitives that
can cope with very large amounts of 3D and 2D data, including the
ability of refining the representation as new information is continuously
received
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