Computer analysis of composite documents with non-uniform background.

The motivation behind most of the applications of off-line text recognition is to convert data from conventional media into electronic media. Such applications are bank cheques, security documents and form processing. In this dissertation a document analysis system is presented to transfer gray level composite documents with complex backgrounds and poor illumination into electronic format that is suitable for efficient storage, retrieval and interpretation. The preprocessing stage for the document analysis system requires the conversion of a paper-based document to a digital bit-map representation after optical scanning followed by techniques of thresholding, skew detection, page segmentation and Optical Character Recognition (OCR). The system as a whole operates in a pipeline fashion where each stage or process passes its output to the next stage. The success of each stage guarantees that the operation of the system as a whole with no failures that may reduce the character recognition rate. By designing this document analysis system a new local bi-level threshold selection technique was developed for gray level composite document images with non-uniform background. The algorithm uses statistical and textural feature measures to obtain a feature vector for each pixel from a window of size (2 n + 1) x (2n + 1), where n ≥ 1. These features provide a local understanding of pixels from their neighbourhoods making it easier to classify each pixel into its proper class. A Multi-Layer Perceptron Neural Network is then used to classify each pixel value in the image. The results of thresholding are then passed to the block segmentation stage. The block segmentation technique developed is a feature-based method that uses a Neural Network classifier to automatically segment and classify the image contents into text and halftone images. Finally, the text blocks are passed into a Character Recognition (CR) system to transfer characters into an editable text format and the recognition results were compared to those obtained from a commercial OCR. The OCR system implemented uses pixel distribution as features extracted from different zones of the characters. A correlation classifier is used to recognize the characters. For the application of cheque processing, this system was used to read the special numerals of the optical barcode found in bank cheques. The OCR system uses a fuzzy descriptive feature extraction method with a correlation classifier to recognize these special numerals, which identify the bank institute and provides personal information about the account holder. The new local thresholding scheme was tested on a variety of composite document images with complex backgrounds. The results were very good compared to the results from commercial OCR software. This proposed thresholding technique is not limited to a specific application. It can be used on a variety of document images with complex backgrounds and can be implemented in any document analysis system provided that sufficient training is performed.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A445. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1061. Advisers: Maher Sid-Ahmed; Majid Ahmadi. Thesis (Ph.D.)--University of Windsor (Canada), 2004

Intelligent simulation of coastal ecosystems

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto, Faculdade de Ciência e Tecnologia. Universidade Fernando Pessoa. 201

Medical image synthesis using generative adversarial networks: towards photo-realistic image synthesis

This proposed work addresses the photo-realism for synthetic images. We introduced a modified generative adversarial network: StencilGAN. It is a perceptually-aware generative adversarial network that synthesizes images based on overlaid labelled masks. This technique can be a prominent solution for the scarcity of the resources in the healthcare sector

Machine learning for property prediction and optimization of polymeric nanocomposites: a state-of-the-art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted

Local Accuracy and Global Consistency for Efficient SLAM

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM) using visual data only. Given the video stream of a moving camera, we wish to estimate the structure of the environment and the motion of the device most accurately and in real-time. Two effective approaches were presented in the past. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods rely on the optimisation approach of bundle adjustment, but computationally must select only a small number of past frames to process. We perform a rigorous comparison between the two approaches for visual SLAM. Especially, we show that accuracy comes from a large number of points, while the number of intermediate frames only has a minor impact. We conclude that keyframe bundle adjustment is superior to ltering due to a smaller computational cost. Based on these experimental results, we develop an efficient framework for large-scale visual SLAM using the keyframe strategy. We demonstrate that SLAM using a single camera does not only drift in rotation and translation, but also in scale. In particular, we perform large-scale loop closure correction using a novel variant of pose-graph optimisation which also takes scale drift into account. Starting from this two stage approach which tackles local motion estimation and loop closures separately, we develop a unified framework for real-time visual SLAM. By employing a novel double window scheme, we present a constant-time approach which enables the local accuracy of bundle adjustment while ensuring global consistency. Furthermore, we suggest a new scheme for local registration using metric loop closures and present several improvements for the visual front-end of SLAM. Our contributions are evaluated exhaustively on a number of synthetic experiments and real-image data-set from single cameras and range imaging devices

Local Accuracy and Global Consistency for Efficient SLAM

This thesis is concerned with the problem of Simultaneous Localisation and Mapping (SLAM) using visual data only. Given the video stream of a moving camera, we wish to estimate the structure of the environment and the motion of the device most accurately and in real-time. Two effective approaches were presented in the past. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods rely on the optimisation approach of bundle adjustment, but computationally must select only a small number of past frames to process. We perform a rigorous comparison between the two approaches for visual SLAM. Especially, we show that accuracy comes from a large number of points, while the number of intermediate frames only has a minor impact. We conclude that keyframe bundle adjustment is superior to ltering due to a smaller computational cost. Based on these experimental results, we develop an efficient framework for large-scale visual SLAM using the keyframe strategy. We demonstrate that SLAM using a single camera does not only drift in rotation and translation, but also in scale. In particular, we perform large-scale loop closure correction using a novel variant of pose-graph optimisation which also takes scale drift into account. Starting from this two stage approach which tackles local motion estimation and loop closures separately, we develop a unified framework for real-time visual SLAM. By employing a novel double window scheme, we present a constant-time approach which enables the local accuracy of bundle adjustment while ensuring global consistency. Furthermore, we suggest a new scheme for local registration using metric loop closures and present several improvements for the visual front-end of SLAM. Our contributions are evaluated exhaustively on a number of synthetic experiments and real-image data-set from single cameras and range imaging devices

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Variational methods and its applications to computer vision

Many computer vision applications such as image segmentation can be formulated in a ''variational'' way as energy minimization problems. Unfortunately, the computational task of minimizing these energies is usually difficult as it generally involves non convex functions in a space with thousands of dimensions and often the associated combinatorial problems are NP-hard to solve. Furthermore, they are ill-posed inverse problems and therefore are extremely sensitive to perturbations (e.g. noise). For this reason in order to compute a physically reliable approximation from given noisy data, it is necessary to incorporate into the mathematical model appropriate regularizations that require complex computations. The main aim of this work is to describe variational segmentation methods that are particularly effective for curvilinear structures. Due to their complex geometry, classical regularization techniques cannot be adopted because they lead to the loss of most of low contrasted details. In contrast, the proposed method not only better preserves curvilinear structures, but also reconnects some parts that may have been disconnected by noise. Moreover, it can be easily extensible to graphs and successfully applied to different types of data such as medical imagery (i.e. vessels, hearth coronaries etc), material samples (i.e. concrete) and satellite signals (i.e. streets, rivers etc.). In particular, we will show results and performances about an implementation targeting new generation of High Performance Computing (HPC) architectures where different types of coprocessors cooperate. The involved dataset consists of approximately 200 images of cracks, captured in three different tunnels by a robotic machine designed for the European ROBO-SPECT project.Open Acces

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability