A Stochastic Modeling Approach to Region-and Edge-Based Image Segmentation

The purpose of image segmentation is to isolate objects in a scene from the background. This is a very important step in any computer vision system since various tasks, such as shape analysis and object recognition, require accurate image segmentation. Image segmentation can also produce tremendous data reduction. Edge-based and region-based segmentation have been examined and two new algorithms based on recent results in random field theory have been developed. The edge-based segmentation algorithm uses the pixel gray level intensity information to allocate object boundaries in two stages: edge enhancement, followed by edge linking. Edge enhancement is accomplished by maximum energy filters used in one-dimensional bandlimited signal analysis. The issue of optimum filter spatial support is analyzed for ideal edge models. Edge linking is performed by quantitative sequential search using the Stack algorithm. Two probabilistic search metrics are introduced and their optimality is proven and demonstrated on test as well as real scenes. Compared to other methods, this algorithm is shown to produce more accurate allocation of object boundaries. Region-based segmentation was modeled as a MAP estimation problem in which the actual (unknown) objects were estimated from the observed (known) image by a recursive classification algorithms. The observed image was modeled by an Autoregressive (AR) model whose parameters were estimated locally, and a Gibbs-Markov random field (GMRF) model was used to model the unknown scene. A computational study was conducted on images having various types of texture images. The issues of parameter estimation, neighborhood selection, and model orders were examined. It is concluded that the MAP approach for region segmentation generally works well on images having a large content of microtextures which can be properly modeled by both AR and GMRF models. On these texture images, second order AR and GMRF models were shown to be adequate

Improved terrain type classification using UAV downwash dynamic texture effect

The ability to autonomously navigate in an unknown, dynamic environment, while at the same time classifying various terrain types, are significant challenges still faced by the computer vision research community. Addressing these problems is of great interest for the development of collaborative autonomous navigation robots. For example, an Unmanned Aerial Vehicle (UAV) can be used to determine a path, while an Unmanned Surface Vehicle (USV) follows that path to reach the target destination. For the UAV to be able to determine if a path is valid or not, it must be able to identify the type of terrain it is flying over. With the help of its rotor air flow (known as downwash e↵ect), it becomes possible to extract advanced texture features, used for terrain type classification. This dissertation presents a complete analysis on the extraction of static and dynamic texture features, proposing various algorithms and analyzing their pros and cons. A UAV equipped with a single RGB camera was used to capture images and a Multilayer Neural Network was used for the automatic classification of water and non-water-type terrains by means of the downwash e↵ect created by the UAV rotors. The terrain type classification results are then merged into a georeferenced dynamic map, where it is possible to distinguish between water and non-water areas in real time. To improve the algorithms’ processing time, several sequential processes were con verted into parallel processes and executed in the UAV onboard GPU with the CUDA framework achieving speedups up to 10x. A comparison between the processing time of these two processing modes, sequential in the CPU and parallel in the GPU, is also presented in this dissertation. All the algorithms were developed using open-source libraries, and were analyzed and validated both via simulation and real environments. To evaluate the robustness of the proposed algorithms, the studied terrains were tested with and without the presence of the downwash e↵ect. It was concluded that the classifier could be improved by per forming combinations between static and dynamic features, achieving an accuracy higher than 99% in the classification of water and non-water terrain.Dotar equipamentos moveis da funcionalidade de navegação autónoma em ambientes desconhecidos e dinâmicos, ao mesmo tempo que, classificam terrenos do tipo água e não água, são desafios que se colocam atualmente a investigadores na área da visão computacional. As soluções para estes problemas são de grande interesse para a navegação autónoma e a colaboração entre robôs. Por exemplo, um veículo aéreo não tripulado (UAV) pode ser usado para determinar o caminho que um veículo terrestre não tripulado (USV) deve percorrer para alcançar o destino pretendido. Para o UAV conseguir determinar se o caminho é válido ou não, tem de ser capaz de identificar qual o tipo de terreno que está a sobrevoar. Com a ajuda do fluxo de ar gerado pelos motores (conhecido como efeito downwash), é possível extrair características de textura avançadas, que serão usadas para a classificação do tipo de terreno. Esta dissertação apresenta uma análise completa sobre extração de texturas estáticas e dinâmicas, propondo diversos algoritmos e analisando os seus prós e contras. Um UAV equipado com uma única câmera RGB foi usado para capturar as imagens. Para classi ficar automaticamente terrenos do tipo água e não água foi usada uma rede neuronal multicamada e recorreu-se ao efeito de downwash criado pelos motores do UAV. Os re sultados da classificação do tipo de terreno são depois colocados num mapa dinâmico georreferenciado, onde é possível distinguir, em tempo real, terrenos do tipo água e não água. De forma a melhorar o tempo de processamento dos algoritmos desenvolvidos, vários processos sequenciais foram convertidos em processos paralelos e executados na GPU a bordo do UAV, com a ajuda da framework CUDA, tornando o algoritmo até 10x mais rápido. Também são apresentadas nesta dissertação comparações entre o tempo de processamento destes dois modos de processamento, sequencial na CPU e paralelo na GPU. Todos os algoritmos foram desenvolvidos através de bibliotecas open-source, e foram analisados e validados, tanto através de ambientes de simulação como em ambientes reais. Para avaliar a robustez dos algoritmos propostos, os terrenos estudados foram testados com e sem a presença do efeito downwash. Concluiu-se que o classificador pode ser melhorado realizando combinações entre as características de textura estáticas e dinâmicas, alcançando uma precisão superior a 99% na classificação de terrenos do tipo água e não água

CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

Uncertainty Estimation: single forward pass methods and applications in Active Learning

Machine Learning (ML) models are now powerful enough to be used in complex automated decision-making settings such as autonomous driving and medical diagnosis. Despite being very accurate in general, these models do still make mistakes. A critical factor in being able to depend on such models is that they can quantify the uncertainty of their predictions, and it is paramount that this is taken into account by users of the model. Unfortunately, deep learning models cannot readily express their uncertainty, rendering them unsafe for many real-world applications. Bayesian modelling provides a mathematical framework for learning models that can express their uncertainty. However, exact Bayesian methods are computationally expensive to learn and evaluate, and approximate methods often reduce accuracy or are still prohibitively expensive. Meanwhile, ML models continue to increase in number of parameters, meaning that one has to make a decision between being (more) Bayesian or using a larger model. So far it has always fallen in favour of larger models. Instead of building on Bayesian methods, we deconstruct uncertainty estimation and formulate desiderata that we base our work on throughout the thesis (Chapter 1). In Chapter 3, we introduce a new model (DUQ) that is able to estimate uncertainty in a single forward pass by carefully constructing the model’s parameter and output space based on the desiderata. We then extend this model in Chapter 4 (DUE) by placing it in the framework provided by Deep Kernel Learning. This enables the model to work well for both classification and regression tasks (as opposed to just classification), and estimate uncertainty over a batch of inputs jointly. Both models are competitive with standard softmax models in terms of accuracy and speed, while having significantly improved uncertainty estimation. We additionally consider the problem of Active Learning (AL), where the goal is to maximise label efficiency by selecting only the most informative data points to be labelled. In Section 4.5, we evaluate the DUE model in AL for personalised healthcare. Here, the labelled dataset needs to adhere to specific assumptions made in causal inference, which makes this a challenging problem. In Chapter 5, we look at AL in the batch setting. We show that current methods do not select diverse batches of data, and we introduce a principled method to overcome this issue. Building upon deep kernel learning, this thesis provides a compelling foundation for single forward pass uncertainty and advances the state of the art in active learning. In the conclusions (Section 6, and at the end of each chapter), we discuss how users of ML models could make use of these tools for making sound and confident decisions

Studies of algorithms and related imaging techniques for industrial inspection

This thesis will deal with algorithms and imaging techniques for use in automated industrial inspection. The work falls into two main areas, the first dealing with general problems relating to typical inspection tasks, the second with specific applications including the analysis of seals on plastic packets.The requirements of a general object location and inspection system will be discussed initially in relation to algorithms supplied with commercial systems, which often seem ad-hoc. This will be followed up with detailed analyses of several corner and small hole detection algorithms. The features looked for in a useful algorithm are: (1) a high execution speed when implemented on a general purpose microcomputer, (2) good accuracy in locating the desired features, (3) robustness when faced with poor quality, noisy or cluttered images and (4) the ability to distinguish between genuine features and others that appear, superficially, to be similar. A program using these feature detectors to locate partially occluded machine parts in typical images will be presented.The second main area of investigation is that of the detection of faults in heat sealed food packets and is one which has hitherto largely been overlooked. The main problem with these packets is that the cellophane wrapper is highly reflective, giving rise to large areas of glare in any off-camera image. Experience has shown that careful lighting arrangement alone will never totally remove this problem. However, a simple arrangement of switched light beams, along with computer processing, can almost totally eliminate the glare. This approach has been used in the inspection of packets where faults are revealed by parts of the product inside showing through holes in the wrapper. Alternatively, by careful alignment of the light sources, the surface structure of the sealed part of a packet may be revealed. This can reveal defects either through the absence of a regular pattern, or by the presence of wrinkles running across the seal. Algorithms have been developed demonstrating each of these inspection tasks.Overall the work presented in this thesis has spanned several traditional areas of interest, and has also developed the techniques required for packet inspection and other situations where glare is a problem.<p

Analysis of textural image features for content based retrieval

Digital archaelogy and virtual reality with archaeological artefacts have been quite hot research topics in the last years 55,56 . This thesis is a preperation study to build the background knowledge required for the research projects, which aim to computerize the reconstruction of the archaelogical data like pots, marbles or mosaic pieces by shape and ex ural features. Digitalization of the cultural heritage may shorten the reconstruction time which takes tens of years currently 61 ; it will improve the reconstruction robustness by incorporating with the literally available machine vision algorithms and experiences from remote experts working on a no-cost virtual object together. Digitalization can also ease the exhibition of the results for regular people, by multiuser media applications like internet based virtual museums or virtual tours. And finally, it will make possible to archive values with their original texture and shapes for long years far away from the physical risks that the artefacts currently face. On the literature 1,2,3,5,8,11,14,15,16 , texture analysis techniques have been throughly studied and implemented for the purpose of defect analysis purposes by image processing and machine vision scientists. In the last years, these algorithms have been started to be used for similarity analysis of content based image retrieval 1,4,10 . For retrieval systems, the concurrent problems seem to be building efficient and fast systems, therefore, robust image features haven't been focused enough yet. This document is the first performance review of the texture algorithms developed for retrieval and defect analysis together. The results and experiences gained during the thesis study will be used to support the studies aiming to solve the 2D puzzle problem using textural continuity methods on archaelogical artifects, Appendix A for more detail. The first chapter is devoted to learn how the medicine and psychology try to explain the solutions of similiarity and continuity analysis, which our biological model, the human vision, accomplishes daily. In the second chapter, content based image retrieval systems, their performance criterias, similiarity distance metrics and the systems available have been summarized. For the thesis work, a rich texture database has been built, including over 1000 images in total. For the ease of the users, a GUI and a platform that is used for content based retrieval has been designed; The first version of a content based search engine has been coded which takes the source of the internet pages, parses the metatags of images and downloads the files in a loop controlled by our texture algorithms. The preprocessing algorithms and the pattern analysis algorithms required for the robustness of the textural feature processing have been implemented. In the last section, the most important textural feature extraction methods have been studied in detail with the performance results of the codes written in Matlab and run on different databases developed