Study of Computational Image Matching Techniques: Improving Our View of Biomedical Image Data

Image matching techniques are proven to be necessary in various fields of science and engineering, with many new methods and applications introduced over the years. In this PhD thesis, several computational image matching methods are introduced and investigated for improving the analysis of various biomedical image data. These improvements include the use of matching techniques for enhancing visualization of cross-sectional imaging modalities such as Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), denoising of retinal Optical Coherence Tomography (OCT), and high quality 3D reconstruction of surfaces from Scanning Electron Microscope (SEM) images. This work greatly improves the process of data interpretation of image data with far reaching consequences for basic sciences research. The thesis starts with a general notion of the problem of image matching followed by an overview of the topics covered in the thesis. This is followed by introduction and investigation of several applications of image matching/registration in biomdecial image processing: a) registration-based slice interpolation, b) fast mesh-based deformable image registration and c) use of simultaneous rigid registration and Robust Principal Component Analysis (RPCA) for speckle noise reduction of retinal OCT images. Moving towards a different notion of image matching/correspondence, the problem of view synthesis and 3D reconstruction, with a focus on 3D reconstruction of microscopic samples from 2D images captured by SEM, is considered next. Starting from sparse feature-based matching techniques, an extensive analysis is provided for using several well-known feature detector/descriptor techniques, namely ORB, BRIEF, SURF and SIFT, for the problem of multi-view 3D reconstruction. This chapter contains qualitative and quantitative comparisons in order to reveal the shortcomings of the sparse feature-based techniques. This is followed by introduction of a novel framework using sparse-dense matching/correspondence for high quality 3D reconstruction of SEM images. As will be shown, the proposed framework results in better reconstructions when compared with state-of-the-art sparse-feature based techniques. Even though the proposed framework produces satisfactory results, there is room for improvements. These improvements become more necessary when dealing with higher complexity microscopic samples imaged by SEM as well as in cases with large displacements between corresponding points in micrographs. Therefore, based on the proposed framework, a new approach is proposed for high quality 3D reconstruction of microscopic samples. While in case of having simpler microscopic samples the performance of the two proposed techniques are comparable, the new technique results in more truthful reconstruction of highly complex samples. The thesis is concluded with an overview of the thesis and also pointers regarding future directions of the research using both multi-view and photometric techniques for 3D reconstruction of SEM images

Correspondence of three-dimensional objects

First many thanks go to Prof. Hans du Buf, for his supervision based on his experience, for providing a stimulating and cheerful research environment in his laboratory, for letting me participate in the projects that produced results for papers, thus made me more aware of the state of the art in Computer Vision, especially in the area of 3D recognition. Also for his encouraging support and his way to always nd time for discussions, and last but not the least for the cooking recipes... Many thanks go also to my laboratory fellows, to Jo~ao Rodrigues, who invited me to participate in FCT and QREN projects, Jaime Carvalho Martins and Miguel Farrajota, for discussing scienti c and technical problems, but also almost all problems in the world. To all persons, that worked in, or visited the Vision Laboratory, especially those with whom I have worked with, almost on a daily basis. A special thanks to the Instituto Superior de Engenharia at UAlg and my colleagues at the Department of Electrical Engineering, for allowing me to suspend lectures in order to be present at conferences. To my family, my wife and my kids

Computational Topology Methods for Shape Modelling Applications

This thesis deals with computational topology, a recent branch of research that involves both mathematics and computer science, and tackles the problem of discretizing the Morse theory to functions defined on a triangle mesh. The application context of Morse theory in general, and Reeb graphs in particular, deals with the analysis of geometric shapes and the extraction of skeletal structures that synthetically represents shape, preserving the topological properties and the main morphological characteristics. Regarding Computer Graphics, shapes, that is a one-, two- or higher- dimensional connected, compact space having a visual appearance, are typically approximated by digital models. Since topology focuses on the qualitative properties of spaces, such as the connectedness and how many and what type of holes it has, topology is the best tool to describe the shape of a mathematical model at a high level of abstraction. Geometry, conversely, is mainly related to the quantitative characteristics of a shape. Thus, the combination of topology and geometry creates a new generation of tools that provide a computational description of the most representative features of the shape along with their relationship. Extracting qualitative information, that is the information related to semantic of the shape and its morphological structure, from discrete models is a central goal in shape modeling. In this thesis a conceptual model is proposed which represents a given surface based on topological coding that defines a sketch of the surface, discarding irrelevant details and classifying its topological type. The approach is based on Morse theory and Reeb graphs, which provide a very useful shape abstraction method for the analysis and structuring of the information contained in the geometry of the discrete shape model. To fully develop the method, both theoretical and computational aspects have been considered, related to the definition and the extension of the Reeb graph to the discrete domain. For the definition and automatic construction of the conceptual model, a new method has been developed that analyzes and characterizes a triangle mesh with respect to the behavior of a real and at least continuous function defined on the mesh. The proposed solution handles also degenerate critical points, such as non-isolated critical points. To do that, the surface model is characterized using a contour-based strategy, recognizing critical areas instead of critical points and coding the evolution of the contour levels in a graph-like structure, named Extended Reeb Graph, (ERG), which is a high-level abstract model suitable for representing and manipulating piece-wise linear surfaces. The descriptive power of the (ERG) has been also augmented with the introduction of geometric information together with the topological ones, and it has been also studied the relation between the extracted topological and morphological features with respect to the real characteristics of the surface, giving and evaluation of the dimension of the discarded details. Finally, the effectiveness of our description framework has been evaluated in several application contexts

A computational model of space-variant vision based on a self-organised artificial retina tessellation

Abstract available: p.i-iii

From Calibration to Large-Scale Structure from Motion with Light Fields

Classic pinhole cameras project the multi-dimensional information of the light flowing through a scene onto a single 2D snapshot. This projection limits the information that can be reconstructed from the 2D acquisition. Plenoptic (or light field) cameras, on the other hand, capture a 4D slice of the plenoptic function, termed the “light field”. These cameras provide both spatial and angular information on the light flowing through a scene; multiple views are captured in a single photographic exposure facilitating various applications. This thesis is concerned with the modelling of light field (or plenoptic) cameras and the development of structure from motion pipelines using such cameras. Specifically, we develop a geometric model for a multi-focus plenoptic camera, followed by a complete pipeline for the calibration of the suggested model. Given a calibrated light field camera, we then remap the captured light field to a grid of pinhole images. We use these images to obtain metric 3D reconstruction through a novel framework for structure from motion with light fields. Finally, we suggest a linear and efficient approach for absolute pose estimation for light fields

Sensing Highly Non-Rigid Objects with RGBD Sensors for Robotic Systems

The goal of this research is to enable a robotic system to manipulate clothing and other highly non-rigid objects using an RGBD sensor. The focus of this thesis is to define and test various algorithms / models that are used to solve parts of the laundry process (i.e. handling, classifying, sorting, unfolding, and folding). First, a system is presented for automatically extracting and classifying items in a pile of laundry. Using only visual sensors, the robot identifies and extracts items sequentially from the pile. When an item is removed and isolated, a model is captured of the shape and appearance of the object, which is then compared against a dataset of known items. The contributions of this part of the laundry process are a novel method for extracting articles of clothing from a pile of laundry, a novel method of classifying clothing using interactive perception, and a multi-layer approach termed L-M-H, more specifically L-C-S-H for clothing classification. This thesis describes two different approaches to classify clothing into categories. The first approach relies upon silhouettes, edges, and other low-level image measurements of the articles of clothing. Experiments from the first approach demonstrate the ability of the system to efficiently classify and label into one of six categories (pants, shorts, short-sleeve shirt, long-sleeve shirt, socks, or underwear). These results show that, on average, classification rates using robot interaction are 59% higher than those that do not use interaction. The second approach relies upon color, texture, shape, and edge information from 2D and 3D data within a local and global perspective. The multi-layer approach compartmentalizes the problem into a high (H) layer, multiple mid-level (characteristics(C), selection masks(S)) layers, and a low (L) layer. This approach produces \u27local\u27 solutions to solve the global classification problem. Experiments demonstrate the ability of the system to efficiently classify each article of clothing into one of seven categories (pants, shorts, shirts, socks, dresses, cloths, or jackets). The results presented in this paper show that, on average, the classification rates improve by +27.47% for three categories, +17.90% for four categories, and +10.35% for seven categories over the baseline system, using support vector machines. Second, an algorithm is presented for automatically unfolding a piece of clothing. A piece of cloth is pulled in different directions at various points of the cloth in order to flatten the cloth. The features of the cloth are extracted and calculated to determine a valid location and orientation in which to interact with it. The features include the peak region, corner locations, and continuity / discontinuity of the cloth. In this thesis, a two-stage algorithm is presented, introducing a novel solution to the unfolding / flattening problem using interactive perception. Simulations using 3D simulation software, and experiments with robot hardware demonstrate the ability of the algorithm to flatten pieces of laundry using different starting configurations. These results show that, at most, the algorithm flattens out a piece of cloth from 11.1% to 95.6% of the canonical configuration. Third, an energy minimization algorithm is presented that is designed to estimate the configuration of a deformable object. This approach utilizes an RGBD image to calculate feature correspondence (using SURF features), depth values, and boundary locations. Input from a Kinect sensor is used to segment the deformable surface from the background using an alpha-beta swap algorithm. Using this segmentation, the system creates an initial mesh model without prior information of the surface geometry, and it reinitializes the configuration of the mesh model after a loss of input data. This approach is able to handle in-plane rotation, out-of-plane rotation, and varying changes in translation and scale. Results display the proposed algorithm over a dataset consisting of seven shirts, two pairs of shorts, two posters, and a pair of pants. The current approach is compared using a simulated shirt model in order to calculate the mean square error of the distance from the vertices on the mesh model to the ground truth, provided by the simulation model

QUIS-CAMPI: Biometric Recognition in Surveillance Scenarios

The concerns about individuals security have justified the increasing number of surveillance cameras deployed both in private and public spaces. However, contrary to popular belief, these devices are in most cases used solely for recording, instead of feeding intelligent analysis processes capable of extracting information about the observed individuals. Thus, even though video surveillance has already proved to be essential for solving multiple crimes, obtaining relevant details about the subjects that took part in a crime depends on the manual inspection of recordings. As such, the current goal of the research community is the development of automated surveillance systems capable of monitoring and identifying subjects in surveillance scenarios. Accordingly, the main goal of this thesis is to improve the performance of biometric recognition algorithms in data acquired from surveillance scenarios. In particular, we aim at designing a visual surveillance system capable of acquiring biometric data at a distance (e.g., face, iris or gait) without requiring human intervention in the process, as well as devising biometric recognition methods robust to the degradation factors resulting from the unconstrained acquisition process. Regarding the first goal, the analysis of the data acquired by typical surveillance systems shows that large acquisition distances significantly decrease the resolution of biometric samples, and thus their discriminability is not sufficient for recognition purposes. In the literature, diverse works point out Pan Tilt Zoom (PTZ) cameras as the most practical way for acquiring high-resolution imagery at a distance, particularly when using a master-slave configuration. In the master-slave configuration, the video acquired by a typical surveillance camera is analyzed for obtaining regions of interest (e.g., car, person) and these regions are subsequently imaged at high-resolution by the PTZ camera. Several methods have already shown that this configuration can be used for acquiring biometric data at a distance. Nevertheless, these methods failed at providing effective solutions to the typical challenges of this strategy, restraining its use in surveillance scenarios. Accordingly, this thesis proposes two methods to support the development of a biometric data acquisition system based on the cooperation of a PTZ camera with a typical surveillance camera. The first proposal is a camera calibration method capable of accurately mapping the coordinates of the master camera to the pan/tilt angles of the PTZ camera. The second proposal is a camera scheduling method for determining - in real-time - the sequence of acquisitions that maximizes the number of different targets obtained, while minimizing the cumulative transition time. In order to achieve the first goal of this thesis, both methods were combined with state-of-the-art approaches of the human monitoring field to develop a fully automated surveillance capable of acquiring biometric data at a distance and without human cooperation, designated as QUIS-CAMPI system. The QUIS-CAMPI system is the basis for pursuing the second goal of this thesis. The analysis of the performance of the state-of-the-art biometric recognition approaches shows that these approaches attain almost ideal recognition rates in unconstrained data. However, this performance is incongruous with the recognition rates observed in surveillance scenarios. Taking into account the drawbacks of current biometric datasets, this thesis introduces a novel dataset comprising biometric samples (face images and gait videos) acquired by the QUIS-CAMPI system at a distance ranging from 5 to 40 meters and without human intervention in the acquisition process. This set allows to objectively assess the performance of state-of-the-art biometric recognition methods in data that truly encompass the covariates of surveillance scenarios. As such, this set was exploited for promoting the first international challenge on biometric recognition in the wild. This thesis describes the evaluation protocols adopted, along with the results obtained by the nine methods specially designed for this competition. In addition, the data acquired by the QUIS-CAMPI system were crucial for accomplishing the second goal of this thesis, i.e., the development of methods robust to the covariates of surveillance scenarios. The first proposal regards a method for detecting corrupted features in biometric signatures inferred by a redundancy analysis algorithm. The second proposal is a caricature-based face recognition approach capable of enhancing the recognition performance by automatically generating a caricature from a 2D photo. The experimental evaluation of these methods shows that both approaches contribute to improve the recognition performance in unconstrained data.A crescente preocupação com a segurança dos indivíduos tem justificado o crescimento do número de câmaras de vídeo-vigilância instaladas tanto em espaços privados como públicos. Contudo, ao contrário do que normalmente se pensa, estes dispositivos são, na maior parte dos casos, usados apenas para gravação, não estando ligados a nenhum tipo de software inteligente capaz de inferir em tempo real informações sobre os indivíduos observados. Assim, apesar de a vídeo-vigilância ter provado ser essencial na resolução de diversos crimes, o seu uso está ainda confinado à disponibilização de vídeos que têm que ser manualmente inspecionados para extrair informações relevantes dos sujeitos envolvidos no crime. Como tal, atualmente, o principal desafio da comunidade científica é o desenvolvimento de sistemas automatizados capazes de monitorizar e identificar indivíduos em ambientes de vídeo-vigilância. Esta tese tem como principal objetivo estender a aplicabilidade dos sistemas de reconhecimento biométrico aos ambientes de vídeo-vigilância. De forma mais especifica, pretende-se 1) conceber um sistema de vídeo-vigilância que consiga adquirir dados biométricos a longas distâncias (e.g., imagens da cara, íris, ou vídeos do tipo de passo) sem requerer a cooperação dos indivíduos no processo; e 2) desenvolver métodos de reconhecimento biométrico robustos aos fatores de degradação inerentes aos dados adquiridos por este tipo de sistemas. No que diz respeito ao primeiro objetivo, a análise aos dados adquiridos pelos sistemas típicos de vídeo-vigilância mostra que, devido à distância de captura, os traços biométricos amostrados não são suficientemente discriminativos para garantir taxas de reconhecimento aceitáveis. Na literatura, vários trabalhos advogam o uso de câmaras Pan Tilt Zoom (PTZ) para adquirir imagens de alta resolução à distância, principalmente o uso destes dispositivos no modo masterslave. Na configuração master-slave um módulo de análise inteligente seleciona zonas de interesse (e.g. carros, pessoas) a partir do vídeo adquirido por uma câmara de vídeo-vigilância e a câmara PTZ é orientada para adquirir em alta resolução as regiões de interesse. Diversos métodos já mostraram que esta configuração pode ser usada para adquirir dados biométricos à distância, ainda assim estes não foram capazes de solucionar alguns problemas relacionados com esta estratégia, impedindo assim o seu uso em ambientes de vídeo-vigilância. Deste modo, esta tese propõe dois métodos para permitir a aquisição de dados biométricos em ambientes de vídeo-vigilância usando uma câmara PTZ assistida por uma câmara típica de vídeo-vigilância. O primeiro é um método de calibração capaz de mapear de forma exata as coordenadas da câmara master para o ângulo da câmara PTZ (slave) sem o auxílio de outros dispositivos óticos. O segundo método determina a ordem pela qual um conjunto de sujeitos vai ser observado pela câmara PTZ. O método proposto consegue determinar em tempo-real a sequência de observações que maximiza o número de diferentes sujeitos observados e simultaneamente minimiza o tempo total de transição entre sujeitos. De modo a atingir o primeiro objetivo desta tese, os dois métodos propostos foram combinados com os avanços alcançados na área da monitorização de humanos para assim desenvolver o primeiro sistema de vídeo-vigilância completamente automatizado e capaz de adquirir dados biométricos a longas distâncias sem requerer a cooperação dos indivíduos no processo, designado por sistema QUIS-CAMPI. O sistema QUIS-CAMPI representa o ponto de partida para iniciar a investigação relacionada com o segundo objetivo desta tese. A análise do desempenho dos métodos de reconhecimento biométrico do estado-da-arte mostra que estes conseguem obter taxas de reconhecimento quase perfeitas em dados adquiridos sem restrições (e.g., taxas de reconhecimento maiores do que 99% no conjunto de dados LFW). Contudo, este desempenho não é corroborado pelos resultados observados em ambientes de vídeo-vigilância, o que sugere que os conjuntos de dados atuais não contêm verdadeiramente os fatores de degradação típicos dos ambientes de vídeo-vigilância. Tendo em conta as vulnerabilidades dos conjuntos de dados biométricos atuais, esta tese introduz um novo conjunto de dados biométricos (imagens da face e vídeos do tipo de passo) adquiridos pelo sistema QUIS-CAMPI a uma distância máxima de 40m e sem a cooperação dos sujeitos no processo de aquisição. Este conjunto permite avaliar de forma objetiva o desempenho dos métodos do estado-da-arte no reconhecimento de indivíduos em imagens/vídeos capturados num ambiente real de vídeo-vigilância. Como tal, este conjunto foi utilizado para promover a primeira competição de reconhecimento biométrico em ambientes não controlados. Esta tese descreve os protocolos de avaliação usados, assim como os resultados obtidos por 9 métodos especialmente desenhados para esta competição. Para além disso, os dados adquiridos pelo sistema QUIS-CAMPI foram essenciais para o desenvolvimento de dois métodos para aumentar a robustez aos fatores de degradação observados em ambientes de vídeo-vigilância. O primeiro é um método para detetar características corruptas em assinaturas biométricas através da análise da redundância entre subconjuntos de características. O segundo é um método de reconhecimento facial baseado em caricaturas automaticamente geradas a partir de uma única foto do sujeito. As experiências realizadas mostram que ambos os métodos conseguem reduzir as taxas de erro em dados adquiridos de forma não controlada

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Symmetry Detection in Geometric Models

PUNTIS (LO1506), SGS-2019-016Symmetry occurs very commonly in real world objects as well as in artificially created geometric models. The knowledge about symmetry of a given object can be very useful in many applications in computer graphics and geometry processing, such as compression, object alignment, symmetric editing or completion of partial objects. In order to use the symmetry of any object in any given application, it first needs to be found. In this work, we provide some background about symmetry in general and about different types of symmetry, mainly in 3D objects. Then we focus on the task of automatic symmetry detection in 3D objects and we also describe the link between symmetry detection and the problem of registration. Most importantly, we present our own contribution in these fields. First, we show a new method of evaluating consensus in RANSAC surface registration together with a thorough analysis of various distance metrics for rigid transformations that can be used in this new approach. Afterwards, we provide an analysis of different representations of the space of planes in context of symmetry plane detection. At last, we propose a new, robust, fast and flexible method for symmetry plane detection based on a novel differentiable symmetry measure