Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques

Among all animals, mosquitoes are responsible for the most deaths worldwide. Interestingly, not all types of mosquitoes spread diseases, but rather, a select few alone are competent enough to do so. In the case of any disease outbreak, an important first step is surveillance of vectors (i.e., those mosquitoes capable of spreading diseases). To do this today, public health workers lay several mosquito traps in the area of interest. Hundreds of mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have to identify only the vectors to gauge their density. This process today is manual, requires complex expertise/ training, and is based on visual inspection of each trapped specimen under a microscope. It is long, stressful and self-limiting. This paper presents an innovative solution to this problem. Our technique assumes the presence of an embedded camera (similar to those in smart-phones) that can take pictures of trapped mosquitoes. Our techniques proposed here will then process these images to automatically classify the genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer Learning yielded an overall accuracy of 80% in classifying mosquitoes when trained on 25,867 images of 250 trapped mosquito vector specimens captured via many smart-phone cameras. In particular, the accuracy of our model in classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are deadly vectors) is amongst the highest. We present important lessons learned and practical impact of our techniques towards the end of the paper

FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation

Liver transplantation often faces fairness challenges across subgroups defined by sensitive attributes like age group, gender, and race/ethnicity. Machine learning models for outcome prediction can introduce additional biases. To address these, we introduce Fairness through the Equitable Rate of Improvement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our experiments show that FERI maintains high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrates an ability to improve fairness without sacrificing accuracy. Specifically, for gender, FERI reduces the demographic parity disparity by 71.74%, and for the age group, it decreases the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advances fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Sensor fusion in distributed cortical circuits

The substantial motion of the nature is to balance, to survive, and to reach perfection. The evolution in biological systems is a key signature of this quintessence. Survival cannot be achieved without understanding the surrounding world. How can a fruit fly live without searching for food, and thereby with no form of perception that guides the behavior? The nervous system of fruit fly with hundred thousand of neurons can perform very complicated tasks that are beyond the power of an advanced supercomputer. Recently developed computing machines are made by billions of transistors and they are remarkably fast in precise calculations. But these machines are unable to perform a single task that an insect is able to do by means of thousands of neurons. The complexity of information processing and data compression in a single biological neuron and neural circuits are not comparable with that of developed today in transistors and integrated circuits. On the other hand, the style of information processing in neural systems is also very different from that of employed by microprocessors which is mostly centralized. Almost all cognitive functions are generated by a combined effort of multiple brain areas. In mammals, Cortical regions are organized hierarchically, and they are reciprocally interconnected, exchanging the information from multiple senses. This hierarchy in circuit level, also preserves the sensory world within different levels of complexity and within the scope of multiple modalities. The main behavioral advantage of that is to understand the real-world through multiple sensory systems, and thereby to provide a robust and coherent form of perception. When the quality of a sensory signal drops, the brain can alternatively employ other information pathways to handle cognitive tasks, or even to calibrate the error-prone sensory node. Mammalian brain also takes a good advantage of multimodal processing in learning and development; where one sensory system helps another sensory modality to develop. Multisensory integration is considered as one of the main factors that generates consciousness in human. Although, we still do not know where exactly the information is consolidated into a single percept, and what is the underpinning neural mechanism of this process? One straightforward hypothesis suggests that the uni-sensory signals are pooled in a ploy-sensory convergence zone, which creates a unified form of perception. But it is hard to believe that there is just one single dedicated region that realizes this functionality. Using a set of realistic neuro-computational principles, I have explored theoretically how multisensory integration can be performed within a distributed hierarchical circuit. I argued that the interaction of cortical populations can be interpreted as a specific form of relation satisfaction in which the information preserved in one neural ensemble must agree with incoming signals from connected populations according to a relation function. This relation function can be seen as a coherency function which is implicitly learnt through synaptic strength. Apart from the fact that the real world is composed of multisensory attributes, the sensory signals are subject to uncertainty. This requires a cortical mechanism to incorporate the statistical parameters of the sensory world in neural circuits and to deal with the issue of inaccuracy in perception. I argued in this thesis how the intrinsic stochasticity of neural activity enables a systematic mechanism to encode probabilistic quantities within neural circuits, e.g. reliability, prior probability. The systematic benefit of neural stochasticity is well paraphrased by the problem of Duns Scotus paradox: imagine a donkey with a deterministic brain that is exposed to two identical food rewards. This may make the animal suffer and die starving because of indecision. In this thesis, I have introduced an optimal encoding framework that can describe the probability function of a Gaussian-like random variable in a pool of Poisson neurons. Thereafter a distributed neural model is proposed that can optimally combine conditional probabilities over sensory signals, in order to compute Bayesian Multisensory Causal Inference. This process is known as a complex multisensory function in the cortex. Recently it is found that this process is performed within a distributed hierarchy in sensory cortex. Our work is amongst the first successful attempts that put a mechanistic spotlight on understanding the underlying neural mechanism of Multisensory Causal Perception in the brain, and in general the theory of decentralized multisensory integration in sensory cortex. Engineering information processing concepts in the brain and developing new computing technologies have been recently growing. Neuromorphic Engineering is a new branch that undertakes this mission. In a dedicated part of this thesis, I have proposed a Neuromorphic algorithm for event-based stereoscopic fusion. This algorithm is anchored in the idea of cooperative computing that dictates the defined epipolar and temporal constraints of the stereoscopic setup, to the neural dynamics. The performance of this algorithm is tested using a pair of silicon retinas

Enhancing endoscopic navigation and polyp detection using artificial intelligence

Colorectal cancer (CRC) is one most common and deadly forms of cancer. It has a very high mortality rate if the disease advances to late stages however early diagnosis and treatment can be curative is hence essential to enhancing disease management. Colonoscopy is considered the gold standard for CRC screening and early therapeutic treatment. The effectiveness of colonoscopy is highly dependent on the operator’s skill, as a high level of hand-eye coordination is required to control the endoscope and fully examine the colon wall. Because of this, detection rates can vary between different gastroenterologists and technology have been proposed as solutions to assist disease detection and standardise detection rates. This thesis focuses on developing artificial intelligence algorithms to assist gastroenterologists during colonoscopy with the potential to ensure a baseline standard of quality in CRC screening. To achieve such assistance, the technical contributions develop deep learning methods and architectures for automated endoscopic image analysis to address both the detection of lesions in the endoscopic image and the 3D mapping of the endoluminal environment. The proposed detection models can run in real-time and assist visualization of different polyp types. Meanwhile the 3D reconstruction and mapping models developed are the basis for ensuring that the entire colon has been examined appropriately and to support quantitative measurement of polyp sizes using the image during a procedure. Results and validation studies presented within the thesis demonstrate how the developed algorithms perform on both general scenes and on clinical data. The feasibility of clinical translation is demonstrated for all of the models on endoscopic data from human participants during CRC screening examinations

Reproducibility and sensitivity of brain network backbones: a demonstration in Small Vessel Disease

Mestrado integrado em Engenharia Biomédica e Biofísica (Sinais e Imagens Médicas) Universidade de Lisboa; Faculdade de Ciências, 2020Whole-brain networks have been used to study the connectivity paths within the brain, constructed using information from diffusion magnetic resonance imaging (dMRI) data and white matter fiber tractography (FT). These techniques can detect alterations in the white matter integrity and changes in axonal connections, whose alterations can be due to the presence of small vessel disease (SVD). However, there is a lack of consensus in network reconstruction methods and therefore no gold-standard model of the human brain network. Moreover, dMRI data are affected by methodological issues such as scan noise, presence of false-positive and false-negative connections. Consequently, the reproducibility and the reliability of these networks is normally very low. A potential solution to deal with the low reproducibility of brain networks is to analyze only its backbone structure. This backbone is assumed to represent the building blocks of structural brain networks and thus composed by a set of strong connections and voided of spurious connections. Such backbone should be reproducible in scan-rescan scenarios and relatively consistent between healthy subjects, while still being sensitive to disease-related changes. Several types of backbones have been proposed, constructed using white matter tractography, with dMRI data. However, no study has directly compared these backbones in terms of reproducibility, consistency, or sensitivity to disease effects in a patient population. In this project, we examined: (1) whether the proposed backbones can be applied to clinical datasets by testing if they are reproducible over two time-points and consistent between groups; (2) if they are sensitive to disease effects both in a cross-sectional and longitudinal analysis. We evaluated our research questions on a longitudinal cohort of patients with cerebral SVD and age matched controls, as well as a validation dataset of healthy young adults. Our cohort contained 87 elderly memory clinic patients with SVD recruited via the UMC Utrecht, scanned twice with an inter-scan time between baseline and follow-up of 27 ± 4 months. We also included baseline scans of 45 healthy elderly, matched in age, sex and education level, to be used as controls. Data from 44 healthy young adults was used as validation data. For each subject, we reconstructed brain structural networks from the diffusion MRI data. Subsequently, we computed 4 types of network backbone, previously described in literature: the Minimal Spanning Tree (MST), the Disparity Core, the K-Core, and Hub-Core. We compared these backbones and tested their reproducibility within subjects, and their consistency across subjects and across groups. Moreover, we performed a cross-sectional analysis between controls and patients at baseline, to evaluate if these backbones can detect disease effects and a longitudinal analysis with patient data over time, to test if they can detect disease progression. Regarding our first objective, our results show that overall MST is the backbone that shows the best reproducibility between repeated scans, as well as the highest consistency among subjects, for all of the three brain templates that we used. Secondly, the MST was also sensitive to network alterations both on a cross-sectional analysis (patients vs. controls) and on a longitudinal analysis (baseline vs. follow-up). We therefore conclude that, the use of these network backbones, as an alternative of the whole-brain network analysis, can successfully be applied to clinical datasets as a novel and reliable way to detect disease effects and evaluate disease progression.A demência vascular cerebral (SVD) é a segunda principal causa de demência, depois da doença de Alzheimer. Este tipo de demência está relacionado com patologias vasculares cerebrais, assim como com perda de funcionalidades cognitivas. Vários estudos explicam que a degradação da atividade cognitiva característica desta doença pode dever-se à diminuição da integridade da substância branca e a alterações nas conexões axonais. O estudo da conectividade cerebral tem sido uma forte aposta no estudo das causas e da forma como a demência vascular cerebral evolui. A construção de mapas neuronais é uma das práticas que mais tem sido usada para estudar e entender os mecanismos principais da conectividade cerebral: representar o cérebro como um conjunto de regiões e as ligações entre elas. Para isso, utiliza-se informação proveniente de imagens de ressonância magnética por difusão (dMRI), especificamente de imagens por tensor de difusão (DTI), capazes de medir a magnitude de difusão das moléculas de água no cérebro in vivo, através de gradientes aplicados em pelo menos seis direções no espaço. Desta forma, é possível estimar a direção principal do movimento das moléculas de água que compõem as microfibras da substância branca cerebral, e reconstruir os percursos de neurónios que conectam as várias regiões do cérebro. Este processo é chamado de tractografia de fibras (FT), que proporciona um modelo a três dimensões da arquitetura tecidular cerebral, permitindo a visualização e o estudo da conectividade e da continuidade dos percursos neuronais. Assim, é possível obter informação quantitativa acerca do sistema nervoso in vivo e contruir mapas de conectividade cerebral. No entanto, existe falta de consenso sobre as regras de reconstrução destes mapas neuronais, fazendo com que não haja um modelo-base para o estudo dos mesmos. Além disto, os dados provenientes das imagens de dMRI são facilmente afetados e podem diferir da realidade. Alguns exemplos mais comuns são a presença de ruído e existência tanto de conexões falsas como a ausência de conexões que deviam estar presentes, chamadas respetivamente de falsos-positivos e falsos-negativos. Consequentemente, os modelos de conectividade não podem ser comparados entre diferentes aparelhos de ressonância, nem mesmo entre diferentes momentos temporais, por terem uma baixa reprodutibilidade, tornando estes métodos poucos fiáveis. As soluções propostas para lidar com esta falta de consenso quanto à reconstrução de mapas ou redes neuronais e a presença de conexões falsas podem ser agrupadas em duas categorias: normalização e redução da rede neuronal através da aplicação de um limiar (threshold, em inglês). Contudo, os processos de normalização para remover certas tendências erradas destas redes não eram suficientes e, por vezes, introduziam outras dependências. Quanto à aplicação de limiares, mesmo que alguns estudos mostrem que a sua utilização no mapa neuronal do cérebro todo pode efetivamente eliminar alguns efeitos, a própria escolha de um limiar pode conduzir a modificações nas redes neuronais através de eliminação de certas comunicações fundamentais. Como uma extensão da redução destas redes neuronais com o objetivo de lidar com a sua baixa reprodutibilidade, vários estudos têm proposto uma nova abordagem: analisar apenas uma espécie de esqueleto das mesmas. O objetivo deste “esqueleto-neuronal” é o de representar as ligações mais importantes e estruturais e estar isento de falsas conexões. Idealmente, este “esqueleto-neuronal” seria reprodutível entre diferentes dispositivos e consistente entre indivíduos saudáveis, enquanto se manteria fiel às diferenças causadas pela presença de doenças. Assim sendo, o estudo da extração de um esqueleto-neuronal, visa encontrar estruturas fundamentais que evitem a perda de propriedades topológicas. Por exemplo, considerando pacientes com SVD, estes esqueletos-neuronais devem fornecer uma melhor compreensão das alterações da conectividade cerebral ao longo do tempo, permitindo uma comparação sólida entre diferentes pontos no tempo e a identificação de alterações que sejam consequência inegável de doença. Alguns tipos destas redes neuronais foram já propostos em diversas publicações científicas, que podem ser construídos utilizando FT de substância branca com informação proveniente de dMRI. Neste estudo, utilizamos o Minimum Spanning Tree (MST), o K-Core, o Disparity Core e o Hub-Core, que são redes-esqueleto já existentes na literatura. A eficácia tanto do uso do MST como do K-Core já foram comprovadas tanto a nível de deteção de alterações da conectividade cerebral, como na medida em que conseguem manter as conexões mais importantes do esqueleto cerebral, eliminando conexões que podem ser consideradas duvidosas. No entanto, até agora, nenhum estudo se focou na comparação dos diferentes esqueletos-neuronais existentes quanto à sua reproducibilidade, consistência ou sensibilidade aos efeitos de doença ao longo do tempo. Neste estudo, utilizamos os quatro modelos-esqueletos mencionados anteriormente, avaliando: (1) se estes esqueletos-neuronais podem ser efetivamente aplicados a dados clínicos, testando a sua reproducibilidade entre dois pontos de tempos distintos e a sua consistência entre grupos de controlos saudáveis; (2) se são sensíveis a efeitos causados por doença, tanto entre controlos e pacientes, como na evolução de alterações de conectividade em pacientes ao longo do tempo. Os dados longitudinais utilizados provêm de imagens ponderadas em T1 de 87 pacientes idosos com SVD, assim como de um grupo controlo de 45 idosos saudáveis coincidentes em idade com estes pacientes, e de um grupo de validação constituído por 44 jovens saudáveis. Para cada um dos objetivos, testamos os 4 tipos de esqueletos-neuronais, baseados primeiramente num modelo que divide o córtex cerebral em 90 regiões de interesse (ROIs) e posteriormente em modelos de 200 e 250 regiões. No pós-processamento, foram construídas e comparadas matrizes de conectividade que representam as ligações entre as várias regiões em que dividimos o córtex. Com estas matrizes foi possível calcular valores de conectividade como a força nodal (NS) e a eficiência global (GE). Também foram comparadas matrizes que diziam respeito a parâmetros específicos de DTI como a anisotropia fracionada (FA) e a difusividade média (MD). A análise estatística foi feita utilizando testes paramétricos t-test e ANOVA. Os resultados indicam que, no geral, estas redes podem ser utilizadas como forma de analisar e estudar mapas de conectividade cerebral de forma mais precisa, pois são reprodutíveis entre controlos saudáveis em tempos diferentes, e conseguem detetar as diferenças de conectividade devidas a doença. Além disso, representam as ligações mais importantes da rede de conectividade neuronal, criando uma base para comparações fiáveis. A maior parte dos esqueletos-neuronais mostraram ser consistentes dentro de cada grupo de estudo, e apresentaram diferenças de conectividade entre controlos e pacientes. Neste caso, comparando sujeitos saudáveis com pacientes, os valores das componentes de FA e de MD destes esqueletos neuronais, e as suas alterações, vão de encontro com a literatura sobre a evolução do estado das ligações neuronais no desenvolvimento de demência. Quanto à análise longitudinal dos pacientes, concluímos que a NS representa uma medida mais fiável de análise das alterações ao longo do tempo da doença do que a GE. Finalmente, e ainda que algumas destes esqueletos-neuronais tenham mostrado melhor desempenho do que outros em diferentes abordagens, concluímos que o MST é a rede-esqueleto que dispõe dos melhores resultados utilizando o modelo de 90 e 200 ROIs, do cérebro todo, assim como usando o modelo de 250 ROIs aplicado só ao hemisfério esquerdo. Em suma, conclui-se que a utilização destes tipos de redes-esqueleto pode vir a tornar-se uma melhor alternativa em relação à utilização das redes neuronais originais do cérebro completo, visto que podem ser eficazmente aplicadas à análise de dados clínicos como forma fiável de detetar presença e evolução de doenças

Clearing the Clouds: Extracting 3D information from amongst the noise

Advancements permitting the rapid extraction of 3D point clouds from a variety of imaging modalities across the global landscape have provided a vast collection of high fidelity digital surface models. This has created a situation with unprecedented overabundance of 3D observations which greatly outstrips our current capacity to manage and infer actionable information. While years of research have removed some of the manual analysis burden for many tasks, human analysis is still a cornerstone of 3D scene exploitation. This is especially true for complex tasks which necessitate comprehension of scale, texture and contextual learning. In order to ameliorate the interpretation burden and enable scientific discovery from this volume of data, new processing paradigms are necessary to keep pace. With this context, this dissertation advances fundamental and applied research in 3D point cloud data pre-processing and deep learning from a variety of platforms. We show that the representation of 3D point data is often not ideal and sacrifices fidelity, context or scalability. First ground scanning terrestrial LIght Detection And Ranging (LiDAR) models are shown to have an inherent statistical bias, and present a state of the art method for correcting this, while preserving data fidelity and maintaining semantic structure. This technique is assessed in the dense canopy of Micronesia, with our technique being the best at retaining high levels of detail under extreme down-sampling (\u3c 1%). Airborne systems are then explored with a method which is presented to pre-process data to preserve a global contrast and semantic content in deep learners. This approach is validated with a building footprint detection task from airborne imagery captured in Eastern TN from the 3D Elevation Program (3DEP), our approach was found to achieve significant accuracy improvements over traditional techniques. Finally, topography data spanning the globe is used to assess past and previous global land cover change. Utilizing Shuttle Radar Topography Mission (SRTM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, paired with the airborne preprocessing technique described previously, a model for predicting land-cover change from topography observations is described. The culmination of these efforts have the potential to enhance the capabilities of automated 3D geospatial processing, substantially lightening the burden of analysts, with implications improving our responses to global security, disaster response, climate change, structural design and extraplanetary exploration

Perception of Unstructured Environments for Autonomous Off-Road Vehicles

Autonome Fahrzeuge benötigen die Fähigkeit zur Perzeption als eine notwendige Voraussetzung für eine kontrollierbare und sichere Interaktion, um ihre Umgebung wahrzunehmen und zu verstehen. Perzeption für strukturierte Innen- und Außenumgebungen deckt wirtschaftlich lukrative Bereiche, wie den autonomen Personentransport oder die Industrierobotik ab, während die Perzeption unstrukturierter Umgebungen im Forschungsfeld der Umgebungswahrnehmung stark unterrepräsentiert ist. Die analysierten unstrukturierten Umgebungen stellen eine besondere Herausforderung dar, da die vorhandenen, natürlichen und gewachsenen Geometrien meist keine homogene Struktur aufweisen und ähnliche Texturen sowie schwer zu trennende Objekte dominieren. Dies erschwert die Erfassung dieser Umgebungen und deren Interpretation, sodass Perzeptionsmethoden speziell für diesen Anwendungsbereich konzipiert und optimiert werden müssen. In dieser Dissertation werden neuartige und optimierte Perzeptionsmethoden für unstrukturierte Umgebungen vorgeschlagen und in einer ganzheitlichen, dreistufigen Pipeline für autonome Geländefahrzeuge kombiniert: Low-Level-, Mid-Level- und High-Level-Perzeption. Die vorgeschlagenen klassischen Methoden und maschinellen Lernmethoden (ML) zur Perzeption bzw.~Wahrnehmung ergänzen sich gegenseitig. Darüber hinaus ermöglicht die Kombination von Perzeptions- und Validierungsmethoden für jede Ebene eine zuverlässige Wahrnehmung der möglicherweise unbekannten Umgebung, wobei lose und eng gekoppelte Validierungsmethoden kombiniert werden, um eine ausreichende, aber flexible Bewertung der vorgeschlagenen Perzeptionsmethoden zu gewährleisten. Alle Methoden wurden als einzelne Module innerhalb der in dieser Arbeit vorgeschlagenen Perzeptions- und Validierungspipeline entwickelt, und ihre flexible Kombination ermöglicht verschiedene Pipelinedesigns für eine Vielzahl von Geländefahrzeugen und Anwendungsfällen je nach Bedarf. Low-Level-Perzeption gewährleistet eine eng gekoppelte Konfidenzbewertung für rohe 2D- und 3D-Sensordaten, um Sensorausfälle zu erkennen und eine ausreichende Genauigkeit der Sensordaten zu gewährleisten. Darüber hinaus werden neuartige Kalibrierungs- und Registrierungsansätze für Multisensorsysteme in der Perzeption vorgestellt, welche lediglich die Struktur der Umgebung nutzen, um die erfassten Sensordaten zu registrieren: ein halbautomatischer Registrierungsansatz zur Registrierung mehrerer 3D~Light Detection and Ranging (LiDAR) Sensoren und ein vertrauensbasiertes Framework, welches verschiedene Registrierungsmethoden kombiniert und die Registrierung verschiedener Sensoren mit unterschiedlichen Messprinzipien ermöglicht. Dabei validiert die Kombination mehrerer Registrierungsmethoden die Registrierungsergebnisse in einer eng gekoppelten Weise. Mid-Level-Perzeption ermöglicht die 3D-Rekonstruktion unstrukturierter Umgebungen mit zwei Verfahren zur Schätzung der Disparität von Stereobildern: ein klassisches, korrelationsbasiertes Verfahren für Hyperspektralbilder, welches eine begrenzte Menge an Test- und Validierungsdaten erfordert, und ein zweites Verfahren, welches die Disparität aus Graustufenbildern mit neuronalen Faltungsnetzen (CNNs) schätzt. Neuartige Disparitätsfehlermetriken und eine Evaluierungs-Toolbox für die 3D-Rekonstruktion von Stereobildern ergänzen die vorgeschlagenen Methoden zur Disparitätsschätzung aus Stereobildern und ermöglichen deren lose gekoppelte Validierung. High-Level-Perzeption konzentriert sich auf die Interpretation von einzelnen 3D-Punktwolken zur Befahrbarkeitsanalyse, Objekterkennung und Hindernisvermeidung. Eine Domänentransferanalyse für State-of-the-art-Methoden zur semantischen 3D-Segmentierung liefert Empfehlungen für eine möglichst exakte Segmentierung in neuen Zieldomänen ohne eine Generierung neuer Trainingsdaten. Der vorgestellte Trainingsansatz für 3D-Segmentierungsverfahren mit CNNs kann die benötigte Menge an Trainingsdaten weiter reduzieren. Methoden zur Erklärbarkeit künstlicher Intelligenz vor und nach der Modellierung ermöglichen eine lose gekoppelte Validierung der vorgeschlagenen High-Level-Methoden mit Datensatzbewertung und modellunabhängigen Erklärungen für CNN-Vorhersagen. Altlastensanierung und Militärlogistik sind die beiden Hauptanwendungsfälle in unstrukturierten Umgebungen, welche in dieser Arbeit behandelt werden. Diese Anwendungsszenarien zeigen auch, wie die Lücke zwischen der Entwicklung einzelner Methoden und ihrer Integration in die Verarbeitungskette für autonome Geländefahrzeuge mit Lokalisierung, Kartierung, Planung und Steuerung geschlossen werden kann. Zusammenfassend lässt sich sagen, dass die vorgeschlagene Pipeline flexible Perzeptionslösungen für autonome Geländefahrzeuge bietet und die begleitende Validierung eine exakte und vertrauenswürdige Perzeption unstrukturierter Umgebungen gewährleistet

A Machine Learning Approach to Credit Allocation

This dissertation seeks to understand the shortcomings of contemporaneous credit allocation, with a specific focus on exploring how an improved statistical technology impacts the credit access of societally important groups. First, this dissertation investigates a variety of limitations of conventional credit scoring models, specifically their tendency to misclassify borrowers by default risk, especially for relatively risky, young, and low income borrowers. Second, this dissertation shows that an improved statistical technology need not to lead to worse outcomes for disadvantaged groups. In fact, the credit access for borrowers belonging to such groups can be improved, while providing more accurate credit risk assessment. Last, this dissertation documents modern-day disparities in debt collection judgments across white and black neighborhoods. Taken together, this dissertation provides valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders and across societally important groups, as well as macroprudential regulation