Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-06-30T17:22:52Z No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) / Made available in DSpace on 2017-06-30T17:22:52Z (GMT). No. of bitstreams: 1 DIS_ANIBAL_SOLON_HEINSFELD_COMPLETO.pdf: 12807619 bytes, checksum: d11b60094a8bde0d839a6f7a23bbb56c (MD5) Previous issue date: 2016-03-28 / O Espectro Autista (EA) compreende uma s?rie de desordens no desenvolvimento neurol?gico, caracterizado por defici?ncias sociais e dificuldades de comunica??o, comportamentos repetitivos e atrasos cognitivos. Atualmente, o diagn?stico do EA ? amplamente baseado em medi??es comportamentais, que pode ser demorado, e depende da coopera??o do paciente e da experi?ncia do examinador. Para mitigar esta limita??o, investigamos padr?es neurais que ajudem no diagn?stico de desordens do EA. Nesta disserta??o, usamos t?cnicas de deep learning, a fim de extrair caracter?sticas robustas de neuroimagens de pacientes com autismo. Neuroimagens cont?m cerca de 300.000 pontos espaciais, com aproximadamente 200 medi??es cada. As t?cnicas de deep learning s?o ?teis para extrair caracter?sticas relevantes que diferenciam autistas de n?o-autistas. Ao utilizar denoising autoencoders, uma t?cnica de deep learning espec?fica que visa reduzir a dimensionalidade dos dados, n?s superamos o estado da arte, atingindo 69% de acur?cia, comparado com o melhor resultado encontrado na literatura, com 60% de acur?cia. / Autism Spectrum Disorders (ASD) comprise a range of neurodevelopmental disorders, characterized by social deficits and communication difficulties, repetitive behaviors, and cognitive delays. The diagnosis of ASD is largely based on behavioral measurements, which can be timeconsuming and relies on the patient cooperation and examiner expertise. In order to address this limitation, we aim to investigate neural patterns to help in the diagnosis of ASD. In this dissertation, we use deep learning techniques to extract robust characteristics from neuroimages of autistic subject brain function. Since neuroimage contains about 300,000 spatial points, with approximately 200 temporal measurements each, deep learning techniques are useful in order to extract important features to discriminate ASD subjects from non-ASD. By using denoising autoencoders, a specific deep learning technique that aims to reduce data dimensionality, we surpass the state-of-the-art by achieving 69% of accuracy, compared to 60% using the same dataset.

This work presents my research about the use of Convolutional Neural Network (CNNs) for transfer learning through its application for colonic polyp classification and iris super-resolution. Traditionally, machine learning methods use the same feature space and the same distribution for training and testing the tools. Several problems in this approach can emerge as, for example, when the number of samples for training (especially in a supervised training) is limited. In the medical field, this problem is recurrent mainly because obtaining a database large enough with appropriate annotations for training is highly costly and may become impractical. Another problem relates to the distribution of textural features in a image database which may be too large such as the texture patterns of the human iris. In this case a single and specific training database might not get enough generalization to be applied to the entire domain. In this work we explore the use of texture transfer learning to surpass these problems for two applications: colonic polyp classification and iris super-resolution. The leading cause of deaths related to intestinal tract is the development of cancer cells (polyps) in its many parts. An early detection (when the cancer is still at an early stage) can reduce the risk of mortality among these patients. More specifically, colonic polyps (benign tumors or growths which arise on the inner colon surface) have a high occurrence and are known to be precursors of colon cancer development. Several studies have shown that automatic detection and classification of image regions which may contain polyps within the colon can be used to assist specialists in order to decrease the polyp miss rate. However, the classification can be a difficult task due to several factors such as the lack or excess of illumination, the blurring due to movement or water injection and the different appearances of polyps. Also, to find a robust and a global feature extractor that summarizes and represents all these pit-patterns structures in a single vector is very difficult and Deep Learning can be a good alternative to surpass these problems. One of the goals of this work is show the effectiveness of CNNs trained from scratch for colonic polyp classification besides the capability of knowledge transfer between natural images and medical images using off-the-shelf pretrained CNNs for colonic polyp classification. In this case, the CNN will project the target database samples into a vector space where the classes are more likely to be separable. The second part of this work dedicates to the transfer learning for iris super-resolution. The main goal of Super-Resolution (SR) is to produce, from one or more images, an image with a higher resolution (with more pixels) at the same time that produces a more detailed and realistic image being faithful to the low resolution image(s). Currently, most iris recognition systems require the user to present their iris for the sensor at a close distance. However, at present, there is a constant pressure to make that relaxed conditions of acquisitions in such systems could be allowed. In this work we show that the use of deep learning and transfer learning for single image super resolution applied to iris recognition can be an alternative for Iris Recognition of low resolution images. For this purpose, we explore if the nature of the images as well as if the pattern from the iris can influence the CNN transfer learning and, consequently, the results in the recognition process. / Diese Arbeit pr¨asentiert meine Forschung hinsichtlich der Verwendung von ”Transfer-Learning” (TL) in Kombination mit Convolutional Neural Networks (CNNs), um dadurch die Klassifikation von Dickdarmpolypen und die Qualit¨at von Iris Bildern (”Iris-Super-Resolution”) zu verbessern. Herk¨ommlicherweise verwenden Verfahren des maschinellen Lernens den gleichen Merkmalsraum und die gleiche Verteilung zum Trainieren und Testen der abgewendeten Methoden. Mehrere Probleme k¨onnen bei diesem Ansatz jedoch auftreten. Zum Beispiel ist es m¨ oglich, dass die Anzahl der zu trainierenden Daten (insbesondere in einem ”supervised training” Szenario) begrenzt ist. Im Speziellen im medizinischen Anwendungsfall ist man regelm¨aßig mit dem angesprochenen Problem konfrontiert, da die Zusammenstellung einer Datenbank, welche ¨ uber eine geeignete Anzahl an verwendbaren Daten verf ¨ ugt, entweder sehr kostspielig ist und/oder sich als ¨ uber die Maßen zeitaufw¨andig herausstellt. Ein anderes Problem betrifft die Verteilung von Strukturmerkmalen in einer Bilddatenbank, die zu groß sein kann, wie es im Fall der Verwendung von Texturmustern der menschlichen Iris auftritt. Dies kann zu dem Umstand f ¨ uhren, dass eine einzelne und sehr spezifische Trainingsdatenbank m¨oglicherweise nicht ausreichend verallgemeinert wird, um sie auf die gesamte betrachtete Dom¨ane anzuwenden. In dieser Arbeit wird die Verwendung von TL auf diverse Texturen untersucht, um die zuvor angesprochenen Probleme f ¨ ur zwei Anwendungen zu ¨ uberwinden: in der Klassifikation von Dickdarmpolypen und in Iris Super-Resolution. Die Hauptursache f ¨ ur Todesf¨alle im Zusammenhang mit dem Darmtrakt ist die Entwicklung von Krebszellen (Polypen) in vielen unterschiedlichen Auspr¨agungen. Eine Fr ¨uherkennung kann das Mortalit¨atsrisiko bei Patienten verringern, wenn sich der Krebs noch in einem fr ¨uhen Stadium befindet. Genauer gesagt, Dickdarmpolypen (gutartige Tumore oder Wucherungen, die an der inneren Dickdarmoberfl¨ache entstehen) haben ein hohes Vorkommen und sind bekanntermaßen Vorl¨aufer von Darmkrebsentwicklung. Mehrere Studien haben gezeigt, dass die automatische Erkennung und Klassifizierung von Bildregionen, die Polypen innerhalb des Dickdarms m¨oglicherweise enthalten, verwendet werden k¨onnen, um Spezialisten zu helfen, die Fehlerrate bei Polypen zu verringern. Die Klassifizierung kann sich jedoch aufgrund mehrerer Faktoren als eine schwierige Aufgabe herausstellen. ZumBeispiel kann das Fehlen oder ein U¨ bermaß an Beleuchtung zu starken Problemen hinsichtlich der Kontrastinformation der Bilder f ¨ uhren, wohingegen Unsch¨arfe aufgrund von Bewegung/Wassereinspritzung die Qualit¨at des Bildmaterials ebenfalls verschlechtert. Daten, welche ein unterschiedlich starkes Auftreten von Polypen repr¨asentieren, bieten auch dieM¨oglichkeit zu einer Reduktion der Klassifizierungsgenauigkeit. Weiters ist es sehr schwierig, einen robusten und vor allem globalen Feature-Extraktor zu finden, der all die notwendigen Pit-Pattern-Strukturen in einem einzigen Vektor zusammenfasst und darstellt. Um mit diesen Problemen ad¨aquat umzugehen, kann die Anwendung von CNNs eine gute Alternative bieten. Eines der Ziele dieser Arbeit ist es, die Wirksamkeit von CNNs, die von Grund auf f ¨ ur die Klassifikation von Dickdarmpolypen konstruiert wurden, zu zeigen. Des Weiteren soll die Anwendung von TL unter der Verwendung vorgefertigter CNNs f ¨ ur die Klassifikation von Dickdarmpolypen untersucht werden. Hierbei wird zus¨atzliche Information von nichtmedizinischen Bildern hinzugezogen und mit den verwendeten medizinischen Daten verbunden: Information wird also transferiert - TL entsteht. Auch in diesem Fall projiziert das CNN iii die Zieldatenbank (die Polypenbilder) in einen vorher trainierten Vektorraum, in dem die zu separierenden Klassen dann eher trennbar sind, daWissen aus den nicht-medizinischen Bildern einfließt. Der zweite Teil dieser Arbeit widmet sich dem TL hinsichtlich der Verbesserung der Bildqualit¨at von Iris Bilder - ”Iris- Super-Resolution”. Das Hauptziel von Super-Resolution (SR) ist es, aus einem oder mehreren Bildern gleichzeitig ein Bild mit einer h¨oheren Aufl¨osung (mit mehr Pixeln) zu erzeugen, welches dadurch zu einem detaillierteren und somit realistischeren Bild wird, wobei der visuelle Bildinhalt unver¨andert bleibt. Gegenw¨artig fordern die meisten Iris- Erkennungssysteme, dass der Benutzer seine Iris f ¨ ur den Sensor in geringer Entfernung pr¨asentiert. Jedoch ist es ein Anliegen der Industrie die bisher notwendigen Bedingungen - kurzer Abstand zwischen Sensor und Iris, sowie Verwendung von sehr teuren hochqualitativen Sensoren - zu ver¨andern. Diese Ver¨anderung betrifft einerseits die Verwendung von billigeren Sensoren und andererseits die Vergr¨oßerung des Abstandes zwischen Iris und Sensor. Beide Anpassungen f ¨ uhren zu Reduktion der Bildqualit¨at, was sich direkt auf die Erkennungsgenauigkeit der aktuell verwendeten Iris- erkennungssysteme auswirkt. In dieser Arbeit zeigen wir, dass die Verwendung von CNNs und TL f ¨ ur die ”Single Image Super-Resolution”, die bei der Iriserkennung angewendet wird, eine Alternative f ¨ ur die Iriserkennung von Bildern mit niedriger Aufl¨osung sein kann. Zu diesem Zweck untersuchen wir, ob die Art der Bilder sowie das Muster der Iris das CNN-TL beeinflusst und folglich die Ergebnisse im Erkennungsprozess ver¨andern kann.

The contact patterns among individuals can significantly affect the progress of an infectious outbreak within a population. Gathering data about these interaction and mixing patterns is essential to assess computational modeling of infectious diseases. Various self-report approaches have been designed in different studies to collect data about contact rates and patterns. Recent advances in sensing technology provide researchers with a bilateral automated data collection devices to facilitate contact gathering overcoming the disadvantages of previous approaches. In this study, a novel unilateral wearable sensing architecture has been proposed that overcome the limitations of the bi-lateral sensing. Our unilateral wearable sensing system gather contact data using hybrid sensor arrays embedded in wearable shirt. A smartphone application has been used to transfer the collected sensors data to the cloud and apply deep learning model to estimate the number of human contacts and the results are stored in the cloud database. The deep learning model has been developed on the hand labelled data over multiple experiments. This model has been tested and evaluated, and these results were reported in the study. Sensitivity analysis has been performed to choose the most suitable image resolution and format for the model to estimate contacts and to analyze the model's consumption of computer resources.

Cardiovascular diseases are the largest mortality factor globally, and early diagnosis is essential for a proper medical response. Cardiac computed tomography can be used to acquire images for their diagnosis, but without radiation dose reduction the radiation emitted to the patient becomes a significant risk factor. By reducing the dose, the image quality is often compromised, and determining a diagnosis becomes difficult. This project proposes image quality enhancement with deep learning. A cycle-consistent generative adversarial neural network was fed low- and high-quality images with the purpose to learn to translate between them. By using a cycle-consistency cost it was possible to train the network without paired data. With this method, a low-quality image acquired from a computed tomography scan with dose reduction could be enhanced in post processing. The results were mixed but showed an increase of ventricular contrast and artifact mitigation. The technique comes with several problems that are yet to be solved, such as structure alterations, but it shows promise for continued development.

This thesis presents novel tasks and deep learning methods for machine reading comprehension and question answering with the goal of achieving natural language understanding. First, we consider a semantic parsing task where the model understands sentences and translates them into a logical form or instructions. We present a novel semi-supervised sequential autoencoder that considers language as a discrete sequential latent variable and semantic parses as the observations. This model allows us to leverage synthetically generated unpaired logical forms, and thereby alleviate the lack of supervised training data. We show the semi-supervised model outperforms a supervised model when trained with the additional generated data. Second, reading comprehension requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess reading comprehension ability, in both artificial agents and children learning to read. We propose a new, challenging, supervised reading comprehension task. We gather a large-scale dataset of news stories from the CNN and Daily Mail websites with Cloze-style questions created from the highlights. This dataset allows for the first time training deep learning models for reading comprehension. We also introduce novel attention-based models for this task and present qualitative analysis of the attention mechanism. Finally, following the recent advances in reading comprehension in both models and task design, we further propose a new task for understanding complex narratives, NarrativeQA, consisting of full texts of books and movie scripts. We collect human written questions and answers based on high-level plot summaries. This task is designed to encourage development of models for language understanding; it is designed so that successfully answering their questions requires understanding the underlying narrative rather than relying on shallow pattern matching or salience. We show that although humans solve the tasks easily, standard reading comprehension models struggle on the tasks presented here.

In this work, we develop an end-to-end neural network-based computer vision system to automatically identify where each person within a 2-D image of a school classroom is looking (“gaze following�), as well as who she/he is looking at. Automatic gaze following could help facilitate data-mining of large datasets of classroom observation videos that are collected routinely in schools around the world in order to understand social interactions between teachers and students. Our network is based on the architecture by Recasens, et al. (2015) but is extended to (1) predict not only where, but who the person is looking at; and (2) predict whether each person is looking at a target inside or outside the image. Since our focus is on classroom observation videos, we collect gaze dataset (48,907 gaze annotations over 2,263 classroom images) for students and teachers in classrooms. Results of our experiments indicate that the proposed neural network can estimate the gaze target - either the spatial location or the face of a person - with substantially higher accuracy compared to several baselines.

Submitted by Maicon Juliano Schmidt (maicons) on 2015-06-12T19:13:14Z No. of bitstreams: 1 Ingo Jost.pdf: 1217467 bytes, checksum: bf67cd6724b1cd182a12a3cd7b5af1eb (MD5) / Made available in DSpace on 2015-06-12T19:13:14Z (GMT). No. of bitstreams: 1 Ingo Jost.pdf: 1217467 bytes, checksum: bf67cd6724b1cd182a12a3cd7b5af1eb (MD5) Previous issue date: 2015-02-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Deep Learning é uma sub-área de Aprendizado de Máquina que tem obtido resultados sa- tisfatórios em várias áreas de aplicação, implementada por diferentes algoritmos, como Stacked Auto-encoders ou Deep Belief Networks. Este trabalho propõe uma modelagem que aplica uma implementação de um classificador que aborda técnicas de Deep Learning em Mineração de Opiniões, área que tem sido alvo de constantes estudos, dada a necessidade das corporações buscarem a compreensão que clientes possuem de seus produtos ou serviços. O favorecimento do crescimento de Mineração de Opiniões também se dá pelo ambiente colaborativo da Web 2.0, em que várias ferramentas propiciam a emissão de opiniões. Os dados utilizados passaram por um refinamento na etapa de pré-processamento com o intuito de aplicar Deep Learning, da qual uma das principais atribuições é a seleção de características, em dados refinados em vez de dados mais brutos. A promissora tecnologia de Deep Learning combinada com a estratégia de refinamento demonstrou nos experimentos a obtenção de resultados competitivos com outros estudos relacionados e abrem perspectiva de extensão deste trabalho. / Deep Learning is a Machine Learning’s sub-area that have achieved satisfactory results in different application areas, implemented by different algorithms, such as Stacked Auto- encoders or Deep Belief Networks. This work proposes a research that applies a classifier that implements Deep Learning concepts in Opinion Mining, area has been approached by con- stant researches, due the need of corporations seeking the understanding that customers have of your products or services. The Opinion Mining’s growth is favored also by the collaborative Web 2.0 environment, where multiple tools provide issuing opinions. The data used for exper- iments were refined in preprocessing step in order to apply Deep Learning, which it one of the main tasks the feature selection, in refined data, instead of applying Deep Learning in more raw data. The refinement strategy combined with the promising technology of Deep Learning has demonstrated in preliminary experiments the achievement of competitive results with other studies and opens the perspective for extension of this work.

O aprendizado profundo tem inúmeras aplicações na visão computacional, reconhecimento de fala, processamento de linguagem natural e outras aplicações de interesse comercial. A visão computacional, por sua vez, possui muitas aplicações em áreas distintas, indo desde o entretenimento à aplicações relevantes e críticas. O reconhecimento e manipulação de faces (Snapchat), e a descrição de objetos em fotos (OneDrive) são exemplos de aplicações no entretenimento. Ao passo que, a inspeção industrial, o diagnóstico médico, o reconhecimento de objetos em imagens capturadas por satélites (usadas em missões de resgate e defesa), os carros autônomos e o Sistema Avançado de Auxílio ao Motorista (SAAM) são exemplos de aplicações relevantes e críticas. Algumas das empresas de circuitos integrados mais importantes do mundo, como Xilinx, Intel e Nvidia estão apostando em plataformas dedicadas para acelerar o treinamento e a implementação de algoritmos de aprendizado profundo e outras alternativas de visão computacional para carros autônomos e SAAM devido às suas altas necessidades computacionais. Assim, implementar sistemas de aprendizado profundo que alcançam alto desempenho com o custo de baixa utilização de área e dissipação de potência é um grande desafio. Além do mais, os circuitos eletrônicos para a indústria automotiva devem ser confiáveis mesmo sob efeitos da radiação, defeitos de fabricação e efeitos do envelhecimento. Assim, um gerador automático de VHSIC (Very High Speed Integrated Circuit) Hardware Description Language (VHDL) para Redes Neurais Convolucionais (RNC) foi desenvolvido para reduzir o tempo associado a implementação de algoritmos de aprendizado profundo em hardware. Como estudo de caso, uma RNC foi treinada pela ferramenta Convolutional Architecture for Fast Feature Embedding (Caffe), de modo a classificar 6 classes de placas de trânsito, alcançando uma precisão de cerca de 89,8% no conjunto de dados German Traffic-Sign Recognition Benchmark (GTSRB), que contém imagens de placas de trânsito em cenários complexos. Essa RNC foi implementada num All-Programmable System-on- Chip (APSoC) Zynq-7000, resultando em 313 Frames Por Segundo (FPS) em imagens normalizadas para 32x32, com o APSoC dissipando uma potência de somente 2.057 W, enquanto uma Graphics Processing Unit (GPU) embarcada, em seu modo de operação mínimo, dissipa 10 W. A confiabilidade da RNC proposta foi investigada por injeções de falhas acumuladas e aleatórias por emulação nos bits de configuração da Lógica Programável (LP) do APSoC, alcançando uma confiabilidade de 80,5% sob Single-Bit-Upset (SBU) onde foram considerados ambos os Dados Corrompidos Silenciosos (DCSs) críticos e os casos em que o sistema não respondeu no tempo esperado (time-outs). Em relação às falhas múltiplas, a confiabilidade da RNC decresce exponencialmente com o número de falhas acumuladas. Em vista disso, a confiabilidade da RNC proposta deve ser aumentada através do uso de técnicas de proteção durante o fluxo de projeto. / Deep learning has a plethora of applications in computer vision, speech recognition, natural language processing and other applications of commercial interest. Computer vision, in turn, has many applications in distinct areas, ranging from entertainment applications to relevant and critical applications. Face recognition and manipulation (Snapchat), and object description in pictures (OneDrive) are examples of entertainment applications. Industrial inspection, medical diagnostics, object recognition in images captured by satellites (used in rescue and defense missions), autonomous cars and Advanced Driver-Assistance System (ADAS) are examples of relevant and critical applications. Some of the most important integrated circuit companies around the world, such as Xilinx, Intel and Nvidia are waging in dedicated platforms for accelerating the training and deployment of deep learning and other computer vision algorithms for autonomous cars and ADAS due to their high computational requirement. Thus, implementing a deep learning system that achieves high performance with low area utilization and power consumption costs is a big challenge. Besides, electronic equipment for automotive industry must be reliable even under radiation effects, manufacturing defects and aging effects, inasmuch as if a system failure occurs, a car accident can happen. Thus, a Convolutional Neural Network (CNN) VHSIC (Very High Speed Integrated Circuit) Hardware Description Language (VHDL) automatic generator was developed to reduce the design time associated to the implementation of deep learning algorithms in hardware. As a case study, a CNN was trained by the Convolutional Architecture for Fast Feature Embedding (Caffe) framework, in order to classify 6 traffic-sign classes, achieving an average accuracy of about 89.8% on the German Traffic-Sign Recognition Benchmark (GTSRB) dataset, which contains trafficsigns images in complex scenarios. This CNN was implemented on a Zynq-7000 All- Programmable System-on-Chip (APSoC), achieving about 313 Frames Per Second (FPS) on 32x32-normalized images, with the APSoC consuming only 2.057W, while an embedded Graphics Processing Unit (GPU), in its minimum operation mode, consumes 10W. The proposed CNN reliability was investigated by random piled-up fault injection by emulation in the Programming Logic (PL) configuration bits of the APSoC, achieving 80.5% of reliability under Single-Bit-Upset (SBU) where both critical Silent Data Corruptions (SDCs) and time-outs were considered. Regarding the multiple faults, the proposed CNN reliability exponentially decreases with the number of piled-up faults. Hence, the proposed CNN reliability must be increased by using hardening techniques during the design flow.

In most of critical infrastructures, Wireless Sensor Networks (WSNs) are deployed due to their low-cost, flexibility and efficiency as well as their wide usage in several infrastructures. Regardless of these advantages, WSNs introduce various security vulnerabilities such as different types of attacks and intruders due to the open nature of sensor nodes and unreliable wireless links. Therefore, the implementation of an efficient Intrusion Detection System (IDS) that achieves an acceptable security level is a stimulating issue that gained vital importance. In this thesis, we investigate the problem of security provisioning in WSNs based critical monitoring infrastructures. We propose a trust based hierarchical model for malicious nodes detection specially for Black-hole attacks. We also present various Machine Learning (ML)-driven IDSs schemes for wirelessly connected sensors that track critical infrastructures. In this thesis, we present an in-depth analysis of the use of machine learning, deep learning, adaptive machine learning, and reinforcement learning solutions to recognize intrusive behaviours in the monitored network. We evaluate the proposed schemes by using KDD'99 as real attacks data-sets in our simulations. To this end, we present the performance metrics for four different IDSs schemes namely the Clustered Hierarchical Hybrid IDS (CHH-IDS), Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS) and Q-learning based IDS (QL-IDS) to detect malicious behaviours in a sensor network. Through simulations, we analyzed all presented schemes in terms of Accuracy Rates (ARs), Detection Rates (DRs), False Negative Rates (FNRs), Precision-recall ratios, F_1 scores and, the area under curves (ROC curves) which are the key performance parameters for all IDSs. To this end, we show that QL-IDS performs with ~ 100% detection and accuracy rates.

Depth estimation is one of the most important problems in computer vision. It has attracted a lot of attention because it has applications in many areas, such as robotics, VR and AR, self-driving cars etc. Using the defocus blur of a camera lens is one of the methods of depth estimation. In this thesis, we have researched this technique in virtual environments. Virtual datasets have been created for this purpose. In this research, we have applied graph cuts and convolutional neural network (DfD-net) to estimate depth from defocus blur using a natural (Middlebury) and a virtual (Maya) dataset. Graph Cuts showed similar performance for both natural and virtual datasets in terms of NMAE and NRMSE. However, with regard to SSIM, the performance of graph cuts is 4% better for Middlebury compared to Maya. We have trained the DfD-net using the natural and the virtual dataset and then combining both datasets. The network trained by the virtual dataset performed best for both datasets. The performance of graph-cuts and DfD-net have been compared. Graph-Cuts performance is 7% better than DfD-Net in terms of SSIM for Middlebury images. For Maya images, DfD-Net outperforms Graph-Cuts by 2%. With regard to NRMSE, Graph-Cuts and DfD-net shows similar performance for Maya images. For Middlebury images, Graph-cuts is 1.8% better. The algorithms show no difference in performance in terms of NMAE. The time DfD-net takes to generate depth maps compared to graph cuts is 500 times less for Maya images and 200 times less for Middlebury images.