Industrial applications of computer vision often utilize traditional image processing techniques whereas state-of-the-art methods in most image processing challenges are almost exclusively based on convolutional neural networks (CNNs). Thus there is a large potential for improving the performance of many machine vision applications by incorporating CNNs. One such application is the classification of juice boxes with straws, where the baseline solution uses classical image processing techniques on depth images to reject or accept juice boxes. This thesis aim to investigate how CNNs perform on the task of semantic segmentation (pixel-wise classification) of said images and if the result can be used to increase classification performance. A drawback of CNNs is that they usually require large amounts of labelled data for training to be able to generalize and learn anything useful. As labelled data is hard to come by, two ways to get cheap data are investigated, one being synthetic data generation and the other being automatic labelling using the baseline solution. The implemented network performs well on semantic segmentation, even when trained on synthetic data only, though the performance increases with the ratio of real (automatically labelled) to synthetic images. The classification task is very sensitive to small errors in semantic segmentation and the results are therefore not as good as the baseline solution. It is suspected that the drop in performance between validation and test data is due to a domain shift between the data sets, e.g. variations in data collection and straw and box type, and fine-tuning to the target domain could definitely increase performance. When trained on synthetic data the domain shift is even larger and the performance on classification is next to useless. It is likely that the results could be improved by using more advanced data generation, e.g. a generative adversarial network (GAN), or more rigorous modelling of the data.

Generic visual tracking is one of the classical problems in computer vision. In this problem, no prior knowledge of the target is available aside from a bounding box in the initial frame of the sequence. The generic visual tracking is a difficult task due to a number of factors such as momentary occlusions, target rotations, changes in target illumination and variations in the target size. In recent years, discriminative correlation filter (DCF) based trackers have shown promising results for visual tracking. These DCF based methods use the Fourier transform to efficiently calculate detection and model updates, allowing significantly higher frame rates than competing methods. However, existing DCF based methods only estimate translation of the object while ignoring changes in size.This thesis investigates the problem of accurately estimating the scale variations within a DCF based framework. A novel scale estimation method is proposed by explicitly constructing translation and scale filters. The proposed scale estimation technique is robust and significantly improve the tracking performance, while operating at real-time. In addition, a comprehensive evaluation of feature representations in a DCF framework is performed. Experiments are performed on the benchmark OTB-2015 dataset, as well as the VOT 2014 dataset. The proposed methods are shown to significantly improve the performance of existing DCF based trackers. / Allmän visuell följning är ett klassiskt problem inom datorseende. I den vanliga formuleringen antas ingen förkunskap om objektet som skall följas, utöver en initial rektangel i en videosekvens första bild.Detta är ett mycket svårt problem att lösa allmänt på grund av occlusioner, rotationer, belysningsförändringar och variationer i objektets uppfattde storlek. På senare år har följningsmetoder baserade på diskriminativea korrelationsfilter gett lovande resultat inom området. Dessa metoder är baserade på att med hjälp av Fourertransformen effektivt beräkna detektioner och modellupdateringar, samtidigt som de har mycket bra prestanda och klarar av många hundra bilder per sekund. De nuvarande metoderna uppskattar dock bara translationen hos det följda objektet, medans skalförändringar ignoreras. Detta examensarbete utvärderar ett antal metoder för att göra skaluppskattningar inom ett korrelationsfilterramverk. En innovativ metod baserad på att konstruera separata skal och translationsfilter. Den föreslagna metoden är robust och har signifikant bättre följningsprestanda, samtidigt som den kan användas i realtid. Det utförs också en utvärdering av olika särdragsrepresentationer på två stora benchmarking dataset för följning.

Integrated camera systems for increasing safety and maneuverability are becoming increasingly common for heavy vehicles. One problem with heavy vehicles today is that there are blind spots where the driver has no or very little view. There is a great demand on increasing the safety and helping the driver to get a better view of his surroundings. This can be achieved by a sophisticated camera system, using cameras with wide field of view, that could cover dangerous blind spots. This master thesis aims to investigate and develop a prototype solution for a camera system consisting of two fisheye cameras. The solution covers both hardware choices and software development including camera calibration and image stitching. Two different fisheye camera calibration toolboxes are compared and their results discussed, with the aim to find the most suitable for this application. The result from the two toolboxes differ in performance, and the result from only one of the toolboxes is sufficient for image stitching.

Anomaly detection is a general theory of detecting unusual patterns or events in data. This master thesis investigates the subject of anomaly detection in two different applications. The first application is product inspection using a camera and the second application is surveillance using a 2D laser scanner. The first part of the thesis presents a system for automatic visual defect inspection. The system is based on aligning the images of the product to a common template and doing pixel-wise comparisons. The system is trained using only images of products that are defined as normal, i.e. non-defective products. The visual properties of the inspected products are modelled using three different methods. The performance of the system and the different methods have been evaluated on four different datasets. The second part of the thesis presents a surveillance system based on a single laser range scanner. The system is able to detect certain anomalous events based on the time, position and velocities of individual objects in the scene. The practical usefulness of the system is made plausible by a qualitative evaluation using unlabelled data.

There are situations where you would like to know the size of an object but do not have a ruler nearby. However, it is likely that you are carrying a smartphone that has an integrated digital camera, so imagine if you could snap a photo of the object to get a size estimation. Different methods for finding the dimensions of a cuboid from a photography are evaluated in this project. A simple Android application implementing these methods has also been created. To be able to perform measurements of objects in images we need to know how the scene is reproduced by the camera. This depends on the traits of the camera, called the intrinsic parameters. These parameters are unknown unless a camera calibration is performed, which is a non-trivial task. Because of this eight smartphone cameras, of different models, were calibrated in search of similarities that could give ground for generalisations. To be able to determine the size of the cuboid the scale needs to be known, which is why a reference object is used. In this project a credit card is used as reference, which is placed on top of the cuboid. The four corners of the reference as well as four corners of the cuboid are used to determine the dimensions of the cuboid. Two methods, one dependent and one independent of the intrinsic parameters, are used to find the width and length, i.e. the sizes of the two dimensions that share a plane with the reference. These results are then used in another two methods to find the height of the cuboid. Errors were purposely introduced to the corners to investigate the performance of the different methods. The results show that the different methods perform very well and are all equally suitable for this type of problem. They also show that having correct reference corners is more important than having correct object corners as the results were highly dependent on the accuracy of the reference corners. Another conclusion is that the camera calibration is not necessary because different approximations of the intrinsic parameters can be used instead. / Det finns tillfällen då man undrar över storleken på ett föremål, men inte har något mätinstrument i närheten. Det är dock troligt att du har en smartphone på dig. Smartphones har oftast en integrerad digitalkamera, så tänk om du kunde ta ett foto på föremålet och få en storleksuppskattning. I det här projektet har olika metoder för att beräkna dimensionerna på ett rätblock utvärderats. En enkel Android-applikation som implementerar dessa metoder har också skapats. För att kunna göra mätningar på föremål i bilder måste vi veta hur vyn återskapas av kameran. Detta beror på kamerans egenskaper vilka kallas kameraparametrarna. Dessa parametrar kan man få fram genom att göra en kamerakalibrering, vilket inte är en trivial uppgift. Därför har åtta smartphonekameror, från olika tillverkare, kalibrerats för att se om det finns likheter mellan kamerorna som kan befoga vissa generaliseringar. För att kunna räkna ut storleken på rätblocket måste skalan vara känd och därför används ett referensobjekt. I detta projekt har ett kreditkort använts som referensobjekt. Referensen placeras ovanpå rätblocket och sedan används fyra av referensens hörn samt fyra av rätblockets hörn i beräkningarna. Två metoder, en beroende och en oberoende av kameraparametrarna, har använts för att beräkna längden och bredden, alltså längden på de två sidor som ligger i samma plan som referensobjektet. Detta resultat används sedan i ytterligare två olika metoder för att beräkna höjden på rätblocket. För att undersöka hur de olika metoderna klarade av fel manipulerades hörnen. Resultaten visar att de olika metoderna fungerar bra och är alla lika lämpliga för att lösa den här uppgiften. De visar också på att det är viktigare att referensobjektets hörn är korrekta än rätblockets hörn eftersom referensobjektets hörn hade större inverkan på resultaten. En slutsats som också kan dras är att kameraparametrarna kan approximeras och att kamerakalibrering därför inte nödvändigtvis behöver utföras.

Fog and haze can have a dramatic impact on vision systems for land and sea vehicles. The impact of such conditions on infrared images is not as severe as for standard images. By fusing images from two cameras, one ordinary and one near-infrared camera, a complete dehazing system with colour preservation can be achieved. Applying several different algorithms to an image set and evaluating the results, the most suitable image fusion algoritm has been identified. Using an FPGA, a programmable integrated circuit, a crucial part of the algorithm has been implemented. It is capable of producing processed images 30 times faster than a laptop computer. This implementation lays the foundation of a real-time dehazing system and provides a significant part of the full solution. The results show that such a system can be accomplished with an FPGA.

This thesis proposes a hypothesis that a conventional, and essentially manual, HAZOP process can be improved with information obtained with model-based dynamic simulation, using a Monte Carlo approach, to update a Bayesian Belief model representing the expected relations between cause and effects - and thereby produce an enhanced HAZOP. The work considers how the expertise of a hazard and operability study team might be augmented with access to behavioural models, simulations and belief inference models. This incorporates models of dynamically complex system behaviour, considering where these might contribute to the expertise of a hazard and operability study team, and how these might bolster trust in the portrayal of system behaviour. With a questionnaire containing behavioural outputs from a representative systems model, responses were collected from a group with relevant domain expertise. From this it is argued that the quality of analysis is dependent upon the experience and expertise of the participants but this might be artificially augmented using probabilistic data derived from a system dynamics model. Consequently, Monte Carlo simulations of an improved exemplar system dynamics model are used to condition a behavioural inference model and also to generate measures of emergence associated with the deviation parameter used in the study. A Bayesian approach towards probability is adopted where particular events and combinations of circumstances are effectively unique or hypothetical, and perhaps irreproducible in practice. Therefore, it is shown that a Bayesian model, representing beliefs expressed in a hazard and operability study, conditioned by the likely occurrence of flaw events causing specific deviant behaviour from evidence observed in the system dynamical behaviour, may combine intuitive estimates based upon experience and expertise, with quantitative statistical information representing plausible evidence of safety constraint violation. A further behavioural measure identifies potential emergent behaviour by way of a Lyapunov Exponent. Together these improvements enhance the awareness of potential hazard cases.

Les systèmes autonomes sont des objets mobiles communicants capables de réaliser un certain nombre de tâches sans intervention humaine. Le coût (e.g. argent, poids, énergie) de la charge utile requise pour effectuer certaines missions est parfois trop important pour permettre aux engins d’embarquer la totalité des capacités nécessaires (i.e. capteurs et actionneurs). Répartir ces capacités sur plusieurs entités est une solution naturelle à ce problème. Un tel groupe d’entités constitue une flotte à laquelle il devient nécessaire de fournir un mécanisme de découverte permettant aux différents engins de partager leurs capacités respectives afin de résoudre une mission globale de façon collaborative. Ce mécanisme, outre l’affectation des tâches, doit gérer les conflits et les pannes potentielles qui peuvent survenir à tout moment sur tout engin de la flotte. Fort de ces constations, nous proposons un nouveau mécanisme collaboratif nommé AMiRALE qui apporte une solution aux problèmes ci-dessus pour les flottes hétérogènes d’engins mobiles autonomes. Notre système est entièrement distribué et repose uniquement sur des communications asynchrones. Nous proposonségalement un nouvel outil nommé NEmu permettant de créer des réseaux virtuels mobiles avec un contrôle important sur les propriétés de la topologie du réseau ainsi que sur la configuration des noeuds et des inter-connexions. Cet outil permet la réalisation d’expérimentations réalistes sur des prototypes d’applications réseaux. Enfin, nous proposons une évaluation de notre système collaboratif AMiRALE au travers d’un scénario de nettoyage de parc utilisant une flotte autonome de drones et de robots terrestres spécialisés. / We call autonomous systems, mobile and communicating objects which are able to perform several tasks without any human intervention. The overall cost (including price, weight and energy) of the payload required by some missions is sometimes too important to enable the entities to embed all the required capabilities (i.e. sensors and actuators). This is the reason why it is more suitable to spread all the capabilities among several entities. The team formed by those entities is called a swarm. It then becomes necessary to provide a discovery mechanism built into the swarm in order to enable its members to share their capabilities and to collaborate for achieving a global mission.This mechanism should perform task allocation as well as management of conflicts and failures which can occur at any moment on any entity of the swarm. In this thesis, we present a novel collaborative system which is called AMiRALE for heterogeneous swarms of autonomous mobile robots. Our system is fully distributed and relies only on asynchronous communications. We also present a novel tool called NEmu which enables to create virtual mobile networks with a complete control over the network topology, links and nodes properties. This tool is designed for performingrealistic experimentation on prototypes of network applications. Finally, we present experimental results on our collaborative system AMiRALE obtained through a park cleaning scenario which relies on an autonomous swarm of drones and specialized ground robots.

Les organisations possédant des infrastructures pour le calcul à haute performance rencontrent des difficultés dans la gestion de leurs ressources. Ces difficultés sont dues au fait que des applications de différents types doivent pouvoir accéder concurremment aux ressources tandis que les utilisateurs peuvent avoir des objectifs de performance variés pour leurs applications. Les nuages informatiques apportent plus de flexibilité et un meilleur contrôle des ressources qui laissent espérer une amélioration de la satisfaction des utilisateurs en terme de qualité de service perçue. Cependant, les solutions de nuage informatique actuelles fournissent un support limité aux utilisateurs pour l'expression ou l'utilisation de politiques de gestion de ressources et elles n'offrent aucun support pour atteindre les objectifs de performance des applications. Dans cette thèse, nous présentons une approche qui aborde ce défi d'une manière unique. Notre approche offre un contrôle des ressources complètement décentralisé en allouant des ressources à travers un marché à pourcentage proportionnel tandis que les applications s'exécutent dans des environnements virtuels autonomes capable d'ajuster la demande de l'application selon les objectifs de performance définis par l'utilisateur. La combinaison de la politique de distribution de la monnaie et de la variation dynamique du prix des ressources assure une utilisation des ressources équitable. Nous avons évalué notre approche en simulation et expérimentalement sur la plate-forme Grid'5000. Nos résultats montrent que notre approche peut permettre la cohabitation des différentes politiques d'utilisation des ressources sur l'infrastructure, tout en améliorant l'utilisation des ressources. / Organizations owning HPC infrastructures are facing difficulties in managing their resources. These difficulties come from the need to provide concurrent resource access to different application types while considering that users might have different performance objectives for their applications. Cloud computing brings more flexibility and better resource control, promising to improve the user’s satisfaction in terms of perceived Quality of Service. Nevertheless, current cloud solutions provide limited support for users to express or use various resource management policies and they don't provide any support for application performance objectives.In this thesis, we present an approach that addresses this challenge in an unique way. Our approach provides a fully decentralized resource control by allocating resources through a proportional-share market, while applications run in autonomous virtual environments capable of scaling the application demand according to user performance objectives.The combination of currency distribution and dynamic resource pricing ensures fair resource utilization.We evaluated our approach in simulation and on the Grid'5000 testbed. Our results show that our approach can enable the co-habitation of different resource usage policies on the infrastructure, improving resource utilisation.

Automated navigability assessment based on image sensor data is an important concern in the design of autonomous robotic systems. The problem consists in finding a mapping from input data to the navigability status of different areas of the surrounding world. Machine learning techniques are often applied to this problem. This thesis investigates an approach to navigability assessment in the image plane, based on offline learning using deep convolutional neural networks, applied to RGB and depth data collected using a robotic platform. Training outputs were generated by manually marking out instances of near collision in the sequences and tracing back the location of the near-collision frame through the previous frames. Several combinations of network inputs were tried out. Inputs included grayscale gradient versions of the RGB frames, depth maps, image coordinate maps and motion information in the form of a previous RGB frame or heading maps. Some improvement compared to simple depth thresholding was demonstrated, mainly in the handling of noise and missing pixels in the depth maps. The resulting networks appear to be mostly dependent on depth information; an attempt to train a network without the depth frames was unsuccessful,and a network trained using the depth frames alone performed similarly to networks trained with additional inputs. An unsuccessful attempt at training a network towards a more motion-dependent navigability concept was also made. It was done by including training frames captured as the robot was moving away from the obstacle, where the corresponding training outputs were marked as obstacle-free.