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Traumatic brain injury (TBI) affects millions of people worldwide, leading to significant mortality and disability rates. Elevated intracranial pressure (ICP) resulting from TBI can cause severe complications and requires early detection to improve patient outcomes. While invasive methods are commonly used to measure ICP accurately, non-invasive techniques such as optic nerve sheath diameter (ONSD) measurement show promise. This study aims at the creation of a tool that can automatically perform a segmentation of the ONS from a head computed tomography (CT) scan, and extracts meaningful measures from the segmentation mask, that can be used by radiologists and medics when treating people affected by TBI. This has been achieved using a deep learning model called ”nnU-Net”, commonly adopted for semantic segmentation in medical contexts. The project makes use of manually labeled head CT scans from a public dataset named CQ500, to train the aforementioned segmentation model, using an iterative approach. The initial training using 33 manually segmented samples demonstrated highly satisfactory segmentations, with good performance indicated by Dice scores. A subsequent training, combined with manual corrections of 44 unseen samples, further improved the segmentation quality. The segmentation masks enabled the development of an automatic tool to extract and straighten optic nerve volumes, facilitating the extraction of relevant measures. Correlation analysis with a binary label indicating potential raised ICP showed a stronger correlation when measurements were taken closer to the eyeball. Additionally, a comparison between manual and automated measures of optic nerve sheath diameter (ONSD), taken at a 3mm distance from the eyeball, revealed similarity between the two methods. Overall, this thesis lays the foundation for the creation of an automatic tool whose purpose is to make faster and more accurate diagnosis, by automatically segmenting the optic nerve and extracting useful prognostic predictors. / Traumatisk hjärnskada (TBI) drabbar miljontals människor över hela världen, vilket leder till betydande dödlighet och funktionshinder. Förhöjt intrakraniellt tryck (ICP) till följd av TBI kan orsaka allvarliga komplikationer och kräver tidig upptäckt för att förbättra patientens resultat. Medan invasiva metoder vanligtvis används för att mäta ICP exakt, icke-invasiva tekniker som synnervens höljediameter (ONSD) mätning ser lovande ut. Denna studie syftar till att skapa ett verktyg som automatiskt kan utföra en segmentering av ONS från en datortomografi skanning av huvudet, och extraherar meningsfulla åtgärder från segmenteringsmasken, som kan användas av radiologer och läkare vid behandling av personer som drabbats av TBI. Detta har uppnåtts med hjälp av en deep learning modell som kallas ”nnU-Net”, som vanligtvis används för semantisk segmentering i medicinska sammanhang. Projektet använder sig av manuellt märkta datortomografi skanningar från en offentlig datauppsättning som heter CQ500, för att träna den tidigare nämnda segmenteringsmodellen, med hjälp av en iterativ metod. Den inledande träningen med 33 manuellt segmenterade prov visade tillfredsställande segmentering, med god prestation indikerad av Dice-poäng. En efterföljande utbildning, i kombination med manuella korrigeringar av 44 osedda prover, förbättrade segmenteringskvaliteten ytterligare. Segmenteringsmaskerna möjliggjorde utvecklingen av ett automatiskt verktyg för att extrahera och räta ut optiska nervvolymer, vilket underlättade utvinningen av relevanta mått. Korrelationsanalys med en binär märkning som indikerar potentiellt förhöjd ICP visade en starkare korrelation när mätningar gjordes närmare ögongloben. Dessutom avslöjade en jämförelse mellan manuella och automatiserade mätningar av optisk nervmanteldiameter (ONSD), tagna på ett avstånd på 3 mm från ögongloben, likheten mellan de två metoderna. Sammantaget lägger denna avhandling grunden för skapandet av ett automatiskt verktyg vars syfte är att göra snabbare och mer exakta diagnoser, genom att automatiskt segmentera synnerven och extrahera användbara prognostiska prediktorer.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

Technology has revamped every aspect of our life, one of those various facets is fashion industry. Plenty of deep learning architectures are taking shape to augment fashion experiences for everyone. There are numerous possibilities of enhancing the fashion technology with deep learning. One of the key ideas is to generate fashion style and recommendation using artificial intelligence. Likewise, another significant feature is to gather reliable information of fashion trends, which includes analysis of existing fashion related images and data. When specifically dealing with images, localisation and segmentation are well known to address in-depth study relating to pixels, objects and labels present in the image. In this master thesis a complete framework is presented to perform localisation and segmentation on fashionista images. This work is a part of an interesting research work related to Fashion Style detection and Recommendation. Developed solution aims to leverage the possibility of localising fashion items in an image by drawing bounding boxes and labelling them. Along with that, it also provides pixel-wise semantic segmentation functionality which extracts fashion item label-pixel data. Collected data can serve as ground truth as well as training data for the aimed deep learning architecture. A study related to localisation and segmentation of videos has also been presented in this work. The developed system has been evaluated in terms of flexibility, output quality and reliability as compared to similar platforms. It has proven to be fully functional solution capable of providing essential localisation and segmentation services while keeping the core architecture simple and extensible. / Tekniken har förnyat alla aspekter av vårt liv, en av de olika fasetterna är modeindustrin. Massor av djupa inlärningsarkitekturer tar form för att öka modeupplevelser för alla. Det finns många möjligheter att förbättra modetekniken med djup inlärning. En av de viktigaste idéerna är att skapa modestil och rekommendation med hjälp av artificiell intelligens. På samma sätt är en annan viktig egenskap att samla pålitlig information om modetrender, vilket inkluderar analys av befintliga moderelaterade bilder och data. När det specifikt handlar om bilder är lokalisering och segmentering väl kända för att ta itu med en djupgående studie om pixlar, objekt och etiketter som finns i bilden. I denna masterprojekt presenteras en komplett ram för att utföra lokalisering och segmentering på fashionista bilder. Detta arbete är en del av ett intressant forskningsarbete relaterat till Fashion Style detektering och rekommendation. Utvecklad lösning syftar till att utnyttja möjligheten att lokalisera modeartiklar i en bild genom att rita avgränsande lådor och märka dem. Tillsammans med det tillhandahåller det även pixel-wise semantisk segmenteringsfunktionalitet som extraherar dataelementetikett-pixeldata. Samlad data kan fungera som grundsannelse samt träningsdata för den riktade djuplärarkitekturen. En studie relaterad till lokalisering och segmentering av videor har också presenterats i detta arbete. Det utvecklade systemet har utvärderats med avseende på flexibilitet, utskriftskvalitet och tillförlitlighet jämfört med liknande plattformar. Det har visat sig vara en fullt fungerande lösning som kan tillhandahålla viktiga lokaliseringsoch segmenteringstjänster samtidigt som kärnarkitekturen är enkel och utvidgbar.

<p>In this dissertation, we dedicate ourselves to applying deep-learning-based computer vision algorithms to industrial applications in 2D and 3D image processing. More specifically, we present the application of deep-learning-based image processing algorithms to the following three topics: RGB-image-based shipping box defect detection, RGB-image-based patients' bed-side status monitoring, and an RGBD-image-based 3D surface video conferencing system. These projects cover 2D image detection of static objects in industrial scenarios, 2D detection of dynamic human images in bedroom environments, and accurate 3D reconstruction of dynamic humanoid objects in video conferencing. In each of these projects, we proposed ready-to-deploy pipelines combining deep learning and traditional computer vision algorithms to improve the overall performance of industrial products. In each chapter, we describe in detail how we utilize, modify, and enhance the architecture of convolutional neural networks, including the training techniques using data acquisition, image annotation, synthetic datasets, and other schemes. In the relevant sections, we also present how post-processing steps with image processing algorithms can improve the overall effectiveness of the algorithm. We hope that our work demonstrates the versatility and advantages of deep neural networks in both 2D and 3D computer vision applications.</p>

An algorithm, which is able to find out, whether a given digital photo was tampered, and to generate tampering map, which depicts the processed parts of the image, was analyzed in details and implemented. The software was also optimized, deeply tested, the modes giving the best quality were found. The program can be launched on a usual user PC. / В процессе работы был детально разобран и реализован алгоритм поиска признаков постобработки в изображениях. Разработанное приложение было оптимизировано, было проведено его тестирование, были найдены режимы работы приложения с более высокими показателями точности. Реализованное приложение может быть запущено на обычном персональном компьютере. Помимо информации о наличии выявленных признаков постобработки полученное приложение генерирует карту поданного на вход изображения, на которой выделены его участки, возможно подвергнутые постобработке.

För närvarande utforskas quantum walk som en potentiell metod för att analysera medicinska bilder. Med inspiration från Gradys random walk-algoritm för bildbehandling har vi utvecklat en metod som bygger på de kvantmekaniska fördelar som quantum walk innehar för att detektera och segmentera medicinska bilder. Vidare har de segmenterade bilderna utvärderats utifrån klinisk relevans. Teoretiskt sett kan quantum walk-algoritmer erbjuda en mer effektiv metod för bildanalys inom medicin jämfört med traditionella metoder för bildsegmentering som exempelvis klassisk random walk, som inte bygger på kvantmekanik. Inom området finns omfattande potential för utveckling, och det är av yttersta vikt att fortsätta utforska och förbättra metoder. För närvarande kan det konstateras att det är en lång väg att vandra innan detta är något som kan appliceras i en klinisk miljö. / Currently, quantum walk is being explored as a potential method for analyzing medical images. Taking inspiration from Grady's random walk algorithm for image processing, we have developed an approach that leverages the quantum mechanical advantages inherent in quantum walk to detect and segment medical images. Furthermore, the segmented images have been evaluated in terms of clinical relevance. Theoretically, quantum walk algorithms have the potential to offer a more efficient method for medical image analysis compared to traditional methods of image segmentation, such as classical random walk, which do not rely on quantum mechanics. Within this field, there is significant potential for development, and it is of utmost importance to continue exploring and refining these methods. However, it should be noted that there is a long way to go before this becomes something that can be applied in a clinical environment.

Accurate segmentation of anatomical structures is crucial for radiation therapy in cancer treatment. Deep learning methods have been demonstrated effective for segmentation of 3D medical images, establishing the current standard. However, they require large amounts of labelled data and suffer from reduced performance on domain shift. A possible solution to these challenges is self-supervised learning, that uses unlabelled data to learn representations, which could possibly reduce the need for labelled data and produce more robust segmentation models. This thesis investigates the impact of self-supervised pre-training on an attention-based model for 3D medical image segmentation, specifically focusing on single-organ semantic segmentation, exploring whether self-supervised pre-training enhances the segmentation performance on CT scans with and without domain shift. The Swin UNETR is chosen as the deep learning model since it has been shown to be a successful attention-based architecture for semantic segmentation. During the pre-training stage, the contracting path is trained for three self-supervised pretext tasks using a large dataset of 5 465 unlabelled CT scans. The model is then fine-tuned using labelled datasets with 97, 142 and 288 segmentations of the stomach, the sternum and the pancreas. The results indicate that a substantial performance gain from self-supervised pre-training is not evident. Parameter freezing of the contracting path suggest that the representational power of the contracting path is not as critical for model performance as expected. Decreasing the amount of supervised training data shows that while the pre-training improves model performance when the amount of training data is restricted, the improvements are strongly decreased when more supervised training data is used. / Noggrann segmentering av anatomiska strukturer är avgörande för strålbehandling inom cancervården. Djupinlärningmetoder har visat sig vara effektiva och utgör standard för segmentering av 3D medicinska bilder. Dessa metoder kräver däremot stora mängder märkt data och kännetecknas av lägre prestanda vid domänskift. Eftersom självövervakade inlärningsmetoder använder icke-märkt data för inlärning, kan de möjligen minska behovet av märkt data och producera mer robusta segmenteringsmodeller. Denna uppsats undersöker effekten av självövervakad förberedande träning av en attention-baserad modell för 3D medicinsk bildsegmentering, med särskilt fokus på semantisk segmentering av enskilda organ. Syftet är att studera om självövervakad förberedande träning förbättrar segmenteringsprestandan utan respektive med domänskift. Swin UNETR har valts som djupinlärningsmodell eftersom den har visat sig vara en framgångsrik attention-baserad arkitektur för semantisk segmentering. Under den förberedande träningsfasen optimeras modellens kontraherande del med 5 465 icke-märkta CT-scanningar. Modellen tränas sedan på märkta dataset med 97, 142 och 288 segmenterade skanningar av magen, bröstbenet och bukspottkörteln. Resultaten visar att prestandaökningen från självövervakad förberedande träning inte är tydlig. Parameterfrysning av den kontraherande delen visar att dess representationer inte lika avgörande för segmenteringsprestandan som förväntat. Minskning av mängden träningsdata tyder på att även om den förberedande träningen förbättrar modellens prestanda när mängden träningsdata är begränsad, minskas förbättringarna betydligt när mer träningsdata används.

<p>Generative Adversarial Networks (GANs) have consistently defined the state-of-the-art in the generative modelling of high-quality images in several applications.  The images generated using GANs, however, do not lend themselves to being directly used in supervised learning tasks without first being curated through annotations.  This dissertation investigates how to carry out automatic on-the-fly segmentation of GAN-generated images and how this can be applied to the problem of producing high-quality simulated data for X-ray based security screening.  The research exploits the hidden layer properties of GAN models in a self-supervised learning framework for the automatic one-shot segmentation of images created by a style-based GAN.  The framework consists of a novel contrastive learner that is based on a Sinkhorn distance-based clustering algorithm and that learns a compact feature space for per-pixel classification of the GAN-generated images.  This facilitates faster learning of the feature vectors for one-shot segmentation and allows on-the-fly automatic annotation of the GAN images.  We have tested our framework on a number of standard benchmarks (CelebA, PASCAL, LSUN) to yield a segmentation performance that not only exceeds the semi-supervised baselines by an average wIoU margin of 1.02 % but also improves the inference speeds by a factor of 4.5.  This dissertation also presents BagGAN, an extension of our framework to the problem domain of X-ray based baggage screening.  BagGAN produces annotated synthetic baggage X-ray scans to train machine-learning algorithms for the detection of prohibited items during security screening.  We have compared the images generated by BagGAN with those created by deterministic ray-tracing models for X-ray simulation and have observed that our GAN-based baggage simulator yields a significantly improved performance in terms of image fidelity and diversity.  The BagGAN framework is also tested on the PIDRay and other baggage screening benchmarks to produce segmentation results comparable to their respective baseline segmenters based on manual annotations.</p>

Background:The dynamic field of computer vision and artificial intelligence has continually evolved, pushing the boundaries in areas like semantic segmentation andenvironmental recognition, pivotal for indoor scene analysis. This research investigates the integration of these two technologies, examining their synergy and implicayions for enhancing indoor scene understanding. The application of this integrationspans across various domains, including smart home systems for enhanced ambientliving, navigation assistance for Cleaning robots, and advanced surveillance for security. Objectives: The primary goal is to assess the impact of integrating semantic segmentation data on the accuracy of environmental recognition algorithms in indoor environments. Additionally, the study explores how environmental context can enhance the precision and accuracy of contour-aware semantic segmentation. Methods: The research employed an extensive methodology, utilizing various machine learning models, including standard algorithms, Long Short-Term Memorynetworks, and ensemble methods. Transfer learning with models like EfficientNet B3, MobileNetV3 and Vision Tranformer was a key aspect of the experimentation. The experiments were designed to measure the effect of semantic segmentation on environmental recognition and its reciprocal influence. Results: The findings indicated that the integration of semantic segmentation data significantly enhanced the accuracy of environmental recognition algorithms. Conversely, incorporating environmental context into contour-aware semantic segmentation led to notable improvements in precision and accuracy, reflected in metrics such as Mean Intersection over Union(MIoU). Conclusion: This research underscores the mutual enhancement between semantic segmentation and environmental recognition, demonstrating how each technology significantly boosts the effectiveness of the other in indoor scene analysis. The integration of semantic segmentation data notably elevates the accuracy of environmental recognition algorithms, while the incorporation of environmental context into contour-aware semantic segmentation substantially improves its precision and accuracy.The results also open avenues for advancements in automated annotation processes, paving the way for smarter environmental interaction.