281 research outputs found

    Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)

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    The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment. FMER is a subset of image processing and it is a multidisciplinary topic to analysis. So, it requires familiarity with other topics of Artifactual Intelligence (AI) such as machine learning, digital image processing, psychology and more. So, it is a great opportunity to write a book which covers all of these topics for beginner to professional readers in the field of AI and even without having background of AI. Our goal is to provide a standalone introduction in the field of MFER analysis in the form of theorical descriptions for readers with no background in image processing with reproducible Matlab practical examples. Also, we describe any basic definitions for FMER analysis and MATLAB library which is used in the text, that helps final reader to apply the experiments in the real-world applications. We believe that this book is suitable for students, researchers, and professionals alike, who need to develop practical skills, along with a basic understanding of the field. We expect that, after reading this book, the reader feels comfortable with different key stages such as color and depth image processing, color and depth image representation, classification, machine learning, facial micro-expressions recognition, feature extraction and dimensionality reduction. The book attempts to introduce a gentle introduction to the field of Facial Micro Expressions Recognition (FMER) using Color and Depth images, with the aid of MATLAB programming environment.Comment: This is the second edition of the boo

    Accurate Colour Reproduction of Human Face using 3D Printing Technology

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    The colour of the face is one of the most significant factors in appearance and perception of an individual. With the rapid development of colour 3D printing technology and 3D imaging acquisition techniques, it is possible to achieve skin colour reproduction with the application of colour management. However, due to the complicated skin structure with uneven and non-uniform surface, it is challenging to obtain accurate skin colour appearance and reproduce it faithfully using 3D colour printers. The aim of this study was to improve the colour reproduction accuracy of the human face using 3D printing technology. A workflow of 3D colour image reproduction was developed, including 3D colour image acquisition, 3D model manipulation, colour management, colour 3D printing, postprocessing and colour reproduction evaluation. Most importantly, the colour characterisation methods for the 3D imaging system and the colour 3D printer were comprehensively investigated for achieving higher accuracy

    Enhancing Mesh Deformation Realism: Dynamic Mesostructure Detailing and Procedural Microstructure Synthesis

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    Propomos uma solução para gerar dados de mapas de relevo dinâmicos para simular deformações em superfícies macias, com foco na pele humana. A solução incorpora a simulação de rugas ao nível mesoestrutural e utiliza texturas procedurais para adicionar detalhes de microestrutura estáticos. Oferece flexibilidade além da pele humana, permitindo a geração de padrões que imitam deformações em outros materiais macios, como couro, durante a animação. As soluções existentes para simular rugas e pistas de deformação frequentemente dependem de hardware especializado, que é dispendioso e de difícil acesso. Além disso, depender exclusivamente de dados capturados limita a direção artística e dificulta a adaptação a mudanças. Em contraste, a solução proposta permite a síntese dinâmica de texturas que se adaptam às deformações subjacentes da malha de forma fisicamente plausível. Vários métodos foram explorados para sintetizar rugas diretamente na geometria, mas sofrem de limitações como auto-interseções e maiores requisitos de armazenamento. A intervenção manual de artistas na criação de mapas de rugas e mapas de tensão permite controle, mas pode ser limitada em deformações complexas ou onde maior realismo seja necessário. O nosso trabalho destaca o potencial dos métodos procedimentais para aprimorar a geração de padrões de deformação dinâmica, incluindo rugas, com maior controle criativo e sem depender de dados capturados. A incorporação de padrões procedimentais estáticos melhora o realismo, e a abordagem pode ser estendida além da pele para outros materiais macios.We propose a solution for generating dynamic heightmap data to simulate deformations for soft surfaces, with a focus on human skin. The solution incorporates mesostructure-level wrinkles and utilizes procedural textures to add static microstructure details. It offers flexibility beyond human skin, enabling the generation of patterns mimicking deformations in other soft materials, such as leater, during animation. Existing solutions for simulating wrinkles and deformation cues often rely on specialized hardware, which is costly and not easily accessible. Moreover, relying solely on captured data limits artistic direction and hinders adaptability to changes. In contrast, our proposed solution provides dynamic texture synthesis that adapts to underlying mesh deformations. Various methods have been explored to synthesize wrinkles directly to the geometry, but they suffer from limitations such as self-intersections and increased storage requirements. Manual intervention by artists using wrinkle maps and tension maps provides control but may be limited to the physics-based simulations. Our research presents the potential of procedural methods to enhance the generation of dynamic deformation patterns, including wrinkles, with greater creative control and without reliance on captured data. Incorporating static procedural patterns improves realism, and the approach can be extended to other soft-materials beyond skin

    Translational Functional Imaging in Surgery Enabled by Deep Learning

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    Many clinical applications currently rely on several imaging modalities such as Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), Computed Tomography (CT), etc. All such modalities provide valuable patient data to the clinical staff to aid clinical decision-making and patient care. Despite the undeniable success of such modalities, most of them are limited to preoperative scans and focus on morphology analysis, e.g. tumor segmentation, radiation treatment planning, anomaly detection, etc. Even though the assessment of different functional properties such as perfusion is crucial in many surgical procedures, it remains highly challenging via simple visual inspection. Functional imaging techniques such as Spectral Imaging (SI) link the unique optical properties of different tissue types with metabolism changes, blood flow, chemical composition, etc. As such, SI is capable of providing much richer information that can improve patient treatment and care. In particular, perfusion assessment with functional imaging has become more relevant due to its involvement in the treatment and development of several diseases such as cardiovascular diseases. Current clinical practice relies on Indocyanine Green (ICG) injection to assess perfusion. Unfortunately, this method can only be used once per surgery and has been shown to trigger deadly complications in some patients (e.g. anaphylactic shock). This thesis addressed common roadblocks in the path to translating optical functional imaging modalities to clinical practice. The main challenges that were tackled are related to a) the slow recording and processing speed that SI devices suffer from, b) the errors introduced in functional parameter estimations under changing illumination conditions, c) the lack of medical data, and d) the high tissue inter-patient heterogeneity that is commonly overlooked. This framework follows a natural path to translation that starts with hardware optimization. To overcome the limitation that the lack of labeled clinical data and current slow SI devices impose, a domain- and task-specific band selection component was introduced. The implementation of such component resulted in a reduction of the amount of data needed to monitor perfusion. Moreover, this method leverages large amounts of synthetic data, which paired with unlabeled in vivo data is capable of generating highly accurate simulations of a wide range of domains. This approach was validated in vivo in a head and neck rat model, and showed higher oxygenation contrast between normal and cancerous tissue, in comparison to a baseline using all available bands. The need for translation to open surgical procedures was met by the implementation of an automatic light source estimation component. This method extracts specular reflections from low exposure spectral images, and processes them to obtain an estimate of the light source spectrum that generated such reflections. The benefits of light source estimation were demonstrated in silico, in ex vivo pig liver, and in vivo human lips, where the oxygenation estimation error was reduced when utilizing the correct light source estimated with this method. These experiments also showed that the performance of the approach proposed in this thesis surpass the performance of other baseline approaches. Video-rate functional property estimation was achieved by two main components: a regression and an Out-of-Distribution (OoD) component. At the core of both components is a compact SI camera that is paired with state-of-the-art deep learning models to achieve real time functional estimations. The first of such components features a deep learning model based on a Convolutional Neural Network (CNN) architecture that was trained on highly accurate physics-based simulations of light-tissue interactions. By doing this, the challenge of lack of in vivo labeled data was overcome. This approach was validated in the task of perfusion monitoring in pig brain and in a clinical study involving human skin. It was shown that this approach is capable of monitoring subtle perfusion changes in human skin in an arm clamping experiment. Even more, this approach was capable of monitoring Spreading Depolarizations (SDs) (deoxygenation waves) in the surface of a pig brain. Even though this method is well suited for perfusion monitoring in domains that are well represented with the physics-based simulations on which it was trained, its performance cannot be guaranteed for outlier domains. To handle outlier domains, the task of ischemia monitoring was rephrased as an OoD detection task. This new functional estimation component comprises an ensemble of Invertible Neural Networks (INNs) that only requires perfused tissue data from individual patients to detect ischemic tissue as outliers. The first ever clinical study involving a video-rate capable SI camera in laparoscopic partial nephrectomy was designed to validate this approach. Such study revealed particularly high inter-patient tissue heterogeneity under the presence of pathologies (cancer). Moreover, it demonstrated that this personalized approach is now capable of monitoring ischemia at video-rate with SI during laparoscopic surgery. In conclusion, this thesis addressed challenges related to slow image recording and processing during surgery. It also proposed a method for light source estimation to facilitate translation to open surgical procedures. Moreover, the methodology proposed in this thesis was validated in a wide range of domains: in silico, rat head and neck, pig liver and brain, and human skin and kidney. In particular, the first clinical trial with spectral imaging in minimally invasive surgery demonstrated that video-rate ischemia monitoring is now possible with deep learning

    Čichový behaviorální obranný systém u člověka

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    This thesis consists of two parts. The first part introduces the importance of behavioural defence mechanisms, specifically the behavioural immune system and mainly in humans. I review current knowledge regarding behavioural defence mediated by odour cues. Although behavioural defence mechanisms are important for all individuals who live in social groups, I focus on mate choice, because that is the context in which olfaction-mediated behavioural defence is studied the most. Subsequently, the importance of olfaction is demonstrated using the example of self-inspection and I discuss how the behavioural immune system may be intertwined with this relatively understudied behaviour. Finally, last chapter deals with associations between olfaction and other modalities that play a role in the detection of cues which help select a healthy and immunocompetent partner. In the second part of the thesis, I present nine papers: three reviews and six empirical studies. The review papers summarise the functioning of the behavioural immune system, olfaction- mediated pathogen avoidance in mammals, and the merely weak association between attractiveness ratings based on different modalities. The first empirical paper investigates whether the threat caused by the Covid-19 pandemic led to increased perceived disgust,...Předkládaná disertační práce se skládá ze dvou částí. První část představuje behaviorální obranné mechanismy u člověka, zejména behaviorální imunitní systém. V této části také shrnuji poznatky o behaviorální obraně zprostředkované čichovými vodítky. Přestože jsou zmíněné obranné mechanismy důležité pro všechny jedince žijící v sociálních skupinách, disertační práce na toto téma nahlíží v kontextu výběru partnera, neboť je tento kontext nejvíce studovaným. V další kapitole pak poukazuji na důležitost čichové sebe-inspekce, a jak toto dosud jen velmi málo studované chování může být propojeno s behaviorálním imunitním systémem. Závěr první části práce je věnován vztahu mezi čichem a dalšími modalitami, které přispívají k rozpoznání zdravého a imunokompetentního partnera. V druhé části disertační práce představuji celkem devět článků, z čehož jsou tři přehledové články a šest empirických studií. Přehledové články shrnují fungování behaviorálního imunitního systému, čichem zprostředkovaného vyhýbání se patogenům a jak jsou vztahy mezi hodnoceními atraktivity z různých modalit asociovány jen slabě. První empirický článek se poté zabývá tím, zdali hrozba vyvolaná koronavirovou pandemií vede ke zvýšení vnímaného znechucení, jakožto hlavní proměnné asociované s behaviorálním imunitním systémem. Zjistili...Department of ZoologyKatedra zoologieFaculty of SciencePřírodovědecká fakult

    Woody Breast Implication on Broiler Meat Quality: A Piece of the Puzzle

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    Woody Breast condition (WB) is detrimental for water holding capacity of the meat (WHC). Two dietary experiments were conducted by varying the ingredients used to formulate traditional corn-soybean meal diets (SBM) in the attempt to minimize the negative impact of WB. In the first study, fast growing, high breast yielding broiler chicks were grown to d 49 and d 56 of age under simulated industry parameters. The birds were fed an iso-nitrogenous and iso-energetic traditional SBM diet, or a diet where the SBM was fully replaced by canola meal (CM), with ad libitum access to feed and water. After processing, the carcass yield (CY), breast yield (BY), drip loss (DL), pH, meat color (L*, a*, b*), and cook loss (CL) were determined to assess WHC. The incidence of WB severity of the fillets was also determined. In the second study, the same parameters were measured on d 42 birds fed four different diets. A third study was conducted to reduce the cooking times of the bigger breast fillets used to determine CL values using the oven method (OM), by using 3-D printed cutting molds to standardize the meat sample size. The data were subjected to analysis for WB incidence, and ranked analysis of variance. In experiment 1, the CM at d 49 reduced % incidence of WB severity and minimized meat quality parameters issues. In experiment 2, the Mix diet produced the lowest % incidence and severity of WB, but the harvested meat had a higher pH value. In contrast, when analyzing WB severity, pH was highest in the moderately affected fillets. In both studies, the reduction in WB was at the cost of BY. The modified OM was able to reduce cooking time and produce similar CL values to the OM

    Characterizing the role of oxygen in beef discoloration

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    The objective of this research was to characterize the role of oxygen in beef discoloration. Limited studies have evaluated the discoloration of the interior of steaks during retail display in association with the development of metmyoglobin. A recent approach in our laboratory has characterized interior color changes by means of needle-probe based single-fiber reflectance (SfR) spectroscopy. The a* values of steaks decreased (P < 0.05) with display time. Metmyoglobin at all depths from 1 mm to 5 mm estimated by the needle-probe SfR increased during display while showing greater metmyoglobin at a depth of 1-mm compared to the depths of 2 – 5 mm. Metmyoglobin estimated at 1 mm depth by the needle-probe SfR and the retail surface by HunterLab spectrophotometer were strongly negatively (P < 0.05) correlated with a* values during retail display. Therefore, internal metmyoglobin formation negatively influences surface color. Furthermore, the display surface was considered as oxygen exposed (OE), while the interior of the steak was denoted as not exposed to oxygen (NOE). NOE steak surface had greater (P < 0.05) metmyoglobin reducing ability compared with OE surfaces on d 6 of display. Oxygen exposure affected the oxygen consumption of the steaks, with the OE surfaces having lower (P < 0.05) oxygen consumption compared to NOE surfaces on d 6 of display. The loss of succinate from d 0 to d 6 of retail display reinforced the decline in color during display. Greater alpha-tocopherol in the NOE surface supported less oxidative changes compared to the OE surface during retail display. These results indicate the presence of oxygen can influence metabolite profile and negatively influence metmyoglobin reducing ability and color. In conclusion, the presence of oxygen can negatively impact the shelf life of steaks; however, the non-exposed interior of muscle remains more biochemically active

    Advanced traffic video analytics for robust traffic accident detection

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    Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time. First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road. Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system. The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents

    Model facial colour appearance and facial attractiveness for human complexions

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    Human facial complexion has been a subject of great interest in many areas of science and technology including dermatology, cosmetology, computer graphics, and computer vision. Facial colour appearance conveys vital personal information and influences social interactions and mate choices as contributing factors to perceived beauty, health, and age. How various colour characteristics affect facial preference and whether there are cultural differences are not fully understood. On the other hand, facial colour appearance cannot be simply quantified by colour measurement. Facial colour perception is distinctive. The perceptual aspects of facial colour appearance haven’t been precisely investigated. The present study aims to better understand the human colour perception of facial complexions. Psychophysical experiments were carried out to assess facial colour preference and facial colour appearance, respectively. A set of facial images of real human faces were used and the colour was rigorously controlled in those experiments so that the facial colour appearance could be evaluated based on the realistic skin models. Experiments on colour preference provided a thorough assessment of the relationships between various facial colour characteristics and preference judgements and meanwhile revealed large cultural differences between Caucasian and Chinese populations. A useful and repeatable analytical framework for facial preference modelling was provided. This work contributes to the growing body of research using realistic skin models and highlights the importance of examining various colour cues utilized in facial preference evaluation. Experiments on colour appearance for the first time precisely measured the overall colour perception of facial appearance. New indices WIS, RIS, and YIS were developed to accurately quantify perceived facial whiteness, redness, and yellowness. The perceptual difference between the colour appearance of the face stimuli and the nonface stimuli was discovered. Taken together, the present study shed new light on how our visual system perceives and processes colour information on human faces
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