L'objectif principale de la Réalité Mixte (RM) est de donner aux utilisateurs l'illusion que les objets virtuels et réels coexistent indistinctement dans le même espace. Une illusion efficace nécessite un recalage précis entre les deux mondes. Ce recalage doit être cohérent du point de vue géométrique et photométrique. Dans cette thèse, nous proposons de nouvelles méthodes de recalage photométrique pour estimer l'illumination et la réflectance de scènes réelles. Plus précisément, nous proposons des approches en nous attaquant à trois grands défis : (1) utilisation d'une seule caméra RGB-D. (2) estimation des propriétés de réflectance diffuse et spéculaire. (3) estimation de la position 3D et de la couleur de sources lumineuses dynamiques multiples. Dans notre première contribution, nous considérons des scènes réelles d’intérieurs où la géométrie et l'éclairage sont statiques. En observant la scène à partir d’une caméra mobile, des réflexions spéculaires peuvent être détectées tout au long de la séquence d'images RGB-D. Ces indices visuels sont très instructifs sur l'éclairage et la réflectance des surfaces des scènes. Par conséquent, nous les modélisons pour estimer à la fois les propriétés de réflectance diffuse et spéculaire ainsi que la position 3D de sources lumineuses multiples. Notre algorithme permet d'obtenir des résultats de RM convaincants tels que des ombres virtuelles réalistes ainsi qu'une suppression correcte de la spécularité réelle. Les ombres sont omniprésentes et représentent l’occultation de la lumière par la géométrie existante. Elles représentent donc des indices intéressants pour reconstituer les propriétés photométriques de la scène. La présence de texture dans ce contexte est un scénario critique. En effet, la séparation de la texture et des effets d'éclairage est souvent gérée par des approches qui nécessitent l’intervention de l'utilisateur ou qui ne répondent pas aux exigences du temps de traitement de la réalité mixte. Nous abordons ces limitations et proposons une méthode d'estimation de la position et de l'intensité des sources lumineuses. L'approche proposée gère les lumières dynamiques et fonctionne en temps quasi-réel. L'existence d'une source lumineuse est plus probable si elle est soutenue par plus d'un indice visuel. Nous abordons donc le problème de l'estimation des propriétés d’éclairage et de réflectance en analysant conjointement les réflexions spéculaires et les ombres projetées. L'approche proposée tire parti de l'information apportée par les deux indices pour traiter une grande variété de scènes. Notre approche est capable de traiter n'importe quelle surface texturée et tient compte à la fois des sources lumineuses statiques et dynamiques. Son efficacité est démontrée par une gamme d'applications, incluant la réalité mixte et la re-texturation. La détection des ombres projetées et des réflexions spéculaires étant au cœur de cette thèse, nous proposons finalement une méthode d'apprentissage approfondi pour détecter conjointement les deux indices visuels dans des scènes réelles d’intérieurs. / The overarching goal of Mixed Reality (MR) is to provide the users with the illusion that virtual and real objects coexist indistinguishably in the same space. An effective illusion requires an accurate registration between both worlds. This registration must be geometrically and photometrically coherent. In this thesis, we propose novel photometric registration methods to estimate the illumination and reflectance of real scenes. Specifically, we propose new approaches which address three main challenges: (1) use of a single RGB-D camera. (2) estimation of both diffuse and specular reflectance properties. (3) estimation of the 3D position and color of multiple dynamic light sources. Within our first contribution, we consider indoor real scenes where both geometry and illumination are static. As the sensor browses the scene, specular reflections can be observed throughout a sequence of RGB-D images. These visual cues are very informative about the illumination and reflectance of scene surfaces. Hence, we model these cues to recover both diffuse and specular reflectance properties as well as the 3D position of multiple light sources. Our algorithm allows convincing MR results such as realistic virtual shadows and correct real specularity removal. Shadows are omnipresent and result from the occlusion of light by existing geometry. They therefore represent interesting cues to reconstruct the photometric properties of the scene. Presence of texture in this context is a critical scenario. In fact, separating texture from illumination effects is often handled via approaches which require user interaction or do not satisfy mixed reality processing time requirements. We address these limitations and propose a method which estimates the 3D position and intensity of light sources. The proposed approach handles dynamic light sources and runs at an interactive frame rate. The existence of a light source is more likely if it is supported by more than one cue. We therefore address the problem of estimating illumination and reflectance properties by jointly analysing specular reflections and cast shadows. The proposed approach takes advantage of information brought by both cues to handle a large variety of scenes. Our approach is capable of handling any textured surface and considers both static and dynamic light sources. Its effectiveness is demonstrated through a range of applications including real-time mixed reality and retexturing. Since the detection of cast shadows and specular reflections are at the heart of this thesis, we further propose a deep-learning framework to jointly detect both cues in indoor real scenes.

Many species are sensitive to a light in ultraviolet spectrum. Some species have surface patterns that reflect ultraviolet light. These markings have been observed in many animal taxa; butterflies (Lepidoptera) are no exception. UV-reflectance in butterflies has been primarily connected to sexual selection and in this respect it has been a subject of many studies. In my work I propose an alternative view to this phenomenon. The aim of my work is to reveal how a particular environmental factors influence the morphospace of UV- reflectant patterns and wing shape of the Gonepteryx rhamni (Pieridae). The effect of various environmental factors (latitude, longitude, altitude, mean annual temperature, mean annual precipitation, normalized difference vegetation index - NDVI or net primary productivity - NPP) on wing morphospace was tested using the methods of Geometric morphometrics. I have also studied shape variability among the males and females, specimens from different locations and differences in morphospace of several G. rhamni subspecies. The dataset used in this analysis includes 118 males and 67 females from the Palearctic ecozone. The effect of almost all environmental (except to NDVI and NPP) predictors on shape of the UV-pattern and wing margin was significant in the case of males. In the...

Breeding of coniferous trees in the Czech Republic is undergoing an important development during last decades, especially thanks to molecular-genetic methods, which refine and simplify mapping of tree genotypes and the selection of superior genotypes. Recently, in the Czech Republic superior genotypes are selected based on forestry parameters (tree height, trunk diameter, and timber quality) what does not always correlate with the ability of a tree to resist abiotic and biotic stresses. Recently, there is an effort to include in the breeding also physiological parameters and select superior genotypes using nonspecific stress indicators, which are able to correspond better to tree fitness than the forestry growth parameters. The present thesis deals with genotypes of Scots pine (Pinus sylvestris L.) growing in seed orchards Doubrava and Silov in the Pilsen region in the Czech Republic. Seed orchards are tree plantations, which serve as a reserve of the genetically valuable reproduction material, they are parts of breeding programs. Pine needles were collected in July 2015 and analyzed for non-specific stress indicators - photosynthetic pigments, phenolics, lignin, cellulose and proline contents and leaf reflectance and fast chlorophyll fluorescence measurements. First objective of the present thesis...

<p>Hyperspectral imaging is widely used for obtaining optical information of light absorbers (e.g. biochemical composition) in a variety of specimens or tissues in a label-free manner. Acquiring and processing spectral data using hyperspectral imaging usually requires advanced instrumentation such as spectrometers, spectrographs or tunable color filters, which are not easily adaptable in developing instrumentation for field-based applications. Also, use of only RGB information from conventional cameras is not sufficient to obtain a reliable correlation with the actual content of the analyte of interest. We propose a new concept of ‘virtual hyperspectral imaging’ to reconstruct the full reflectance spectra from RGB image data. This allows us to use only RGB image data to determine detailed spatial distributions of analytes of interest. More importantly, it simplifies instrumentation without requiring bulky and expensive hardware. Using a data-driven approach, we apply multivariate regression to reconstruct hyperspectral reflectance image data from RGB images obtained using a conventional camera or a smartphone. </p> <p> </p> <p>In developing a reliable reconstruction matrix, it is critical to obtain a training data set of the specimen of study under the same optical geometry since the spectral reflectance and absorbance is sensitive to the detection and illumination parameters. We designed an image-guided hyperspectral system that can acquire both hyperspectral reflectance and RGB data sets under the same imaging configuration to minimize any discrepancies in the hyperspectral reflectance data acquired using different optical sensing geometries. In our technology development, a telecentric lens that is commonly used in machine vision systems but rarely in bioimaging, serves as a key component for reducing unwanted scattering in biological tissue due to its highly anisotropic scattering properties, by acting as a back-directional gating component to suppress diffuse light. We evaluate our spectrometer-less reflectance imaging method using RGB-based hyperspectral reconstruction algorithm for integration into a smartphone application for non-invasive hemoglobin analysis for anemia risk assessment in communities with limited access to central laboratory tests.</p>

<p></p><p>The study deals with one of the critical aspects of the Road Departure Mitigation System. The first part of the study finds out what all color combinations and patterns represent the US roadside curbs, what should be the range of Radar Reflectivity values, and LiDAR Reflectance bounds that a surrogate curb should satisfy. The later part of the thesis illustrates methods and steps on how to mimic the extracted properties, design a surrogate curb as per federal standards, and then develop a surrogate curb.</p><br><p></p>

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Black carbon has been reported to have major impacts on climate, environmental quality, and health. A chemothermal oxidation method, ordinarily used to isolate black carbon from soils, sediments and aquatic samples was applied to explore atmospheric samples. The results were then compared with thermal optical reflectance measurements, and then investigated in parallel with a health survey conducted during sampling. This study assesses atmospheric emissions from the NTPC Farakka coal-fired power plant in West Bengal, the distribution of emissions, the mobilization of black carbon, and its impact on pulmonary and cardiovascular conditions among respondents in Murshidabad and Malda, in West Bengal, India. Two measurement campaigns were conducted during winter and summer (November 2021 – June 2022); a total of 81 samples (Murshidabad) and 65 samples (Malda) were collected and analyzed and 193 respondents were surveyed for lung functionality. Over the study period, the mean concentration of PM2.5 and soot were observed to be 147 μg/m³; 113 μg/m³ and 1.52 μg/m³; 1.54 μg/m³ respectively. From the spirometry tests, it was concluded that there was chronic exposure to PM2.5 at both the sites, during the winter and monsoon campaigns. With increases in PM2.5 corresponding with higher numbers of restrictive and obstructive cases.

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In the past few years, studies regarding new bio-based materials have led to an increased attention in the nanoscale product of cellulose, called nanocellulose. This biodegradable and renewable material has interesting physical, optical and thermal properties. The optical properties could be affected by tuning the nanostructure of the material, which makes it interesting for further investigation. The promising properties of nanocellulose can be useful in many different applications. The aim of this work was therefore to study the optical properties of nanocellulose, and to examine if the material is suitable for spectral design of camouflage or in passive cooling applications. The optical properties of a nanocellulose, specifically cellulose nanofiber (CNF), have been studied. Freestanding CNF films and CNF films deposited on glass substrates were made and characterized by spectroscopy, ellipsometry, BRDF measurements, and optical microscopy. The freestanding samples were examined with different CNF concentrations of 0.52% and 1.0%, and different thicknesses. The samples on glass substrates all had a concentration of 1.0% CNF, but with different amount deposited solution which was either drop or spin coated. The freestanding CNF samples show high transmission in the visual region and relatively high emissivity in the atmospheric windows. This implies that it can be used as an effective material for passive radiative cooling. A thicker sample could also be used to increase the emissivity in the atmospheric windows and improve the ability for passive cooling. The low reflectance, and high emissivity in the atmospheric windows can be promising for use in camouflage applications, according to earlier studies. However, the suitable properties are very dependent on the spectral response of the background. Additional measurements need to be performed and more specified scenarios are necessary to draw any further conclusions.