Myopia is a refractive defect of the eye in which collimated light produces images focused in front of the retina. Myopia can be artificially induced in animal models by form deprivation (form deprivation myopia, FDM) or by application of negative lenses (lens induced myopia, LIM). In this study myopia was induced using diffusers. The project had two main aims: 1. To determine if there is an interaction between the GABAergic system and dopaminergic system in the retina in terms of myopia? 2. To determine if there is an interaction between the GABAergic system and cholinergic system in the retina in terms of myopia? Firstly, an experiment focusing on the interaction between dopaminergic receptors antagonists and GABAC receptor antagonist was developed. Comparison of the different drug treated eye with the control was found and the effects of combination injections were compared to individual drug injections. Use of different blockers for various subtype of receptors simplified the understandings the underlying pharmacological interventions for GABAC receptor antagonist TPMPA. The D1 subtype of receptors was found to be involved in transmission of signals from GABAC receptors. Our results showed that D1 receptor antagonist SCH-23390 antagonizes the actions of TPMPA. In addition to this it was also found that possibly 5HT receptor may also play an important role in modulation of signaling from GABA receptor to dopaminergic receptors in the retina. These results were consistent with the drug combination effects for agonists. GABA A/C receptor agonist muscimol negativate the efficacy of D1 receptor agonist SKF-38393 but the activity of D2/4 receptor agonist quinpirole was not affected by muscimol. Although dopaminergic receptors are found to interact with GABAergic signaling, but an alternative interaction with anticholinergic (most widely studied antimyopic agents) could not be ruled out. This problem led to a follow-up experiment, in which GABA receptors intervention in anticholinergic agents was studied. The GABAergic receptor agonist muscimol when injected with anticholinergics (atropine and pirenzepine) showed a moderate interaction. As muscimol interacted with atropine to a lesser extent a more specific M1/5 receptor antagonist pirenzepine (earlier found to inhibit myopia) was used under these circumstances. The second aim to study the interaction between muscimol and pirenzepine showed more interaction with GABAA/C receptor agonist. There were data suggesting that there is a muscarinic and GABAergic interaction in retina, such that each modulation of each receptor had an effect on FDM. However, a drug combination treatment helped in understanding the underlying mechanism. Several previous studies have indicated that there exist a strong interaction between excitatory neurotransmitter acetylcholine and inhibitory transmitter GABA in retina. The results of this study indicate a similar finding. Thus results of this study may be summarized as: 1. D1 antagonists and not D2 antagonists blocks the antimyopic effects of GABAC antagonist TPMPA 2. GABA A/C agonist muscimol partially blocks the antimyopic activity of anticholinergics (e.g. atropine and pirenzepine).

La majorité des dispositifs de métrologie basés vision sont équipés de systèmes optiques stéréo ou de systèmes de mesure externes dits actifs. Les méthodes de reconstruction tridimensionnelle (Structure-from-Motion, Shape-from-Shading) applicables à la vision monoculaire souffrent généralement de l’ambiguïté d’échelle. Cette dernière est inhérente au processus d’acquisition d’images qui implique la perte de l’information de profondeur de la scène. La relation entre la taille des objets et la distance de la prise de vue est équivoque.Cette étude a pour objet l’estimation de l’échelle absolue d’une scène par vision passive monofocale. Elle vise à apporter une solution à l’ambiguïté d’échelle uniquement basée vision, pour un système optique monoculaire dont les paramètres internes sont fixes. Elle se destine plus particulièrement à la mesure des lésions en coloscopie. Cette procédure endoscopique (du grec endom : intérieur et scopie : vision) permet l’exploration et l’intervention au sein du côlon à l’aide d’un dispositif flexible (coloscope) embarquant généralement un système optique monofocal. Dans ce contexte, la taille des néoplasies (excroissances anormales de tissu) constitue un critère diagnostic essentiel. Cette dernière est cependant difficile à évaluer et les erreurs d’estimations visuelles peuvent conduire à la définition d’intervalles de temps de surveillance inappropriés. La nécessité de concevoir un système d’estimation de la taille des lésions coloniques constitue la motivation majeure de cette étude. Nous dressons dans la première partie de ce manuscrit un état de l’art synoptique des différents systèmes de mesure basés vision afin de positionner notre étude dans ce contexte. Nous présentons ensuite le modèle de caméra monofocal ainsi que le modèle de formation d’image qui lui a été associé. Ce dernier est la base essentielle des travaux menés dans le cadre de cette thèse. La seconde partie du manuscrit présente la contribution majeure de notre étude. Nous dressons tout d’abord un état de l’art détaillé des méthodes de reconstruction 3D basées sur l’analyse de l’information de flou optique (DfD (Depth-from-Defocus) et DfF (Depth-from-Defocus)). Ces dernières sont des approches passives permettant, sous certaines contraintes d’asservissement de la caméra, de résoudre l’ambiguïté d’échelle. Elles ont directement inspiré le système de mesure par extraction du point de rupture de netteté présenté dans le chapitre suivant. Nous considérons une vidéo correspondant à un mouvement d’approche du système optique face à une région d’intérêt dont on souhaite estimer les dimensions. Notre système de mesure permet d’extraire le point de rupture nette/flou au sein de cette vidéo. Nous démontrons que, dans le cas d’un système optique monofocale, ce point unique correspond à une profondeur de référence pouvant être calibrée. Notre système est composé de deux modules. Le module BET (Blur EstimatingTracking) permet le suivi et l’estimation conjointe de l’information de mise au point d’une région d’intérêt au sein d’une vidéo. Le module BMF (Blur Model Fitting) permet d’extraire de façon robuste le point de rupture de netteté grâce à l’ajustement d’un modèle de flou optique. Une évaluation de notre système appliqué à l’estimation de la taille des lésions coloniques démontre sa faisabilité. Le dernier chapitre de ce manuscrit est consacré à une perspective d’extension de notre approche par une méthode générative. Nous présentons, sous la forme d’une étude théorique préliminaire, une méthode NRSfM (Non-Rigid Structure-from-Motion) permettant la reconstruction à l’échelle de surfaces déformables. Cette dernière permet l’estimation conjointe de cartes de profondeurs denses ainsi que de l’image de la surface aplanie entièrement mise au point. (...) / Vision-based metrology devices generally embed stereoscopic sensors or active measurement systems. Most of the passive 3D reconstruction techniques (Structure-from-Motion, Shape from-Shading) adapted to monocular vision suffer from scale ambiguity. Because the processing of image acquisition implies the loss of the depth information, there is an ambiguous relationship between the depth of a scene and the size of an imaged object. This study deals with the estimation of the absolute scale of a scene using passive monofocal vision. Monofocal vision describes monocular system for which optical parameters are fixed. Such optical systems are notably embedded within endoscopic systems used in colonoscopy. This minimally invasive technique allows endoscopists to explore the colon cavity and remove neoplasias (abnormal growths of tissue). Their size is an essential diagnostic criterion for estimating their rate of malignancy. However, it is difficult to estimate and erroneous visual estimations lead to neoplasias surveillance intervals being inappropriately assigned. The need to design a neoplasia measurement system is the core motivation for our study. In the first part of this manuscript, we review state-of-the-art vision-based metrology devices to provide context for our system. We then introduce monofocal optical systems and the specific image formation model used in our study. The second part deals with the main contribution of our work. We first review in detail state of the art DfD (Depth-from-Defocus) and DfF (Depth-from-Defocus) approaches. They are passive computer vision techniques that enable us to resolve scale ambiguity. Our core contribution is introduced in the following chapter. We define the Infocus-Breakpoint (IB) that allows us to resolve scale from a regular video. The IB is the lower limit of the optical system’s depth of field. Our system relies on two novel technical modules: Blur-Estimating Tracking (BET) and Blur-Model Fitting (BMF). BET allows us to simultaneously track an area of interest and estimate the optical blur information. BMF allows us to robustly extract the IB by fitting an optical blur model to the blur measurement estimated by the BET module. For the optical system is monofocal, the IB corresponds to a reference depth that can be calibrated. In the last chapter, we evaluate our method and propose a neoplasia measurement system adapted to the constraints in colonoscopy examination. The last part of this manuscript is dedicated to a prospect of extension of our method by a generative approach. We present, as a preliminary study, a new NRSfM (Non-Rigid Structure-from-Motion) method allowing the scaled Euclidean 3D reconstruction of deformable surfaces. This approach is based on the simultaneous estimation of dense depth maps corresponding to a set of deformations as well as the in-focus color map of the flattened surface. We first review state-of-the-art methods for 3D reconstruction of deformable surfaces. We then introduce our new generative model as well as an alternation method allowing us to infer it.

Thesis (M.Sc. Agriculture (Pasture Science)) -- University of Limpopo, 2021 / Savanna rangelands are ecosystems which are characterized by the co-existence of scattered trees and shrubs with a continuous grass layer. However, the grass and tree balance has been highly altered as a result of disturbances caused by bush encroachment. Encroaching woody species have been shown to decrease species richness and abundance of the seed bank and ground‐layer diversity. So far little is known on the effect of bush encroachment and soil depth on the soil seed bank diversity in savanna rangelands. The objectives of this mini-dissertation were to (1) determine the influence of soil depth on soil seed bank diversity in bush encroached savanna rangelands, and (2) determine the relationships between soil seed bank herbaceous vegetation and physicochemical properties in encroached rangeland. In order to address these objectives, a savanna rangeland was demarcated into two encroachment gradients spanning from open to encroached rangeland. Within each encroachment gradient, six plots of 10 m x 10 m were randomly selected, whereby soil sampling and herbaceous vegetation were carried out and determined. In each replicate plot per encroachment level, five soil samples were randomly collected at 0-10 and 10-20 cm depths. The number of seedlings of different species emerging from the soil samples was used as a measure of the number of viable seeds in the soil and the composition of the seed bank using the seedling emergence method. The total seed densities showed significant differences (P<0.05) in the 0-10 cm depth layer in the open rangeland and encroached rangeland. Bush encroachment significantly (P<0.05) decreased the seed density of perennial grasses, specifically in 0-10 cm depth layer. Further, species diversity increased with bush encroachment in the 10-20 cm depth layer. Menhinick’s richness index showed no significant difference in the open and encroached rangeland, while species evenness decreased in the 0-10 cm depth layer and increased at 10-20 cm depth.The study also revealed negative correlations between organic carbon, calcium, clay, silt and forbs while mean weight diameter (MWD), a measure of soil aggregate stability was positively correlated with forbs. The canonical correspondence analysis (CCA) showed that pH, phosphorus, potassium and calcium were positively correlated to Eragrostis curvula and magnesium was negatively correlated to Panicum maximum. In open rangeland, CCA revealed that clay content was negatively correlated with species evenness while xii magnesium was negatively correlated to the Shannon Weiner index. Further, silt content was positively correlated with species richness and evenness. In the encroached rangeland, the CCA showed a negative correlation between magnesium and the Shannon Weiner index. The Sørensen’s index between soil seed banks and aboveground vegetation was low with index values of 0.22 and 0.24 in open and encroached rangeland, respectively. / AgriSeta

Depth information is a vital component for perception of the 3D structure of vehicle's surroundings in the autonomous scenario. Ubiquity and relatively low cost of camera equipment make image-based depth estimation very attractive compared to employment of the specialised sensors. Classical image-based depth estimation approaches typically rely on multi-view geometry, requiring alignment and calibration between multiple image sources, which is both cumbersome and error-prone. In contrast, single images lack both temporal information and multi-view correspondences. Also, depth information is lost in projection from the 3D world to a 2D image during the image formation process, making single image depth estimation problem ill-posed. In recent years, Deep Learning-based approaches have been widely proposed for single image depth estimation. The problem is typically tackled in a supervised manner, requiring access to image data with pixel-wise depth information. Acquisition of large amounts of such data that is both varied and accurate, is a laborious and costly task. As an alternative, a number of self-supervised approaches exist showing that it is possible to train models performing single image depth estimation using synchronized stereo image-pairs or sequences of monocular images instead of depth labeled data. This thesis investigates the self-supervised approach utilizing sequences of monocular images, by training and evaluating one of the state-of-the-art methods on both the popular public KITTI dataset and the data of the host company, Scania. A number of extensions are implemented for the method of choice, namely addition of weak supervision with velocity data, employment of geometry consistency constraints and incorporation of a self-attention mechanism. Resulting models showed good depth estimation performance for major components of the scene, such as nearby roads and buildings, however struggled at further ranges, and with dynamic objects and thin structures. Minor qualitative and quantitative improvements in performance were observed with introduction of geometry consistency loss and mask, as well as the self-attention mechanism. Qualitative improvements included the models' enhanced ability to identify clearer object boundaries and better distinguish objects from their background. Geometry consistency loss also proved to be informative in low-texture regions of the image and resolved artifacting behaviour that was observed when training models on Scania's data. Incorporation of the supervision of predicted translations using velocity data has proved to be effective at enforcing the metric scale of the depth network's predictions. However, a risk of overfitting to such supervision was observed when training on Scania's data. In order to resolve this issue, velocity-supervised fine-tuning procedure is proposed as an alternative to velocity-supervised training from scratch, resolving the observed overfitting issue while still enabling the model to learn the metric scale. Proposed fine-tuning procedure was effective even when training models on the KITTI dataset, where no overfitting was observed, suggesting its general applicability.

Thesis (M.Sc. Agriculture (Soil Science)) -- University of Limpopo, 2021 / Woody plant encroachment results in the degradation of grasslands. It is defined here as the increase in density, cover and biomass of woody plants into formerly open grasslands, reducing grassland productivity. Globally, many arid and semi-arid savanna grasslands are affected by this land cover transformation which changes the vegetation structure by altering the ratio of woody plants relative to grass species and influences soil hydrology. In the existing literature there is limited information on the effects of woody plant encroachment on soil physical and hydrological properties, especially in savanna grasslands. This study quantified and compared the soil physical and hydrological properties in the topsoil and subsoil of open and woody plant encroached grassland sites located on two contrasting soil forms, namely Bainsvlei and Rensburg. To achieve this objective, the two soils were sampled at various depth intervals from dug soil profiles at both sites at Towoomba Research Station in Bela Bela, Limpopo Province, South Africa. Soil physical properties including bulk density, porosity and aggregate stability as well as hydrological properties (water retention and hydraulic conductivity) were determined from collected samples. Compared to open grassland, soil bulk density was 11% and 10% greater in the topsoil and subsoil, while porosity was respectively 6% and 9% lower in the topsoil and subsoil of woody plant encroached grassland for Rensburg soils. In Bainsvlei soil, there was a minimal increase and decrease in the soil bulk density and porosity, respectively. Soil aggregate stability increased by 38% in the subsoil of woody plant encroached grasslands in Rensburg soil, due to increasing clay content with depth. In Bainsvlei soil, the soil aggregate stability was 9% and 13% lower in the topsoil and subsoil of the woody plant encroached grasslands compared to open grassland. Furthermore, the results revealed that in both soils, there was lower soil water retention and hydraulic conductivity in the topsoil and subsoil layers of woody plant encroached grassland than in open grasslands. There were no significant differences observed for soil hydraulic conductivity in the Bainsvlei and Rensburg topsoil. The subsoil hydraulic conductivity decreased by 24% in Bainsvlei and 44% in Rensburg soils in the woody plant encroached grassland. The soil water retention (SWR) decreased with an increase in woody plants. Specifically, there was 25% and 42% decrease in SWR with woody plant encroachment in the topsoil and subsoil of Bainsvlei soil, respectively. The same trend was observed in the Rensburg soils with 50% and 19% decrease in SWR in the topsoil and subsoil, respectively. Overall, the results revealed that soil type and depth influenced soil physical and hydrological properties in the studied woody plant encroached savanna grassland. As such, interventions aimed at controlling woody plant encroachment need to factor in soil type and depth in the development of management practices tailored to improve the soil hydrology of savanna grasslands

The diploma thesis deals with the use of Laser-Induced Breakdown Spectroscopy (LIBS) for depth profiling and 3D mapping of the zinc-coated steel used in the automotive industry. Before creating depth maps and depth profiles, optimization of the experiment was performed. It was shown that the LIBS technique is suited for making depth profiles and depth maps. The theoretical part deals with the description of the LIBS instrumentation, characteristics of plasma, and assembling of scientific papers which reflect the up to date knowledge about depth profiling and mapping with the use of the LIBS technique. The experimental part describes the optimization of the experiment. Gate delay, the depth and the diameter of craters using the profilometer, the position of the focal plane relative to the sample surface, and selection of spectral lines with the smallest residual signal and small scattering of data were optimized. Depth profiles of zinc, iron, chromium and manganese with the depth map of zinc and iron were made, and also the depth resolution for both elements was calculated.

Die vorliegende Arbeit leistet einen Beitrag zur Identifizierung von Einflussgrößen und Fehlerursachen auf Verkehrsunfälle. Diese können als Grundlage für Hinweise für den Einsatz und die Gestaltung von Fahrerassistenzsystemen genutzt werden. Dafür wurden insgesamt 506 Unfälle umfassend (in depth) untersucht. Zur Analyse der Pre-Crash-Phase der Unfälle wurden die Ergebnisse einer psychologischen Befragung mit Angaben aus der polizeilichen Verkehrsunfallanzeige, Informationen zur Unfallstelle, medizinischen Berichten sowie Informationen aus der technischen Rekonstruktion integriert. Anschließend wurde eine Fehleranalyse unter Betrachtung der Teilsysteme Fahrer, Umwelt und Fahrzeug durchgeführt. Um den bestmöglichen Befragungszeitpunkt herauszufinden, wurden in einer Vorstudie jeweils 15 Interviews am Unfallort sowie telefonische Interviews ein bis 14 Tage bzw. 15 bis 90 Tage nach dem Unfall bezüglich der Anzahl ihrer Genauigkeits- und Glaubhaftigkeitsmerkmale, der Motivation zur Interviewteilnahme sowie möglicher Vergessenseffekte verglichen. Im Ergebnis konnten keine Nachteile nachträglicher telefonischer Befragungen im Vergleich zu Befragungen an der Unfallstelle gefunden werden. Zur Fehleranalyse wurde ein verkehrspsychologisches Fehlerklassifikationsschema auf der Basis der verhütungsbezogenen Klassifikation von Fehlhandlungsursachen (Hacker, 1998) erstellt. Mit dessen Hilfe wurden insgesamt 696 Unfalleinflussfaktoren für die Unfallverursacher (n=343) ermittelt. Im Ergebnis wurde so bei allen Unfalltypengruppen ein hoher Anteil von Fehlern infolge von Ablenkung sowie Aktivierungsmängeln festgestellt (jeweils zwischen 28 % und 47%). Des Weiteren gab es bei Kreuzungsunfällen zahlreiche Fehler infolge von Sichtverdeckungen (40%), Fokusfehlern (30%), Reizmaskierungen (26%) und Verstößen gen die Verkehrsregeln (11%). Unfälle durch Abkommen von der Fahrbahn traten zudem häufig infolge von Erwartungsfehlern (35%), Reizmaskierungen (26%), Verstößen gegen die Verkehrsregeln (24%) sowie Zielsetzungs- bzw. Handlungsfehler (23%) auf. Unfälle im Längsverkehr passierten des Weiteren durch Erwartungsfehler (36%), Zielsetzungs- und Handlungsfehler (36%) sowie durch Setzen eines falschen Aufmerksamkeitsfokus (24%) auf. Anhand dieser Studienergebnisse ist das Sicherheitspotential für Fahrerassistenzsysteme, welche den Fahrer bei der Informationsaufnahme unterstützen und ihm helfen Ablenkungen und Aktivierungsdefizite zu vermeiden, als hoch einzuschätzen. So könnten insgesamt über zwei Drittel der erfassten Fehlhandlungen vermieden werden. Darüber hinaus münden die Studienergebnisse in ein Klassifikationsschema zur Erfassung von Unfalleinflussfaktoren, welches im Rahmen der Unfallforschung dauerhaft eingesetzt werden sollte.

La vision par ordinateur est une branche de l'intelligence artificielle dont le but est de permettre à une machine d'analyser, de traiter et de comprendre le contenu d'images numériques. La compréhension de scène en particulier est un enjeu majeur en vision par ordinateur. Elle passe par une caractérisation à la fois sémantique et structurelle de l'image, permettant d'une part d'en décrire le contenu et, d'autre part, d'en comprendre la géométrie. Cependant tandis que l'espace réel est de nature tridimensionnelle, l'image qui le représente, elle, est bidimensionnelle. Une partie de l'information 3D est donc perdue lors du processus de formation de l'image et il est d'autant plus complexe de décrire la géométrie d'une scène à partir d'images 2D de celle-ci.Il existe plusieurs manières de retrouver l'information de profondeur perdue lors de la formation de l'image. Dans cette thèse nous nous intéressons à l’estimation d'une carte de profondeur étant donné une seule image de la scène. Dans ce cas, l'information de profondeur correspond, pour chaque pixel, à la distance entre la caméra et l'objet représenté en ce pixel. L'estimation automatique d'une carte de distances de la scène à partir d'une image est en effet une brique algorithmique critique dans de très nombreux domaines, en particulier celui des véhicules autonomes (détection d’obstacles, aide à la navigation).Bien que le problème de l'estimation de profondeur à partir d'une seule image soit un problème difficile et intrinsèquement mal posé, nous savons que l'Homme peut apprécier les distances avec un seul œil. Cette capacité n'est pas innée mais acquise et elle est possible en grande partie grâce à l'identification d'indices reflétant la connaissance a priori des objets qui nous entourent. Par ailleurs, nous savons que des algorithmes d'apprentissage peuvent extraire ces indices directement depuis des images. Nous nous intéressons en particulier aux méthodes d’apprentissage statistique basées sur des réseaux de neurones profond qui ont récemment permis des percées majeures dans de nombreux domaines et nous étudions le cas de l'estimation de profondeur monoculaire. / Computer vision is a branch of artificial intelligence whose purpose is to enable a machine to analyze, process and understand the content of digital images. Scene understanding in particular is a major issue in computer vision. It goes through a semantic and structural characterization of the image, on one hand to describe its content and, on the other hand, to understand its geometry. However, while the real space is three-dimensional, the image representing it is two-dimensional. Part of the 3D information is thus lost during the process of image formation and it is therefore non trivial to describe the geometry of a scene from 2D images of it.There are several ways to retrieve the depth information lost in the image. In this thesis we are interested in estimating a depth map given a single image of the scene. In this case, the depth information corresponds, for each pixel, to the distance between the camera and the object represented in this pixel. The automatic estimation of a distance map of the scene from an image is indeed a critical algorithmic brick in a very large number of domains, in particular that of autonomous vehicles (obstacle detection, navigation aids).Although the problem of estimating depth from a single image is a difficult and inherently ill-posed problem, we know that humans can appreciate distances with one eye. This capacity is not innate but acquired and made possible mostly thanks to the identification of indices reflecting the prior knowledge of the surrounding objects. Moreover, we know that learning algorithms can extract these clues directly from images. We are particularly interested in statistical learning methods based on deep neural networks that have recently led to major breakthroughs in many fields and we are studying the case of the monocular depth estimation.

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