Recovering facial shape using a statistical model of surface normal direction

In this paper, we show how a statistical model of facial shape can be embedded within a shape-from-shading algorithm. We describe how facial shape can be captured using a statistical model of variations in surface normal direction. To construct this model, we make use of the azimuthal equidistant projection to map the distribution of surface normals from the polar representation on a unit sphere to Cartesian points on a local tangent plane. The distribution of surface normal directions is captured using the covariance matrix for the projected point positions. The eigenvectors of the covariance matrix define the modes of shape-variation in the fields of transformed surface normals. We show how this model can be trained using surface normal data acquired from range images and how to fit the model to intensity images of faces using constraints on the surface normal direction provided by Lambert's law. We demonstrate that the combination of a global statistical constraint and local irradiance constraint yields an efficient and accurate approach to facial shape recovery and is capable of recovering fine local surface details. We assess the accuracy of the technique on a variety of images with ground truth and real-world images

3D shape reconstruction using a polarisation reflectance model in conjunction with shading and stereo

Reconstructing the 3D geometry of objects from images is a fundamental problem in computer vision. This thesis focuses on shape from polarisation where the goal is to reconstruct a dense depth map from a sequence of polarisation images. Firstly, we propose a linear differential constraints approach to depth estimation from polarisation images. We demonstrate that colour images can deliver more robust polarimetric measurements compared to monochrome images. Then we explore different constraints by taking the polarisation images under two different light conditions with fixed view and show that a dense depth map, albedo map and refractive index can be recovered. Secondly, we propose a nonlinear method to reconstruct depth by an end-to-end method. We re-parameterise a polarisation reflectance model with respect to the depth map, and predict an optimum depth map by minimising an energy cost function between the prediction from the reflectance model and observed data using nonlinear least squares. Thirdly, we propose to enhance the polarisation camera with an additional RGB camera in a second view. We construct a higher-order graphical model by utilising an initial rough depth map estimated from the stereo views. The graphical model will correct the surface normal ambiguity which arises from the polarisation reflectance model. We then build a linear system to combine the corrected surface normal, polarimetric information and rough depth map to produce an accurate and dense depth map. Lastly, we derive a mixed polarisation model that describes specular and diffuse polarisation as well as mixtures of the two. This model is more physically accurate and allows us to decompose specular and diffuse reflectance from multiview images

Integrating Shape-from-Shading & Stereopsis

This thesis is concerned with inferring scene shape by combining two specifictechniques: shape-from-shading and stereopsis. Shape-from-shading calculates shape using the lighting equation, which takes surface orientation and lighting information to irradiance. As irradiance and lighting information are provided this is the problem of inverting a many to one function to get surface orientation. Surface orientation may be integrated to get depth. Stereopsismatches pixels between two images taken from different locations of the same scene - this is the correspondence problem. Depth can then be calculated using camera calibration information, via triangulation. These methods both fail for certain inputs; the advantage of combining them is that where one fails the other may continue to work. Notably, shape-from-shading requires a smoothly shaded surface, without texture, whilst stereopsis requires texture - each works where the other does not. The first work of this thesis tackles the problem directly. A novel modular solution is proposed to combine both methods; combining is itself done using Gaussian belief propagation. This modular approach highlights missing and weak modules; the rest of the thesis is then concerned with providing a new module and an improved module. The improved module is given in the second research chapter and consists of a new shape-from-shading algorithm. It again uses belief propagation, but this time with directional statistics to represent surface orientation. Message passing is performed using a novel method; it is analytical, which makes this algorithm particularly fast. In the final research chapter a new module is provided, to estimate the light source direction. Without such a modulethe user of the system has to provide it; this is tedious and error prone, andimpedes automation. It is a probabilistic method that uniquely estimates the light source direction using a stereo pair as input

Computational Analyses of mRNA Ribosome Loading in Arabidopsis Thaliana

Translation of mRNA into protein is a critical step in gene expression, but the principles guiding its regulation at the genome level are not completely understood. Translation can be quantified at a genome scale by measuring the ribosome loading of mRNA—the extent to which mRNA is associated with ribosomes. In this dissertation, I present investigations into how genome-wide ribosome loading is controlled in Arabidopsis thaliana. In chapter 1, I give an overview of regulation of ribosome loading and translation. In chapter 2, I present research demonstrating for the first time that genome-wide ribosome loading in plants is partially controlled by the circadian clock. In chapter 3, I present a study of a computational model that describes how various biochemical steps control ribosome loading. And in chapter 4, I conclude by briefly summarizing the dissertation as a whole and discussing future perspectives

Effects of the vertical structure of the water column on the phytoplankton in a shallow, lagoonal estuary

Phytoplankton community composition is an important determinant of the effects of eutrophication in coastal systems. However, the system specific attributes governing the phytoplankton response to anthropogenic nutrient loading are still poorly resolved. The mixing regime of the Neuse River Estuary is an unusual attribute of the ecosystem. Unlike lakes and open ocean systems, stratification is primarily determined by salinity rather than temperature. Unlike most estuaries where tidal straining is the dominant mixing process, astronomical tides are negligible in the Neuse River Estuary. As a result, riverine discharge and wind stress determine oscillations between well-mixed and poorly mixed conditions on time scales of hours to days rather than the seasonal time scales of lakes and oceans or the daily time scales of most estuaries. The purpose of this study was to determine how this unusual pattern of mixing affects the phytoplankton community. The observed negative correlation between diatom biomass and stratification intensity indicates that settling losses significantly impact diatom biomass. Wind energy for mixing is linked to the maintenance of the diatoms through increases in the eddy diffusivity of the upper layer, deepening of the pycnocline, and resuspension from the sediments. When mixing is weak, growth limiting nutrients often accumulate in the bottom waters. The depth of the euphotic zone approximates the depth of the pycnocline creating a strong tendency for vertically separated light and nutrient resources. Under these conditions flagellates, particularly dinoflagellates and cryptophytes, use vertical migrations to access light and nutrients for growth. Water column stability does not appear to have an effect on flagellate biomass. However, as diatom biomass decreases under stratified conditions, flagellates dominate. The linkage between water column stability and community composition helps explain previously observed spatial and temporal distributions of community composition within the Neuse River Estuary. Additionally, average intensities of wind induced mixing may explain why the Neuse River Estuary is dominated by flagellates while tidally forced estuaries, such as Chesapeake Bay and San Francisco Bay, are dominated by diatoms. Eutrophication models may be improved by separately simulating the ecologically distinct diatom and flagellate groups