Simultaneous whole-animal 3D-imaging of neuronal activity using light field microscopy

3D functional imaging of neuronal activity in entire organisms at single cell level and physiologically relevant time scales faces major obstacles due to trade-offs between the size of the imaged volumes, and spatial and temporal resolution. Here, using light-field microscopy in combination with 3D deconvolution, we demonstrate intrinsically simultaneous volumetric functional imaging of neuronal population activity at single neuron resolution for an entire organism, the nematode Caenorhabditis elegans. The simplicity of our technique and possibility of the integration into epi-fluoresence microscopes makes it an attractive tool for high-speed volumetric calcium imaging.Comment: 25 pages, 7 figures, incl. supplementary informatio

NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies

Bayesian Optimization for Probabilistic Programs

We present the first general purpose framework for marginal maximum a posteriori estimation of probabilistic program variables. By using a series of code transformations, the evidence of any probabilistic program, and therefore of any graphical model, can be optimized with respect to an arbitrary subset of its sampled variables. To carry out this optimization, we develop the first Bayesian optimization package to directly exploit the source code of its target, leading to innovations in problem-independent hyperpriors, unbounded optimization, and implicit constraint satisfaction; delivering significant performance improvements over prominent existing packages. We present applications of our method to a number of tasks including engineering design and parameter optimization

An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk

Statistical models used to estimate the spatio-temporal pattern in disease risk from areal unit data represent the risk surface for each time period with known covariates and a set of spatially smooth random effects. The latter act as a proxy for unmeasured spatial confounding, whose spatial structure is often characterised by a spatially smooth evolution between some pairs of adjacent areal units while other pairs exhibit large step changes. This spatial heterogeneity is not consistent with existing global smoothing models, in which partial correlation exists between all pairs of adjacent spatial random effects. Therefore we propose a novel space-time disease model with an adaptive spatial smoothing specification that can identify step changes. The model is motivated by a new study of respiratory and circulatory disease risk across the set of Local Authorities in England, and is rigorously tested by simulation to assess its efficacy. Results from the England study show that the two diseases have similar spatial patterns in risk, and exhibit a number of common step changes in the unmeasured component of risk between neighbouring local authorities

A real-time low-cost vision sensor for robotic bin picking

This thesis presents an integrated approach of a vision sensor for bin picking. The vision system that has been devised consists of three major components. The first addresses the implementation of a bifocal range sensor which estimates the depth by measuring the relative blurring between two images captured with different focal settings. A key element in the success of this approach is that it overcomes some of the limitations that were associated with other related implementations and the experimental results indicate that the precision offered by the sensor discussed in this thesis is precise enough for a large variety of industrial applications. The second component deals with the implementation of an edge-based segmentation technique which is applied in order to detect the boundaries of the objects that define the scene. An important issue related to this segmentation technique consists of minimising the errors in the edge detected output, an operation that is carried out by analysing the information associated with the singular edge points. The last component addresses the object recognition and pose estimation using the information resulting from the application of the segmentation algorithm. The recognition stage consists of matching the primitives derived from the scene regions, while the pose estimation is addressed using an appearance-based approach augmented with a range data analysis. The developed system is suitable for real-time operation and in order to demonstrate the validity of the proposed approach it has been examined under varying real-world scenes

Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost

The demand for energy and cost efficient buildings has made architects and contractors more aware of the resources consumed by the built environment. While the actual economic and environmental costs of future construction can never be completely predicted, energy simulations and cost modeling have become accepted ways to guide the design and construction process by comparing possible outcomes. These tools are now commonplace in the construction industry, and researchers are continuing to develop new and innovative strategies to optimize building design and construction. Previous research has proven that genetic algorithms are effective methods to evaluate and optimize building design in situations that contain a large number of possible solutions. The technique makes a computationally difficult multi-optimization process possible but is still a reactive and time consuming process that focuses on evaluation rather than solution generation. This research presented in this paper builds upon established multi-objective optimization techniques that use an energy simulator to estimate a conceptual building’s energy use as well as construction cost. The study compares simulations of a simplified model of a 3-story inpatient hospital located in Atlanta, Georgia using a defined set of variables. A combined global minimum of annual energy consumption and total construction is sought after using a method that utilizes a genetic algorithm. The second phase of this research uses a modified approach that combines the traditional genetic algorithm with a seeding method that utilizes previous results. A new set of simulations were established that duplicates the initial trials using a slightly modified set of design variables. The simulation was altered, and the phase one trials were utilized as the first generation of simulated solutions. The objective of this thesis is to explore one method of making energy use and cost estimating more accessible to the construction industry by combining simulation optimization and indexing. The results indicate that this study’s proposed augmented approach has potential benefits to building design optimization, although more research is required to validate this hypothesis in its entirety. This study concludes that the proposed approach can potentially reduce the time needed for individual optimization exercises by creating a cumulative, robust catalog of previous computations that will inform and seed future analyses. The research was conducted in five general stages. The first part defines the research problem and scope of research to be conducted. In the second part, the concepts of genetic algorithms and energy simulation are explored in a comprehensive literature review. The remaining parts explain the trial simulations performed in this study. Part three explains the experiment’s methodology, and part four describes the simulation results. The fifth and final part looks at what the possible conclusions that can be made from analyzing the study’s results.MSCommittee Chair: Dr. Ashuri, Baabak; Committee Member: Dr. Castro, Daniel; Committee Member: Farrow, Rober

Towards extracting artistic sketches and maps from digital elevation models

The main trend of computer graphics is the creation of photorealistic images however, there is increasing interest in the simulation of artistic and illustrative techniques. This thesis investigates a profile based technique for automatically extracting artistic sketches from regular grid digital elevation models. The results resemble those drawn by skilled cartographers and artists.The use of cartographic line simplification algorithms, which are usually applied to complex two-dimensional lines such as coastlines, allow a set of most important points on the terrain surface to be identified, these form the basis for sketching.This thesis also contains a wide ranging review of terrain representation techniques and suggests a new taxonomy

Dynamics of an Idealized Fluid Model for Investigating Convective-scale Data Assimilation

An idealized fluid model of convective-scale numerical weather prediction, intended for use in inexpensive data assimilation experiments, is described here and its distinctive dynamics are investigated. The model modifies the rotating shallow water equations to include some simplified dynamics of cumulus convection and associated precipitation, extending and improving the model of Würsch and Craig. Changes to this original model are the removal of ad hoc diffusive terms and the addition of Coriolis rotation terms, leading to a so-called 1.5-dimensional model. Despite the non-trivial modifications to the parent equations, it is shown that this shallow water type model remains hyperbolic in character and can be integrated accordingly using a discontinuous Galerkin finite element method for nonconservative hyperbolic systems of partial differential equations. Combined with methods to ensure well-balancedness and non-negativity, the resulting numerical solver is novel, efficient and robust. Classical numerical experiments in the shallow water theory, such as the Rossby geostrophic adjustment and flow over topography, are reproduced for the standard shallow water model and used to highlight the modified dynamics of the new model. In particular, it exhibits important aspects of convective-scale dynamics relating to the disruption of large-scale balance and is able to simulate other features related to convecting and precipitating weather systems. Our analysis here and preliminary results suggest that the model is well suited for efficiently and robustly investigating data assimilation schemes in an idealized ‘convective-scale’ forecast assimilation framework

A Framework for Dynamic Terrain with Application in Off-road Ground Vehicle Simulations

The dissertation develops a framework for the visualization of dynamic terrains for use in interactive real-time 3D systems. Terrain visualization techniques may be classified as either static or dynamic. Static terrain solutions simulate rigid surface types exclusively; whereas dynamic solutions can also represent non-rigid surfaces. Systems that employ a static terrain approach lack realism due to their rigid nature. Disregarding the accurate representation of terrain surface interaction is rationalized because of the inherent difficulties associated with providing runtime dynamism. Nonetheless, dynamic terrain systems are a more correct solution because they allow the terrain database to be modified at run-time for the purpose of deforming the surface. Many established techniques in terrain visualization rely on invalid assumptions and weak computational models that hinder the use of dynamic terrain. Moreover, many existing techniques do not exploit the capabilities offered by current computer hardware. In this research, we present a component framework for terrain visualization that is useful in research, entertainment, and simulation systems. In addition, we present a novel method for deforming the terrain that can be used in real-time, interactive systems. The development of a component framework unifies disparate works under a single architecture. The high-level nature of the framework makes it flexible and adaptable for developing a variety of systems, independent of the static or dynamic nature of the solution. Currently, there are only a handful of documented deformation techniques and, in particular, none make explicit use of graphics hardware. The approach developed by this research offloads extra work to the graphics processing unit; in an effort to alleviate the overhead associated with deforming the terrain. Off-road ground vehicle simulation is used as an application domain to demonstrate the practical nature of the framework and the deformation technique. In order to realistically simulate terrain surface interactivity with the vehicle, the solution balances visual fidelity and speed. Accurately depicting terrain surface interactivity in off-road ground vehicle simulations improves visual realism; thereby, increasing the significance and worth of the application. Systems in academia, government, and commercial institutes can make use of the research findings to achieve the real-time display of interactive terrain surfaces