Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview Learning for Medical Image Segmentation

The utilisation of deep learning segmentation algorithms that learn complex organs and tissue patterns and extract essential regions of interest from the noisy background to improve the visual ability for medical image diagnosis has achieved impressive results in Medical Image Computing (MIC). This thesis focuses on retinal blood vessel segmentation tasks, providing an extensive literature review of deep learning-based medical image segmentation approaches while comparing the methodologies and empirical performances. The work also examines the limitations of current state-of-the-art methods by pointing out the two significant existing limitations: data size constraints and the dependency on high computational resources. To address such problems, this work proposes a novel efficient, simple multiview learning framework that contrastively learns invariant vessel feature representation by comparing with multiple augmented views by various transformations to overcome data shortage and improve generalisation ability. Moreover, the hybrid network architecture integrates the attention mechanism into a Convolutional Neural Network to further capture complex continuous curvilinear vessel structures. The result demonstrates the proposed method validated on the CHASE-DB1 dataset, attaining the highest F1 score of 83.46% and the highest Intersection over Union (IOU) score of 71.62% with UNet structure, surpassing existing benchmark UNet-based methods by 1.95% and 2.8%, respectively. The combination of the metrics indicates the model detects the vessel object accurately with a highly coincidental location with the ground truth. Moreover, the proposed approach could be trained within 30 minutes by consuming less than 3 GB GPU RAM, and such characteristics support the efficient implementation for real-world applications and deployments

Investigating Surface Temperature from First Principles: Seedling Survival, Microclimate Buffering, and Implications for Forest Regeneration

Forests are extremely important ecosystems with large impacts on global water, energy, and biogeochemical cycling, and they provide numerous ecosystems services to human populations. Even though these systems consist of long-lived vegetation, forests are constantly experiencing changes to their extent and composition through the interacting forces of disturbance dynamics and climate change. In semi-arid landscapes like the western United States, patterns of recurring wildfire and subsequent seedling recruitment and forest regeneration are important in establishing the distribution of forests on the landscape. In this context, climate, hydrology, and existing vegetation all act together to limit the current and potential range of forest tree species. Most studies of forest persistence and regeneration use empirical observations and models that are unable to investigate the underlying climatic and hydrologic drivers of forest range shifts or extrapolate to novel temporal or spatial conditions. Here, we aim to study forest persistence and regeneration from first principles, identifying patterns of seedling survival in response to soil surface temperature, an integrative representation of the energy and water balance. We then use a process-based model to examine surface temperatures as a constraint on seedling establishment and forest persistence across the western United States

DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform

Kinematic Signatures of Galaxy Evolution: The Energetics of AGN Outflows and The Accurate Identification of Merging Galaxies

Both galaxies and supermassive black holes grow and evolve over cosmic time. My work utilizes the kinematics of the stars and gas in galaxies to investigate some key processes that drive this evolution: Active galactic nucleus (AGN) feedback and galaxy mergers. I will first present my work modeling AGN-driven biconical outflows and examine the potential for these biconical outflows to drive feedback that regulates star formation in their host galaxies. Then, I will focus on merging galaxies, and how progress in our understanding of galaxy evolution is slowed by the difficulty of making accurate galaxy merger identifications. My approach to improving the accuracy of galaxy merger identification involves using N-body/hydrodynamical simulations of merging galaxies to create mockup images and kinematic maps of galaxies that match the specifications of observational surveys. From these, I create a classification tool that identifies merging galaxies of different gas fractions, mass ratios, and merger stages. I will discuss the strengths and limitations of the classification technique and then my plans to apply the classification to Sloan Digital Sky Survey imaging as well as the &gt;10,000 observed galaxies in the MaNGA (Mapping Nearby Galaxies at Apache Point) integral field spectroscopy survey. Through accurate identification of merging galaxies in the MaNGA survey, I will advance our understanding of supermassive black hole growth in galaxy mergers and other open questions related to galaxy evolution.</p

Demonstration of Large Area Land Cover Classification with a One Dimensional Convolutional Neural Network Applied to Single Pixel Temporal Metric Percentiles

Over large areas, land cover classification has conventionally been undertaken using satellite time series. Typically temporal metric percentiles derived from single pixel location time series have been used to take advantage of spectral differences among land cover classes over time and to minimize the impact of missing observations. Deep convolutional neural networks (CNNs) have demonstrated potential for land cover classification of single date images. However, over large areas and using time series their application is complicated because they are sensitive to missing observations and they may misclassify small and spatially fragmented surface features due to their spatial patch-based implementation. This study demonstrates, for the first time, a one-dimensional (1D) CNN single pixel time series land classification approach that uses temporal percentile metrics and that does not have these issues. This is demonstrated for all the Conterminous United States (CONUS) considering two different 1D CNN structures with 5 and 8 layers, respectively. CONUS 30 m land cover classifications were derived using all the available Landsat-5 and -7 imagery over a seven-month growing season in 2011 with 3.3 million 30 m land cover class labelled samples extracted from the contemporaneous CONUS National Land Cover Database (NLCD) 16 class land cover product. The 1D CNNs and, a conventional random forest model, were trained using 10%, 50% and 90% samples, and the classification accuracies were evaluated with an independent 10% proportion. Temporal metrics were classified using 5, 7 and 9 percentiles for each of five Landsat reflective wavelength bands and their eight band ratios. The CONUS and detailed 150 × 150 km classification results demonstrate that the approach is effective at scale and locally. The 1D CNN classification land cover class boundaries were preserved for small axis dimension features, such as roads and rivers, with no stripes or anomalous spatial patterns. The 8-layer 1D CNN provided the highest overall classification accuracies and both the 5-layer and 8-layer 1D CNN architectures provided higher accuracies than the random forest by 1.9% - 2.8% which as all the accuracies were \u3e 83% is a meaningful increase. The CONUS overall classification accuracies increased marginally with the number of percentiles (86.21%, 86.40%, and 86.43% for 5, 7 and 9 percentiles, respectively) using the 8-layer 1D-CNN. Class specific producer and user accuracies were quantified, with lower accuracies for the developed land, crop and pasture/hay classes, but no systematic pattern among classes with respect to the number of temporal percentiles used. Application of the trained model to a different year of CONUS Landsat ARD showed moderately decreased accuracy (80.79% for 7 percentiles) that we illustrate is likely due to different intra-annual surface variations between years. These encouraging results are discussed with recommended research for deep learning using temporal metric percentiles

Optimization techniques for computationally expensive rendering algorithms

Realistic rendering in computer graphics simulates the interactions of light and surfaces. While many accurate models for surface reflection and lighting, including solid surfaces and participating media have been described; most of them rely on intensive computation. Common practices such as adding constraints and assumptions can increase performance. However, they may compromise the quality of the resulting images or the variety of phenomena that can be accurately represented. In this thesis, we will focus on rendering methods that require high amounts of computational resources. Our intention is to consider several conceptually different approaches capable of reducing these requirements with only limited implications in the quality of the results. The first part of this work will study rendering of time-­¿varying participating media. Examples of this type of matter are smoke, optically thick gases and any material that, unlike the vacuum, scatters and absorbs the light that travels through it. We will focus on a subset of algorithms that approximate realistic illumination using images of real world scenes. Starting from the traditional ray marching algorithm, we will suggest and implement different optimizations that will allow performing the computation at interactive frame rates. This thesis will also analyze two different aspects of the generation of anti-­¿aliased images. One targeted to the rendering of screen-­¿space anti-­¿aliased images and the reduction of the artifacts generated in rasterized lines and edges. We expect to describe an implementation that, working as a post process, it is efficient enough to be added to existing rendering pipelines with reduced performance impact. A third method will take advantage of the limitations of the human visual system (HVS) to reduce the resources required to render temporally antialiased images. While film and digital cameras naturally produce motion blur, rendering pipelines need to explicitly simulate it. This process is known to be one of the most important burdens for every rendering pipeline. Motivated by this, we plan to run a series of psychophysical experiments targeted at identifying groups of motion-­¿blurred images that are perceptually equivalent. A possible outcome is the proposal of criteria that may lead to reductions of the rendering budgets

Understanding and forecasting tropical cyclone intensity change

Includes bibliographical references.This research investigates several issues pertaining to tropical cyclone intensity change. Previous research on tropical cyclone intensity change is reviewed in great detail. The applicability of upper-level forcing theories is questioned. Inner-core processes related to intensity change are studied, with particular attention on the relationship between the vertical profile of the tangential wind (vt) field in the eyewall region and future pressure changes. For cases under minimal wind shear and warm SSTs such that vigorous inner-core updrafts exist, the cyclonic circulation should be mostly conserved almost to the upper-troposphere, with the largest vertical vt variation confined near the tropopause. It is hypothesized that a vertically conserved wind profile is conducive to fast intensification. Observations support this theory. By stratifying inner-core data into fast and slow developers, it is shown that fast developing tropical cyclones contain a more vertically stacked inner-core vortex than slow developers. It is also shown that a direct correlation exists between inner-core upper-level winds and tropical cyclone intensification, with the rate of intensification proportional to the magnitude and symmetry of upper-level vt. Diagnostic calculations using the Balanced Vortex equations also support this assertion. An alternative air-sea interaction theory is presented which incorporates boundary layer cooling. The buoyancy calculations include partial water-loading and ice microphysics, and their relevance to CAPE calculations in the tropics is discussed. It is shown that the lateral extension of the eye, above a sloping eyewall, is the crucial component in maintaining the air-sea interaction despite boundary layer cooling. Implications on the maximum intensity a storm may achieve are discussed. A multiple regression scheme with intensity change as the dependent variable has been developed. The new scheme is titled the Typhoon Intensity Prediction Scheme (TIPS), and is similar to one used operationally at the National Hurricane Center. However, TIPS contains two major differences: it is developed for the western North Pacific Ocean, and utilizes digitized satellite data. It is shown that the satellite data can distinguish between fast and slow developing tropical cyclones. The importance of other statistical predictors (such as SSTs, wind shear, persistence, and climatology) to intensity change are also clarified. The statistics reveal threshold values useful to forecasters. It is shown that TIPS is competitive with the Joint Typhoon Warning Center.DOD-USAF-OSR: F49620-93-1-0415

Repeatable semantic reef-mapping through photogrammetry and label-augmentation

In an endeavor to study natural systems at multiple spatial and taxonomic resolutions, there is an urgent need for automated, high-throughput frameworks that can handle plethora of information. The coalescence of remote-sensing, computer-vision, and deep-learning elicits a new era in ecological research. However, in complex systems, such as marine-benthic habitats, key ecological processes still remain enigmatic due to the lack of cross-scale automated approaches (mms to kms) for community structure analysis. We address this gap by working towards scalable and comprehensive photogrammetric surveys, tackling the profound challenges of full semantic segmentation and 3D grid definition. Full semantic segmentation (where every pixel is classified) is extremely labour-intensive and difficult to achieve using manual labeling. We propose using label-augmentation, i.e., propagation of sparse manual labels, to accelerate the task of full segmentation of photomosaics. Photomosaics are synthetic images generated from a projected point-of-view of a 3D model. In the lack of navigation sensors (e.g., a diver-held camera), it is difficult to repeatably determine the slope-angle of a 3D map. We show this is especially important in complex topographical settings, prevalent in coral-reefs. Specifically, we evaluate our approach on benthic habitats, in three different environments in the challenging underwater domain. Our approach for label-augmentation shows human-level accuracy in full segmentation of photomosaics using labeling as sparse as 0.1%, evaluated on several ecological measures. Moreover, we found that grid definition using a leveler improves the consistency in community-metrics obtained due to occlusions and topology (angle and distance between objects), and that we were able to standardise the 3D transformation with two percent error in size measurements. By significantly easing the annotation process for full segmentation and standardizing the 3D grid definition we present a semantic mapping methodology enabling change-detection, which is practical, swift, and cost-effective. Our workflow enables repeatable surveys without permanent markers and specialized mapping gear, useful for research and monitoring, and our code is available online. Additionally, we release the Benthos data-set, fully manually labeled photomosaics from three oceanic environments with over 4500 segmented objects useful for research in computer-vision and marine ecology

Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)

The implicit objective of the biennial "international - Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST) is to foster collaboration between international scientific teams by disseminating ideas through both specific oral/poster presentations and free discussions. For its second edition, the iTWIST workshop took place in the medieval and picturesque town of Namur in Belgium, from Wednesday August 27th till Friday August 29th, 2014. The workshop was conveniently located in "The Arsenal" building within walking distance of both hotels and town center. iTWIST'14 has gathered about 70 international participants and has featured 9 invited talks, 10 oral presentations, and 14 posters on the following themes, all related to the theory, application and generalization of the "sparsity paradigm": Sparsity-driven data sensing and processing; Union of low dimensional subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph sensing/processing; Blind inverse problems and dictionary learning; Sparsity and computational neuroscience; Information theory, geometry and randomness; Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?; Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website: http://sites.google.com/site/itwist1