Robust computational intelligence techniques for visual information processing

The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, ₚ-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

Multi-scale active shape description in medical imaging

Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed

Proper shape representation of single figure and multi-figure anatomical objects

Extracting anatomic objects from medical images is an important process in various medical applications. This extraction, called image segmentation, is often realized by deformable models. Among deformable model methods, medial deformable models have the unique advantage of representing not only the object boundary surfaces but also the object interior volume. Based on one medial deformable model called the m-rep, the main goal of this dissertation is to provide proper shape representations of simple anatomical objects of one part and complex anatomical objects of multiple parts in a population. This dissertation focuses on several challenges in the existing medially based deformable model method: 1. how to derive a proper continuous form by interpolating a discrete medial shape representation; 2. how to represent complex objects with several parts and do statistical analysis on them; 3. how to avoid local shape defects, such as folding or creasing, in shapes represented by the deformable model. The proposed methods in this dissertation address these challenges in more detail: 1. An interpolation method for a discrete medial shape model is proposed to guarantee the legality of the interpolated shape. This method is based on the integration of medial shape operators. 2. A medially based representation with hierarchy is proposed to represent complex objects with multiple parts by explicitly modeling interrelations between object parts and modeling smooth transitions between each pair of connected parts. A hierarchical statistical analysis is also proposed for these complex objects. 3. A method to fit a medial model to binary images is proposed to use an explicit legality penalty derived from the medial shape operators. Probability distributions learned from the fitted shape models by the proposed fitting method have proven to yield better image segmentation results

The Cosmic Microwave Background and Particle Physics

In forthcoming years, connections between cosmology and particle physics will be made increasingly important with the advent of a new generation of cosmic microwave background (CMB) experiments. Here, we review a number of these links. Our primary focus is on new CMB tests of inflation. We explain how the inflationary predictions for the geometry of the Universe and primordial density perturbations will be tested by CMB temperature fluctuations, and how the gravitational waves predicted by inflation can be pursued with the CMB polarization. The CMB signatures of topological defects and primordial magnetic fields from cosmological phase transitions are also discussed. Furthermore, we review current and future CMB constraints on various types of dark matter (e.g. massive neutrinos, weakly interacting massive particles, axions, vacuum energy), decaying particles, the baryon asymmetry of the Universe, ultra-high-energy cosmic rays, exotic cosmological topologies, and other new physics.Comment: 43 pages. To appear in Annual Reviews of Nuclear and Particle Scienc

Detecting, Tracking, And Recognizing Activities In Aerial Video

In this dissertation, we address the problem of detecting humans and vehicles, tracking them in crowded scenes, and finally determining their activities in aerial video. Even though this is a well explored problem in the field of computer vision, many challenges still remain when one is presented with realistic data. These challenges include large camera motion, strong scene parallax, fast object motion, large object density, strong shadows, and insufficiently large action datasets. Therefore, we propose a number of novel methods based on exploiting scene constraints from the imagery itself to aid in the detection and tracking of objects. We show, via experiments on several datasets, that superior performance is achieved with the use of proposed constraints. First, we tackle the problem of detecting moving, as well as stationary, objects in scenes that contain parallax and shadows. We do this on both regular aerial video, as well as the new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data, and low frame-rate of video. We propose a method for detecting moving and stationary objects that overcomes these challenges, and evaluate it on CLIF and VIVID datasets. In order to find moving objects, we use median background modelling which requires few frames to obtain a workable model, and is very robust when there is a large number of moving objects in the scene while the model is being constructed. We then iii remove false detections from parallax and registration errors using gradient information from the background image. Relying merely on motion to detect objects in aerial video may not be sufficient to provide complete information about the observed scene. First of all, objects that are permanently stationary may be of interest as well, for example to determine how long a particular vehicle has been parked at a certain location. Secondly, moving vehicles that are being tracked through the scene may sometimes stop and remain stationary at traffic lights and railroad crossings. These prolonged periods of non-motion make it very difficult for the tracker to maintain the identities of the vehicles. Therefore, there is a clear need for a method that can detect stationary pedestrians and vehicles in UAV imagery. This is a challenging problem due to small number of pixels on the target, which makes it difficult to distinguish objects from background clutter, and results in a much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of the ground plane normal, the orientation of the shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We utilize the above information in a geometry-based shadow and ground plane normal blob detector, which provides an initial estimation for the locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Additionally, we combine regular SCOOP and inverted SCOOP candidates to obtain vehicle candidates. We show impressive results on sequences from VIVID and CLIF datasets, and provide comparative quantitative and qualitative analysis. We also show that we can extend the SCOOP detection method to automatically estimate the iv orientation of the shadow in the image without relying on metadata. This is useful in cases where metadata is either unavailable or erroneous. Simply detecting objects in every frame does not provide sufficient understanding of the nature of their existence in the scene. It may be necessary to know how the objects have travelled through the scene over time and which areas they have visited. Hence, there is a need to maintain the identities of the objects across different time instances. The task of object tracking can be very challenging in videos that have low frame rate, high density, and a very large number of objects, as is the case in the WAAS data. Therefore, we propose a novel method for tracking a large number of densely moving objects in an aerial video. In order to keep the complexity of the tracking problem manageable when dealing with a large number of objects, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link the tracks across the cells. Besides tractability, grid cells also allow us to define a set of local scene constraints, such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem; This allows us to track fast-moving objects in low frame rate videos. In addition to moving through the scene, the humans that are present may be performing individual actions that should be detected and recognized by the system. A number of different approaches exist for action recognition in both aerial and ground level video. One of the requirements for the majority of these approaches is the existence of a sizeable dataset of examples of a particular action from which a model of the action can be constructed. Such a luxury is not always possible in aerial scenarios since it may be difficult to fly a large number of missions to observe a particular event multiple times. Therefore, we propose a method for v recognizing human actions in aerial video from as few examples as possible (a single example in the extreme case). We use the bag of words action representation and a 1vsAll multi-class classification framework. We assume that most of the classes have many examples, and construct Support Vector Machine models for each class. Then, we use Support Vector Machines that were trained for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples, via late weighted fusion of decision values

Improving Cardiovascular Stent Design Using Patient-Specific Models and Shape Optimization

Stent geometry influences local hemodynamic alterations (i.e. the forces moving blood through the cardiovascular system) associated with adverse clinical outcomes. Computational fluid dynamics (CFD) is frequently used to quantify stent-induced hemodynamic disturbances, but previous CFD studies have relied on simplified device or vascular representations. Additionally, efforts to minimize stent-induced hemodynamic disturbances using CFD models often only compare a small number of possible stent geometries. This thesis describes methods for modeling commercial stents in patient-specific vessels along with computational techniques for determining optimal stent geometries that address the limitations of previous studies. An efficient and robust method was developed for virtually implanting stent models into patient-specific vascular geometries derived from medical imaging data. Models of commercial stent designs were parameterized to allow easy control over design features. Stent models were then virtually implanted into vessel geometries using a series of Boolean operations. This approach allowed stented vessel models to be automatically regenerated for rapid analysis of the contribution of design features to resulting hemodynamic alterations. The applicability of the method was demonstrated with patient-specific models of a stented coronary artery bifurcation and basilar trunk aneurysm to reveal how it can be used to investigate differences in hemodynamic performance in complex vascular beds for a variety of clinical scenarios. To identify hemodynamically optimal stents designs, a computational framework was constructed to couple CFD with a derivative-free optimization algorithm. The optimization algorithm was fully-automated such that solid model construction, mesh generation, CFD simulation and time-averaged wall shear stress (TAWSS) quantification did not require user intervention. The method was applied to determine the optimal number of circumferentially repeating stent cells (NC) for a slotted-tube stents and various commercial stents. Optimal stent designs were defined as those minimizing the area of low TAWSS. It was determined the optimal value of NC is dependent on the intrastrut angle with respect to the primary flow direction. Additionally, the geometries of current commercial stents were found to generally incorporate a greater NC than is hemodynamically optimal. The application of the virtual stent implantation and optimization methods may lead to stents with superior hemodynamic performance and the potential for improved clinical outcomes. Future in vivo studies are needed to validate the findings of the computational results obtained from the methods developed in this thesis

The Antarctic Impulsive Transient Antenna Ultra-high Energy Neutrino Detector Design, Performance, and Sensitivity for 2006-2007 Balloon Flight

We present a detailed report on the experimental details of the Antarctic Impulsive Transient Antenna (ANITA) long duration balloon payload, including the design philosophy and realization, physics simulations, performance of the instrument during its first Antarctic flight completed in January of 2007, and expectations for the limiting neutrino detection sensitivity. Neutrino physics results will be reported separately.Comment: 50 pages, 49 figures, in preparation for PR

Prototyping Elliptically Profiled Inverted Pendulum Walls in Cross-laminated Timber (CLT) for Passive Self-centering and Seismic Resiliency

Cross-laminated timber (CLT) buildings garnered international attention, nearly a decade ago, for elevating wood construction to new heights on fully panelized assemblies of floors and walls. While highly regarded as a sustainable building material, use of CLT as a structural wall system depends on seismically resilient strategies like controlled rocking. This project prototyped elliptically profiled CLT panels and slotted-pin steel connections, at full-scale, to produce rolling and slip-friction inverted pendulum wall systems of one-story height and inspired by seismic isolation concepts. Digital fabrication realized elliptical profiles along the load-bearing edges of six 5-ply CLT panels and various customized slot shapes for accompanying steel connections. Pins traveling within V-shaped slots intended only to guide rolling as displacement restraints, in contrast with pins constrained within vertical slots that forced panels into slip-friction combinations of rolling and sliding. Six CLT panels and two versions of shear transfer connections yielded a total of 12 full-scale wall prototype configurations for cyclic lateral load-displacement testing that emulated standard quasi-static protocols for seismic isolation. The hysteresis plots generated by the tests confirmed that elliptical eccentricity predictably controlled effective lateral stiffness and displacement capacity, while providing inherent self-centering. When configured to roll using traction along steel bearing surfaces as the primary mechanism of story shear transfer, CLT panels supported simulated gravity loads as high as 400 kN (90 kips) while achieving story drifts commonly exceeding 10 and even 20 percent. When configured to transfer shear primarily through a pin connection, however, CLT panels slid and sustained damage that limited gravity load capacity to 133 kN (30 kips). Connection constraint, therefore, dictated whether friction essentially transferred story shears transfer or dissipated energy. To help explain implications of friction, Digital Image Correlation (DIC), piezoelectric film pressure mapping, Finite Element Analysis, and fundamental free-body diagrams visualized the behavior of high-pressure contact between timber and steel. Despite the low damping exhibited by rolling and increased damage of slip-friction rocking, both models of elliptically profiled rocking walls can develop into viable options for isolation planes within multistory building schemes, based on the results of this study