Mesh simplification is an important task in Computer Graphics due to the ever increasing complexity of polygonal geometric models. Specifically in real-time rendering, there is a necessity that these models, which can be acquired through 3D scanning or through artistic conception, have to be simplified or optimized to be rendered on today's hardware while losing as little detail as possible. This thesis proposes a mesh simplification algorithm that works by identifying and simplifying features. Then it simplifies the remaining mesh with the simplified features frozen. The algorithm is called Quadric Error with Feature Curves (QEFC). Quadric Error with Feature Curves works as a tool that allows the user to interactively select a percentage of the most important points of the feature curves to be preserved along with the points determined by the Quadric Error Metric algorithm.

Successful characterization of vortex structures in unsteady flow fields depend crucially upon an adequate choice of a reference frame. Vortex detection approaches in flow visualization aspire to be objective, i.e., invariant under time-dependent rotations and translations of the input reference frame. However, objectivity by itself does not guarantee good results as different specific approaches lead to different results. Moreover, recent more generic approaches to objectivity still require parameters to be specified beforehand which can significantly influence the resulting vortex detection, depending on the complexity and characteristics of the input flow field. With the assumption that human intervention is unavoidable to some extent, we tackle the problem of specifying parameters for vortex detection from a human-centered perspective. In this work, we present a novel system that enables users to interactively explore the parameter space of a flexible objective method, while jointly computing and visualizing the resulting vortex structures. We build on the computation of an objective field of reference frames and enable users to interactively change computation parameters as well as choose different observers, compute vortex structures on-the-fly during exploration, and visualize the flow field from the viewpoint of the chosen observers. Overall, we illustrate that such an interactive approach can be of significant value to the user for analyzing vortex structures visually and understanding why a computational method has detected a specific structure as a vortex.

Mass spectrometry plays a significant role in many proteomics experiments owing to its ability to provide high quality, detailed data on complex samples containing proteins and/or their constituent peptides. As with any technology, the capabilities of mass spectrometers are constantly increasing to provide better resolution, faster data acquisition, and more accurate mass measurements. However, the existence and widespread use of previous-generation instruments is not negligible. While these instruments may not have the capabilities of their modern counterparts they are still able to collect useful experimental data, though their limitations can result in trade-offs between certain parameters such as resolution, sample run-time, and tandem MS experiments. This work describes an alternative method of MS data analysis, dubbed Parallel Isotopic Tag Screening (PITS), which seeks to enable higher throughput and the collection of better quality data on such previous generation instruments.

This research focuses on the development of a persistent navigation algorithm for a hovering vehicle with a single, downward-facing visible spectrum camera. A successful persistent navigation algorithm allows a vehicle to: * Continuously estimate its location and pose within a local, if not global, coordinate frame. * Continuously align incoming data to both temporally proximal and temporally distant data. For aerial images, this alignment is equivalent to image mosaicking, as is commonly used in aerial photogrammetry to produce broad-scale photomaps from a sequence of discrete images. * Operate relative to, and be commanded relative to the sensor data, rather than relative to an abstract coordinate system. The core application space considered here is moderate-to-high altitude aerial mapping, and a number of sets of high-resolution, high-overlap aerial photographs are used as the core test data set. These images are captured from a sufficient altitude that the apparent perspective shift of objects on the ground is minimized -- the scene is effectively planar. As such, this research focuses heavily on the properties and advantages available when processing such planar images. This research is split into two threads which track the two main challenges in visual persistent navigation: the association and alignment of visual data given significant image change, and the development of an estimation algorithm and data storage structure with bounded computational and storage costs for a fixed map size. Persistent navigation requires the robot to accurately align incoming images against historical data. By its nature, however, visual data contains a high degree of variability despite minimal changes in the scene itself. As a simple example, as the sun moves and weather conditions change, the apparent illumination and shading of objects in the scene can vary significantly. More critically, image alignment must be robust to change in the scene itself, as that change is often a critical output from the robot's re-exploration. This problem is considered in two contexts. First, a set of state-of-the-art feature detection algorithms are evaluated against sample data sets which include both temporally proximal and disparate images of the same location. The capacity of each algorithm to identify repeated point features is measured for a spectrum of algorithm-specific parameter values. Next, the potential of using a prior estimate on the inter-image geometry to improve the robustness of precise image alignment is considered for two phases of the image alignment process: feature matching and robust outlier rejection. A number of geometry-aware algorithms are proposed for both phases, and tested against similar sets of similar and disparate aerial images. While many of the proposed algorithms do improve on the performance of the unguided algorithms, none are vastly superior. The second thread starts by considering the problem of navigation fromdownward-looking aerial images from the perspective of Simultaneous Localization and Mapping (SLAM). This leads to the development of Simultaneous Mosaicking and Resectioning Through Planar Image Graphs (SMARTPIG), an online, iterative mosaicking and SLAM algorithm built on the assumption of a planar scene. A number of samples of SMARTPIG outputs are shown, including mosaics of a 600-meter square airport with approximately 3-meter reprojection errors relative to ground control points. SMARTPIG, like most SLAM algorithms, does not fulfill the criteria for persistent navigation because the computational and storage costs are proportional to the total mission length, not the total area explored. SMARTPIG is evolved towards persistent navigation by the introduction of the featurescape, a storage structure for long-term point-feature data, to produce Planar Image Graphs for PErsistent Navigation (PIGPEN). PIGPEN is demonstrated perfoming robot re-localization onto an existing SMARTPIG mosaic with an accuracy comparable to the original mosaic.

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In Experiment 1, the degree of belief in a focal hypothesis was manipulated using priming as well as the principle of unpacking of Tversky and Koehler (1994). The effects of these manipulations on feature detection was measured. It was found that regardless of the degree of belief in the focal hypothesis, novice diagnosticians who have it in mind will call more of its features than those who do not have it in mind. It is believed that this is due to the fact that having a diagnosis in mind seems to focus the attention of diagnosticians to the relevant features. Also, our manipulation of suggesting alternatives to the diagnosticians did not have the effect of decreasing the diagnosticians' belief in the focal hypothesis, contrary to what is predicted by Tversky and Koehler's unpacking principle (1994). The results from Experiment 1 suggest, and those from Experiment 2 confirm the hypothesis that in order to decrease the degree of belief in the focal hypothesis when it is presented with alternatives, the alternatives must be plausible. If the focal hypothesis is extremely dominant over the alternatives, a reversal of the unpacking principle will occur. / Thesis / Master of Science (MS)

In this thesis, I explore region detection and consider its impact on image matching for exemplar-based object recognition. Detecting regions is important to provide semantically meaningful spatial cues in images. Matching establishes similarity between visual entities, which is crucial for recognition. My thesis starts by detecting regions in both local and object level. Then, I leverage geometric cues of the detected regions to improve image matching for the ultimate goal of object recognition. More specifically, my thesis considers four key questions: 1) how can we extract distinctively-shaped local regions that also ensure repeatability for robust matching? 2) how can object-level shape inform bottom-up image segmentation? 3) how should the spatial layout imposed by segmented regions influence image matching for exemplar-based recognition? and 4) how can we exploit regions to improve the accuracy and speed of dense image matching? I propose novel algorithms to tackle these issues, addressing region-based visual perception from low-level local region extraction, to mid-level object segmentation, to high-level region-based matching and recognition. First, I propose a Boundary Preserving Local Region (BPLR) detector to extract local shapes. My approach defines a novel spanning-tree based image representation whose structure reflects shape cues combined from multiple segmentations, which in turn provide multiple initial hypotheses of the object boundaries. Unlike traditional local region detectors that rely on local cues like color and texture, BPLRs explicitly exploit the segmentation that encodes global object shape. Thus, they respect object boundaries more robustly and reduce noisy regions that straddle object boundaries. The resulting detector yields a dense set of local regions that are both distinctive in shape as well as repeatable for robust matching. Second, building on the strength of the BPLR regions, I develop an approach for object-level segmentation. The key insight of the approach is that objects shapes are (at least partially) shared among different object categories--for example, among different animals, among different vehicles, or even among seemingly different objects. This shape sharing phenomenon allows us to use partial shape matching via BPLR-detected regions to predict global object shape of possibly unfamiliar objects in new images. Unlike existing top-down methods, my approach requires no category-specific knowledge on the object to be segmented. In addition, because it relies on exemplar-based matching to generate shape hypotheses, my approach overcomes the viewpoint sensitivity of existing methods by allowing shape exemplars to span arbitrary poses and classes. For the ultimate goal of region-based recognition, not only is it important to detect good regions, but we must also be able to match them reliably. A matching establishes similarity between visual entities (images, objects or scenes), which is fundamental for visual recognition. Thus, in the third major component of this thesis, I explore how to leverage geometric cues of the segmented regions for accurate image matching. To this end, I propose a segmentation-guided local feature matching strategy, in which segmentation suggests spatial layout among the matched local features within each region. To encode such spatial structures, I devise a string representation whose 1D nature enables efficient computation to enforce geometric constraints. The method is applied for exemplar-based object classification to demonstrate the impact of my segmentation-driven matching approach. Finally, building on the idea of regions for geometric regularization in image matching, I consider how a hierarchy of nested image regions can be used to constrain dense image feature matches at multiple scales simultaneously. Moving beyond individual regions, the last part of my thesis studies how to exploit regions' inherent hierarchical structure to improve the image matching. To this end, I propose a deformable spatial pyramid graphical model for image matching. The proposed model considers multiple spatial extents at once--from an entire image to grid cells to every single pixel. The proposed pyramid model strikes a balance between robust regularization by larger spatial supports on the one hand and accurate localization by finer regions on the other. Further, the pyramid model is suitable for fast coarse-to-fine hierarchical optimization. I apply the method to pixel label transfer tasks for semantic image segmentation, improving upon the state-of-the-art in both accuracy and speed. Throughout, I provide extensive evaluations on challenging benchmark datasets, validating the effectiveness of my approach. In contrast to traditional texture-based object recognition, my region-based approach enables to use strong geometric cues such as shape and spatial layout that advance the state-of-the-art of object recognition. Also, I show that regions' inherent hierarchical structure allows fast image matching for scalable recognition. The outcome realizes the promising potential of region-based visual perception. In addition, all my codes for local shape detector, object segmentation, and image matching are publicly available, which I hope will serve as useful new additions for vision researchers' toolbox. / text

Made available in DSpace on 2014-06-11T19:30:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-05-17Bitstream added on 2014-06-13T18:40:48Z : No. of bitstreams: 1 statella_t_dr_prud.pdf: 3750237 bytes, checksum: 5e7d05a021f74eef1040300825e464e5 (MD5) / Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Dust Devils são vórtices convectivos formados por correntes de ar quente instáveis, próximas à superfície planetária. Inúmeros pesquisadores têm estudado dust devils marcianos na tentativa de melhor entender o fenômeno. Em geral, as áreas de pesquisa compreendem a simulação numérica e mecânica de dust devils em laboratório, metodologias para reconhecimento de vórtices por robôs pousados na superfície de Marte e a detecção de vórtices e rastros em imagens orbitais. A despeito do grande número de artigos relacionados ao assunto, nenhum deles aborda a detecção automática de rastros de dust devils, tarefa que ganha especial importância quando a quantidade de imagens da superfície de Marte cresce a uma taxa maior que a capacidade humana de analisá-las em um curto período de tempo. Esta Tese descreve um método inédito para detecção automática de rastros de dust devils. O banco de imagens utilizado contém 200 imagens (90 MOC e 110 HiRISE), distribuídas pelas regiões Aeolis, Noachis, Argyre, Eridania e Hellas. O método é fortemente baseado na Morfologia Matemática e usa transformações como abertura e fechamento por área morfológicos, fechamento por caminho morfológico, método de Otsu... / Dust devils are vortices caused by unstable wind convection processes near the planetary surfaces, due to solar heat. Many researchers have being studying Martian dust devils in an attempt to better understand the phenomena. Generally, the research fields comprise mechanic and numerical simulation of dust devils in laboratories, methodologies for recognition of dust devils plumes from rovers on Mars surface, detection of plumes and tracks from orbital images. Despite the number of papers regarding the subject, none of them addresses the automatic detection of dust devil tracks which is an important issue as the amount of images taken grows at a rate greater than the human capability to analyze them. This Thesis describes a novel method to detect Martian dust devil tracks automatically. The dataset comprises 200 images (90 MOC and 110 HiRISE), distributed over the regions of Aeolis, Noachis, Argyre, Eridania and Hellas. The method is strongly based on Mathematical Morphology and uses transformations such as morphological surface area closing and opening, morphological path closing and Otsu's method for automatic image binarization, among others. The method was applied to the dataset and results were compared... (Complete abstract click electronic access below)

Orientador: Erivaldo Antônio da Silva / Coorientador: Pedro Miguel Berardo Duarte Pina / Banca: Ana Lucia Bezerra Candeias / Banca: João Rodrigues Tavares Júnior / Banca: José Roberto Nogueira / Banca: Maurício Araújo Dias / Resumo: Dust Devils são vórtices convectivos formados por correntes de ar quente instáveis, próximas à superfície planetária. Inúmeros pesquisadores têm estudado dust devils marcianos na tentativa de melhor entender o fenômeno. Em geral, as áreas de pesquisa compreendem a simulação numérica e mecânica de dust devils em laboratório, metodologias para reconhecimento de vórtices por robôs pousados na superfície de Marte e a detecção de vórtices e rastros em imagens orbitais. A despeito do grande número de artigos relacionados ao assunto, nenhum deles aborda a detecção automática de rastros de dust devils, tarefa que ganha especial importância quando a quantidade de imagens da superfície de Marte cresce a uma taxa maior que a capacidade humana de analisá-las em um curto período de tempo. Esta Tese descreve um método inédito para detecção automática de rastros de dust devils. O banco de imagens utilizado contém 200 imagens (90 MOC e 110 HiRISE), distribuídas pelas regiões Aeolis, Noachis, Argyre, Eridania e Hellas. O método é fortemente baseado na Morfologia Matemática e usa transformações como abertura e fechamento por área morfológicos, fechamento por caminho morfológico, método de Otsu... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: Dust devils are vortices caused by unstable wind convection processes near the planetary surfaces, due to solar heat. Many researchers have being studying Martian dust devils in an attempt to better understand the phenomena. Generally, the research fields comprise mechanic and numerical simulation of dust devils in laboratories, methodologies for recognition of dust devils plumes from rovers on Mars surface, detection of plumes and tracks from orbital images. Despite the number of papers regarding the subject, none of them addresses the automatic detection of dust devil tracks which is an important issue as the amount of images taken grows at a rate greater than the human capability to analyze them. This Thesis describes a novel method to detect Martian dust devil tracks automatically. The dataset comprises 200 images (90 MOC and 110 HiRISE), distributed over the regions of Aeolis, Noachis, Argyre, Eridania and Hellas. The method is strongly based on Mathematical Morphology and uses transformations such as morphological surface area closing and opening, morphological path closing and Otsu's method for automatic image binarization, among others. The method was applied to the dataset and results were compared... (Complete abstract click electronic access below) / Doutor

Underwater localization is a difficult task when it comes to making the system autonomous due to the unpredictable environment. The fact that radio signals such as GPS cannot be transmitted through water makes autonomous movement and localization underwater even more challenging. One specific method that is widely used for autonomous underwater navigation applications is Simultaneous Localization and Mapping (SLAM), a technique in which a map is created and updated while localizing the vehicle within the map. In SLAM, feature detection is used in landmark extraction and data association by examining each pixel and differentiating landmarks pixels from those of the background. Previous research on the performance of different feature detection methods have been done in environments such as cisterns and caverns where the effects of the ocean are reduced. Our objective, however, is to achieves robust localization in the open ocean environment of the Cal Poly pier. This thesis performs a comparative study between different feature detection methods including Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Oriented FAST and Rotated BRIEF (ORB) on different sensors. We evaluate the feature detection and matching performance of these algorithms in a simulated open-ocean environment.