968 resultados para 3D local shape descriptor
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Automatic segmentation and tracking of the coronary artery tree from Cardiac Multislice-CT images is an important goal to improve the diagnosis and treatment of coronary artery disease. This paper presents a semi-automatic algorithm (one input point per vessel) based on morphological grayscale local reconstructions in 3D images devoted to the extraction of the coronary artery tree. The algorithm has been evaluated in the framework of the Coronary Artery Tracking Challenge 2008 [1], obtaining consistent results in overlapping measurements (a mean of 70% of the vessel well tracked). Poor results in accuracy measurements suggest that future work should refine the centerline extraction. The algorithm can be efficiently implemented and its general strategy can be easily extrapolated to a completely automated centerline extraction or to a user interactive vessel extraction
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Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.
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The aim of this work is to develop an automated tool for the optimization of turbomachinery blades founded on an evolutionary strategy. This optimization scheme will serve to deal with supersonic blades cascades for application to Organic Rankine Cycle (ORC) turbines. The blade geometry is defined using parameterization techniques based on B-Splines curves, that allow to have a local control of the shape. The location in space of the control points of the B-Spline curve define the design variables of the optimization problem. In the present work, the performance of the blade shape is assessed by means of fully-turbulent flow simulations performed with a CFD package, in which a look-up table method is applied to ensure an accurate thermodynamic treatment. The solver is set along with the optimization tool to determine the optimal shape of the blade. As only blade-to-blade effects are of interest in this study, quasi-3D calculations are performed, and a single-objective evolutionary strategy is applied to the optimization. As a result, a non-intrusive tool, with no need for gradients definition, is developed. The computational cost is reduced by the use of surrogate models. A Gaussian interpolation scheme (Kriging model) is applied for the estimated n-dimensional function, and a surrogate-based local optimization strategy is proved to yield an accurate way for optimization. In particular, the present optimization scheme has been applied to the re-design of a supersonic stator cascade of an axial-flow turbine. In this design exercise very strong shock waves are generated in the rear blade suction side and shock-boundary layer interaction mechanisms occur. A significant efficiency improvement as a consequence of a more uniform flow at the blade outlet section of the stator is achieved. This is also expected to provide beneficial effects on the design of a subsequent downstream rotor. The method provides an improvement to gradient-based methods and an optimized blade geometry is easily achieved using the genetic algorithm.
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Index to the treaties of the United States, p. 287-320.
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Luminance changes within a scene are ambiguous; they can indicate reflectance changes, shadows, or shading due to surface undulations. How does vision distinguish between these possibilities? When a surface painted with an albedo texture is shaded, the change in local mean luminance (LM) is accompanied by a similar modulation of the local luminance amplitude (AM) of the texture. This relationship does not necessarily hold for reflectance changes or for shading of a relief texture. Here we concentrate on the role of AM in shape-from-shading. Observers were presented with a noise texture onto which sinusoidal LM and AM signals were superimposed, and were asked to indicate which of two marked locations was closer to them. Shape-from-shading was enhanced when LM and AM co-varied (in-phase), and was disrupted when they were out-of-phase. The perceptual differences between cue types (in-phase vs out-of-phase) were enhanced when the two cues were present at different orientations within a single image. Similar results were found with a haptic matching task. We conclude that vision can use AM to disambiguate luminance changes. LM and AM have a positive relationship for rendered, undulating, albedo textures, and we assess the degree to which this relationship holds in natural images. [Supported by EPSRC grants to AJS and MAG].
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The pattern of illumination on an undulating surface can be used to infer its 3-D form (shape-from-shading). But the recovery of shape would be invalid if the luminance changes actually arose from changes in reflectance. So how does vision distinguish variation in illumination from variation in reflectance to avoid illusory depth? When a corrugated surface is painted with an albedo texture, the variation in local mean luminance (LM) due to shading is accompanied by a similar modulation in local luminance amplitude (AM). This is not so for reflectance variation, nor for roughly textured surfaces. We used depth mapping and paired comparison methods to show that modulations of local luminance amplitude play a role in the interpretation of shape-from-shading. The shape-from-shading percept was enhanced when LM and AM co-varied (in-phase) and was disrupted when they were out of phase or (to a lesser degree) when AM was absent. The perceptual differences between cue types (in-phase vs out-of-phase) were enhanced when the two cues were present at different orientations within a single image. Our results suggest that when LM and AM co-vary (in-phase) this indicates that the source of variation is illumination (caused by undulations of the surface), rather than surface reflectance. Hence, the congruence of LM and AM is a cue that supports a shape-from-shading interpretation. © 2006 Elsevier Ltd. All rights reserved.
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We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.
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An array of FBG curvature sensors are wavelength-interrogated and the recovered data combined with a three-dimensional algorithm to reconstruct in real time the enveloped object with a 1% to 9% volumetric error. © 2012 OSA.
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The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.
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The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.
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In questa tesi sono stati analizzati alcuni metodi di ricerca per dati 3D. Viene illustrata una panoramica generale sul campo della Computer Vision, sullo stato dell’arte dei sensori per l’acquisizione e su alcuni dei formati utilizzati per la descrizione di dati 3D. In seguito è stato fatto un approfondimento sulla 3D Object Recognition dove, oltre ad essere descritto l’intero processo di matching tra Local Features, è stata fatta una focalizzazione sulla fase di detection dei punti salienti. In particolare è stato analizzato un Learned Keypoint detector, basato su tecniche di apprendimento di machine learning. Quest ultimo viene illustrato con l’implementazione di due algoritmi di ricerca di vicini: uno esauriente (K-d tree) e uno approssimato (Radial Search). Sono state riportate infine alcune valutazioni sperimentali in termini di efficienza e velocità del detector implementato con diversi metodi di ricerca, mostrando l’effettivo miglioramento di performance senza una considerabile perdita di accuratezza con la ricerca approssimata.
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This is an abstract of an invited talk presented at AVA Animal Vision Meeting / Camocon 2015, which took place in Liverpool, 23 August 2015.
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In French Polynesia, the aquaculture of P. margaritifera is carried out in numerous grow-out sites, located over three archipelagos (Gambier, Society and Tuamotu). To evaluate the impact of macro-geographical effects of these growing sites on pearl quality traits, five hatcheries produced families were used as homogeneous donor oysters in an experimental graft. The molluscs were then reared in two commercial locations: Tahaa island (Society) and Rangiroa atoll (Tuamotu). At harvest, eight pearl quality traits were recorded and compared: surface defects, lustre, grade, circles, shape categories, darkness level, body and secondary colour and visual colour categories. Overall inter-site comparison revealed that: 1) all traits were affected by grow-out location except for lustre and round shape, and 2) a higher mean rate of valuable pearls was produced in Rangiroa. Indeed, for pearl grade, Rangiroa showed twice as many A-B and less reject samples than Tahaa. This was related to the number of surface defects (grade component): in Rangiroa, twice as many pearls had no defects and less pearls had up to 10 defects. Concerning pearl shape, more circled and baroque pearls were found in Tahaa (+10%). For colour variation, 10% more pearls have an attractive green overtone in Rangiroa than in Tahaa, where more grey bodycolor were harvested. Lustre does not seem to be affected by these two culture site (except at a family scale). This is the first time P. margaritifera donor family have been shown to vary in the quality of pearls they produce depending on their grow-out location.
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There has been considerable interest in developing shape-changing soft materials for potential applications in drug delivery, microfluidics and biosensing. These shape- changing materials are inspired by the morphological changes exhibited by plants in nature, such as the Venus flytrap. One specific class of shape-change is that from a flat sheet to a folded structure (e.g., a tube). Such “self-folding” materials are usually composed of polymer hydrogels, and these typically fold in response to external stimuli such as pH and temperature. In order to develop these hydrogels for the previously described applications, it is necessary to expand the range of triggers. The focus of this dissertation is the advancement of shape-changing polymer hydrogels that are sensitive to uncommon cues such as specific biomolecules (enzymes), the substrates for such enzymes, or specific multivalent cations. First, we describe a hybrid gel that responds to the presence of low concentrations of a class of enzymes known as matrix metalloproteinases (MMPs). The hybrid gel was created by utilizing photolithographic techniques to combine two or more gels with distinct chemical composition into the same material. Certain portions of the hybrid gel are composed of a biopolymer derivative with crosslinkable groups. The hybrid gel is flat in water; however, in the presence of MMPs, the regions containing the biopolymer are degraded and the flat sheet folds to form a 3D structure. We demonstrate that hydrogels with different patterns can transform into different 3D structures such as tubes, helices and pancakes. Furthermore, this shape change can be made to occur at physiological concentrations of enzymes. Next, we report a gel with two layers that undergoes a shape change in the presence of glucose. The enzyme glucose oxidase (GOx) is immobilized in one of the layers. GOx catalyzes the conversion of glucose to gluconic acid. The production of gluconic acid decreases the local pH. The decrease in local pH causes one of the layers to swell. As a result, the flat sheet folds to form a tube. The tube unfolds to form a flat sheet when it is transferred to a solution with no glucose present. Therefore, this biomolecule- triggered shape transformation is reversible, meaning the glucose sensing gel is reusable. Furthermore, this shape change only occurs in the presence of glucose and it does not occur in the presence of other small sugars such as fructose. In our final study, we report the shape change of a gel with two layers in the presence of multivalent ions such as Ca2+ and Sr2+. The gel consists of a passive layer and an active layer. The passive layer is composed of dimethylyacrylamide (DMAA), which does not interact with multivalent ions. The active layer consists of DMAA and the biopolymer alginate. In the presence of Ca2+ ions, the alginate chains crosslink and the active layer shrinks. As a result, the gel converts from a flat sheet to a folded tube. What is particularly unusual is the direction of folding. In most cases, when flat rectangular gels fold, they do so about their short-side. However, our gels typically fold about their long-side. We hypothesize that non-homogeneous swelling determines the folding axis.