A Statistical Image-Based Shape Model for Visual Hull Reconstruction and 3D Structure Inference

We present a statistical image-based shape + structure model for Bayesian visual hull reconstruction and 3D structure inference. The 3D shape of a class of objects is represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes are then estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We show how the use of a class-specific prior in a visual hull reconstruction can reduce the effect of segmentation errors from the silhouette extraction process. The proposed method is applied to a data set of pedestrian images, and improvements in the approximate 3D models under various noise conditions are shown. We further augment the shape model to incorporate structural features of interest; unknown structural parameters for a novel set of contours are then inferred via the Bayesian reconstruction process. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a data set of thousands of pedestrian images generated from a synthetic model, we can accurately infer the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

Active Learning with Statistical Models

For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate.

Sequential Optimal Recovery: A Paradigm for Active Learning

In most classical frameworks for learning from examples, it is assumed that examples are randomly drawn and presented to the learner. In this paper, we consider the possibility of a more active learner who is allowed to choose his/her own examples. Our investigations are carried out in a function approximation setting. In particular, using arguments from optimal recovery (Micchelli and Rivlin, 1976), we develop an adaptive sampling strategy (equivalent to adaptive approximation) for arbitrary approximation schemes. We provide a general formulation of the problem and show how it can be regarded as sequential optimal recovery. We demonstrate the application of this general formulation to two special cases of functions on the real line 1) monotonically increasing functions and 2) functions with bounded derivative. An extensive investigation of the sample complexity of approximating these functions is conducted yielding both theoretical and empirical results on test functions. Our theoretical results (stated insPAC-style), along with the simulations demonstrate the superiority of our active scheme over both passive learning as well as classical optimal recovery. The analysis of active function approximation is conducted in a worst-case setting, in contrast with other Bayesian paradigms obtained from optimal design (Mackay, 1992).

A Formulation for Active Learning with Applications to Object Detection

We discuss a formulation for active example selection for function learning problems. This formulation is obtained by adapting Fedorov's optimal experiment design to the learning problem. We specifically show how to analytically derive example selection algorithms for certain well defined function classes. We then explore the behavior and sample complexity of such active learning algorithms. Finally, we view object detection as a special case of function learning and show how our formulation reduces to a useful heuristic to choose examples to reduce the generalization error.

On Degeneracy of Linear Reconstruction from Three Views: Linear Line Complex and Applications

This paper investigates the linear degeneracies of projective structure estimation from point and line features across three views. We show that the rank of the linear system of equations for recovering the trilinear tensor of three views reduces to 23 (instead of 26) in the case when the scene is a Linear Line Complex (set of lines in space intersecting at a common line) and is 21 when the scene is planar. The LLC situation is only linearly degenerate, and we show that one can obtain a unique solution when the admissibility constraints of the tensor are accounted for. The line configuration described by an LLC, rather than being some obscure case, is in fact quite typical. It includes, as a particular example, the case of a camera moving down a hallway in an office environment or down an urban street. Furthermore, an LLC situation may occur as an artifact such as in direct estimation from spatio-temporal derivatives of image brightness. Therefore, an investigation into degeneracies and their remedy is important also in practice.

Sparse Correlation Kernel Analysis and Reconstruction

This paper presents a new paradigm for signal reconstruction and superresolution, Correlation Kernel Analysis (CKA), that is based on the selection of a sparse set of bases from a large dictionary of class- specific basis functions. The basis functions that we use are the correlation functions of the class of signals we are analyzing. To choose the appropriate features from this large dictionary, we use Support Vector Machine (SVM) regression and compare this to traditional Principal Component Analysis (PCA) for the tasks of signal reconstruction, superresolution, and compression. The testbed we use in this paper is a set of images of pedestrians. This paper also presents results of experiments in which we use a dictionary of multiscale basis functions and then use Basis Pursuit De-Noising to obtain a sparse, multiscale approximation of a signal. The results are analyzed and we conclude that 1) when used with a sparse representation technique, the correlation function is an effective kernel for image reconstruction and superresolution, 2) for image compression, PCA and SVM have different tradeoffs, depending on the particular metric that is used to evaluate the results, 3) in sparse representation techniques, L_1 is not a good proxy for the true measure of sparsity, L_0, and 4) the L_epsilon norm may be a better error metric for image reconstruction and compression than the L_2 norm, though the exact psychophysical metric should take into account high order structure in images.

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

A new optimised De Bruijn coding strategy for structured light patterns

Coded structured light is an optical technique based on active stereovision that obtains the shape of objects. One shot techniques are based on projecting a unique light pattern with an LCD projector so that grabbing an image with a camera, a large number of correspondences can be obtained. Then, a 3D reconstruction of the illuminated object can be recovered by means of triangulation. The most used strategy to encode one-shot patterns is based on De Bruijn sequences. In This work a new way to design patterns using this type of sequences is presented. The new coding strategy minimises the number of required colours and maximises both the resolution and the accuracy

Overview of coded light projection techniques for automatic 3D profiling

Obtaining automatic 3D profile of objects is one of the most important issues in computer vision. With this information, a large number of applications become feasible: from visual inspection of industrial parts to 3D reconstruction of the environment for mobile robots. In order to achieve 3D data, range finders can be used. Coded structured light approach is one of the most widely used techniques to retrieve 3D information of an unknown surface. An overview of the existing techniques as well as a new classification of patterns for structured light sensors is presented. This kind of systems belong to the group of active triangulation method, which are based on projecting a light pattern and imaging the illuminated scene from one or more points of view. Since the patterns are coded, correspondences between points of the image(s) and points of the projected pattern can be easily found. Once correspondences are found, a classical triangulation strategy between camera(s) and projector device leads to the reconstruction of the surface. Advantages and constraints of the different patterns are discussed

Active control of structures with uncertain coupled subsystems and actuator dynamics

This paper deals with the problem of stabilizing a class of structures subject to an uncertain excitation due to the temporary coupling of the main system with another uncertain dynamical subsystem. A Lyapunov function based control scheme is proposed to attenuate the structural vibration. In the control design, the actuator dynamics is taken into account. The control scheme is implemented by using only feedback information of the main system. The effectiveness of the control scheme is shown for a bridge platform with crossing vehicle

Active citizenship

The aim of this activity is to allow students to explore the nature of political action, which can be thought of as a form of active as opposed to passive citizenship. By learning about and reflecting upon past instances of political action, or activism, students will be able to start thinking about what is likely to make a campaign successful. It is intended that these reflections can then be applied to their own actions as active citizens. It is hoped that the historical case studies combined with the information provided on different campaigning tools and methods will help to make students feel empowered and inspired to take action. In setting students the task of planning an action, it is expected that time management and organizational skills will be improved. It is believed that by putting themselves in the shoes of activists and going through the process of planning an action, they will have an engaged learning experience. The reflective element of the activity encourages students to form and defend opinions on the relative strengths and weaknesses of different campaigning methods, and on the acceptable limits to political action. This learning activity has been designed presuming no prior knowledge of activism or its methods, and has been successfully used with first year undergraduate students from a variety of disciplines. However, the activity provides a basis for more in-depth study of several issues, or alternatively study into further examples of campaign organizations. There are 3 different learning activities presented on this web site. For a dynamic and well-illustrated introduction to contemporary activism, see Jordan, T. (2002) Activism!: Direct Action, Hacktivism and the Future of Society, London: Reaktion Books Ltd. This material is also available via JORUM.

Efecto de la hipoxia-reoxigenación y las radiaciones ionizantes en la captación de glucosa en líneas tumorales de seno y colon cocultivadas con células endoteliales.

La captación de glucosa y su conversión en lactato juega un papel fundamental en el metabolismo tumoral, independientemente de la concentración de oxígeno presente en el tejido (efecto Warburg). Sin embrago, dicha captación varía de un tipo tumoral a otro, y dentro del mismo tumor, situación que podría depender de las características microambientales tumorales (fluctuaciones de oxígeno, presencia de otros tipos celulares) y de factores estresores asociados a los tratamientos. Se estudió el efecto de la hipoxia-reoxigenación (HR) y las radiaciones ionizantes (RI) sobre la captación de glucosa, en cultivos de líneas tumorales MCF-7 y HT-29, cultivadas de forma aislada o en cocultivo con la línea celular EAhy296. Se encontró que la captación de glucosa en HR es diferente para lo descrito en condiciones de hipoxia permanente y que es modificada en el cocultivo. Se identificaron poblaciones celulares dentro de la misma línea celular, de alta y baja captación de glucosa, lo que implicaría una simbiosis metabólica de la célula como respuesta adaptativa a las condiciones tumorales. Se evaluó la expresión de NRF2 y la translocación nuclear de NRF2 y HIF1a, como vías de respuesta a estrés celular e hipoxia. La translocación nuclear de las proteínas evaluadas explicaría el comportamiento metabólico de las células tumorales de seno, pero no de colon, por lo cual deben existir otras vías metabólicas implicadas. Las diferencias en el comportamiento de las células tumorales en HR en relación con hipoxia permitirá realizar planeaciones dosimétricas más dinámicas, que reevalúen las condiciones de oxigenación tumoral constantemente.

Asociación de desórdenes músculo esqueléticos en región cervical, dorsal y lumbar y factores de riesgo psicosocial en conductores de vehículos de carga en una empresa de transporte terrestre en Bogotá, Colombia.

Este estudio buscó evaluar la asociación de desórdenes músculo esqueléticos en región cervical, dorsal y lumbar identificados mediante el Cuestionario Nórdico en su versión validada al español y los factores de riesgo psicosocial con el Cuestionario del contenido del trabajo (JCQ), en conductores de vehículos de carga de una empresa de transporte terrestre en Bogotá, Colombia; fue un estudio de corte transversal con la participación voluntaria de 125 conductores. Los resultados demostraron mayor prevalencia de trastornos músculo esqueléticos en región lumbar en los últimos 12 meses en el 36% de los participantes y en los últimos tres meses en región cervical con el 17.6%; la prevalencia de factores psicosociales arrojó trabajo de baja tensión en el 29.6%, trabajo activo 26.4%, trabajo con alta tensión 23.2% y trabajo pasivo con el 20.8%. El valor p fue mayor de 0,005 no hallándose asociaciones significativas de desórdenes músculo esqueléticos en región cervical, dorsal y lumbar con factores de riesgo psicosocial.