A neural network-based scheme coupled with the RPV model inversion package

A hybrid scheme for the inversion of the Rahman-Pinty-Verstraete (RPV) model is presented. It combines the inversion technique described by Lavergne et al. (Lavergne, T., Kaminski, T., Pinty, B., Taberner, M., Gobron, N., Verstraete, M.M., Vossbeck, M., Widlowski, J.L., Giering, R. (2007). Application to MISR land products of an RPV model inversion package using adjoint and Hessian codes. Remote Sensing of Environment, 107, 362-375.) and a multilayer backpropagation feedforward neural network. The RPV inversion package is applied to a sample set of pixels within the satellite scene. Subsequently the pairs of bidirectional reflectance factors (BRF) and model parameters estimated from the sample set of pixels are used to train the neural network. Since the mathematical formulation of the RPV model is embedded in these training data variables, the neural network can efficiently retrieve the model parameters for the whole satellite scene. This scheme has been tested for a MISR L2 BRF scene, MISR L1B2-derived BRF data corresponding to two different dates and a mosaic of MISR L2 BRF scenes acquired over Southern Africa covering a large extent of Miombo woodland. The results show this strategy retrieves the RPV model parameters and uncertainties with high accuracy and considerable speed over large areas.     

The vulnerability of structures to unforeseen events

A structure is vulnerable to an unforeseen event if it is not sufficiently robust. One insight into the lack of robustness is gained through the vulnerability of a structure to disproportionate collapse. In this paper, the importance of assessing the vulnerability of a structure to unforeseen events is highlighted and the nature of unforeseen events examined. A theory of structural vulnerability which examines the form of the structure to determine the most vulnerable sequence of failure events is briefly described. The potential for damage to propagate through a structure is examined through a new measure of hazard potential and vertical pushover analysis. An analysis of an example structure shows that such an approach is able to capture the weaknesses in a structure either due to form or external actions.     

Mesh-Mon: A multi-radio mesh monitoring and management system

Mesh networks are a potential solution for providing communication infrastructure in an emergency. They can be rapidly deployed by first responders in the wake of a major disaster to augment an existing wireless or wired network. We imagine a mesh node with multiple radios embedded in each emergency vehicle arriving at the site to form the backbone of a mobile wireless mesh. The ability of such a mesh network to monitor itself, diagnose faults and anticipate problems are essential features for its sustainable operation. Typical SNMP-based centralized solutions introduce a single point of failure and are unsuitable for managing such a network. Mesh-Mon is a decentralized monitoring and management system designed for such a mobile, rapidly deployed, unplanned mesh network and works independently of the underlying mesh routing protocol. Mesh-Mon nodes are designed to actively cooperate and use localized algorithms to predict, detect, diagnose and resolve network problems in a scalable manner. Mesh-Mon is independent of the underlying routing protocol and can operate even if the mesh routing protocol completely fails. One novel aspect of our approach is that we employ mobile users of the mesh, running software called Mesh-Mon-Ami, to ferry management packets between physically-disconnected partitions in a delay-tolerant-network manner. The main contributions of this paper are the design, implementation and evaluation of a comprehensive monitoring and management architecture that helps a network administrator proactively identify, diagnose and resolve a range of issues that can occur in a dynamic mesh network. In experiments on Dart-Mesh, our 16-node indoor mesh testbed, we found Mesh-Mon to be effective in quickly diagnosing and resolving a variety of problems with high accuracy, without adding significant management overhead.     

Maximum-likelihood registration of range images with missing data

Missing data are common in range images, due to geometric occlusions, limitations in the sensor field of view, poor reflectivity, depth discontinuities, and cast shadows. Using registration to align these data often fails, because points without valid correspondences can be incorrectly matched. This paper presents a maximum likelihood method for registration of scenes with unmatched or missing data. Using ray casting, correspondences are formed between valid and missing points in each view. These correspondences are used to classify points by their visibility properties, including occlusions, field of view, and shadow regions. The likelihood of each point match is then determined using statistical properties of the sensor, such as noise and outlier distributions. Experiments demonstrate a high rates of convergence on complex scenes with varying degrees of overlap.     

Free-form object reconstruction from silhouettes, occluding edges and texture edges: a unified and robust operator based on duality

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, i.e., each tangent plane of the primal surface is represented as a four-dimensional vector which constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, i.e., the dual of the dual goes back to the primal. This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data, by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences.     

Geometric rectification of camera-captured document images

Compared to typical scanners, handheld cameras offer convenient, flexible, portable, and non-contact image capture, which enables many new applications and breathes new life into existing ones. However, camera-captured documents may suffer from distortions caused by non-planar document shape and perspective projection, which lead to failure of current OCR technologies. We present a geometric rectification framework for restoring the frontal-flat view of a document from a single camera-captured image. Our approach estimates 3D document shape from texture flow information obtained directly from the image without requiring additional 3D/metric data or prior camera calibration. Our framework provides a unified solution for both planar and curved documents and can be applied in many, especially mobile, camera-based document analysis applications. Experiments show that our method produces results that are significantly more OCR compatible than the original images.     

Optimal randomized RANSAC

A randomized model verification strategy for RANSAC is presented. The proposed method finds, like RANSAC, a solution that is optimal with user-specified probability. The solution is found in time that is (i) close to the shortest possible and (ii) superior to any deterministic verification strategy. A provably fastest model verification strategy is designed for the (theoretical) situation when the contamination of data by outliers is known. In this case, the algorithm is the fastest possible (on average) of all randomized \\RANSAC algorithms guaranteeing a confidence in the solution. The derivation of the optimality property is based on Wald's theory of sequential decision making, in particular a modified sequential probability ratio test (SPRT). Next, the R-RANSAC with SPRT algorithm is introduced. The algorithm removes the requirement for a priori knowledge of the fraction of outliers and estimates the quantity online. We show experimentally that on standard test data the method has performance close to the theoretically optimal and is 2 to 10 times faster than standard RANSAC and is up to 4 times faster than previously published methods.     

Out-of-sample extrapolation of learned manifolds

We investigate the problem of extrapolating the embedding of a manifold learned from finite samples to novel out-of-sample data. We concentrate on the manifold learning method called Maximum Variance Unfolding (MVU) for which the extrapolation problem is still largely unsolved. Taking the perspective of MVU learning being equivalent to Kernel PCA, our problem reduces to extending a kernel matrix generated from an unknown kernel function to novel points. Leveraging on previous developments, we propose a novel solution which involves approximating the kernel eigenfunction using Gaussian basis functions. We also show how the width of the Gaussian can be tuned to achieve extrapolation. Experimental results which demonstrate the effectiveness of the proposed approach are also included.