Selecting surface features for accurate multi-camera surface reconstruction

This paper proposes a novel feature detector for selecting local textures that are suitable for accurate multi-camera surface reconstruction, and in particular planar patch fitting techniques. This approach is in contrast to conventional feature detectors, which focus on repeatability under scale and affine transformations rather than suitability for multi-camera reconstruction techniques. The proposed detector selects local textures that are sensitive to affine transformations, which is a fundamental requirement for accurate patch fitting. The proposed detector is evaluated against the SIFT detector on a synthetic dataset and the fitted patches are compared against ground truth. The experiments show that patches originating from the proposed detector are fitted more accurately to the visible surfaces than those originating from SIFT keypoints. In addition, the detector is evaluated on a performance capture studio dataset to show the real-world application of the proposed detector

Multi-frame scene-flow estimation using a patch model and smooth motion prior

This paper addresses the problem of estimating the dense 3D motion of a scene over several frames using a set of calibrated cameras. Most current 3D motion estimation techniques are limited to estimating the motion over a single frame, unless a strong prior model of the scene (such as a skeleton) is introduced. Estimating the 3D motion of a general scene is difficult due to untextured surfaces, complex movements and occlusions. In this paper, we show that it is possible to track the surfaces of a scene over several frames, by introducing an effective prior on the scene motion. Experimental results show that the proposed method estimates the dense scene-flow over multiple frames, without the need for multiple-view reconstructions at every frame. Furthermore, the accuracy of the proposed method is demonstrated by comparing the estimated motion against a ground truth

OBTAINING MODELS FOR ALFALFA, SORGHUM, AND WHEAT RESIDUE DECOMPOSITION

Crop residues provide an economical means for controlling wind and water erosion in addition to being a valuable source of plant nutrients. As residues decompose they lose nutrients, mass and the ability to protect the soil surface from erosive forces. The research was designed to evaluate rates of residue decomposition of sorghum, wheat and alfalfa on the soil surface and buried, in five soil moisture regimes. Moisture was applied to soil by line source irrigation and bags containing crop residues were retrieved and analyzed across time. Thus, observations were repeated in both space and time . Wieder and Lang (1982) reported that mass-loss over time was modeled well by the negative exponential. Because residue can be divided into fast (labile) and slow (recalcitrant) decomposing fractions, the double exponential is suggested. Assuming the ratio of labile to recalcitrant is constant for. a crop regardless of soil moisture, and whether on the surface or buried, it would be sufficient for each crop to fit a set of simultaneous non-linear functions with three parameters, a constant A (proportion labile) over all equations with different k1,\u27s (labile fraction decomposition rates) and k 2\u27s (recalcitrant decomposition rates) for soil moisture levels and whether buried or unburied . For alfalfa the results were consistent with the above theory. For wheat and sorghum data holding A constant over all environments resulted in k\u27 s \u3e 0. Convergence of the estimations process could not be obtained when forcing k\u27s ≤ 0. The single exponential provided a satisfactory model of decomposition, but without the advantage of separating the residues into labile and recalcitrant fractions. The inability to obtain estimates using the double exponential apparently resulted from an insufficient observation period. The recalcitrant fraction of the surface residues of these crops had not disappeared after more than a year

A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions

Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal ∼12° and ∼15° mean error in pitch and yaw respectively via ASM landmark features

Neutrino transport in accretion disks

We test approximate approaches to solving a neutrino transport problem that presents itself in the analysis of some accretion-disk models. Approximation #1 consists of replacing the full, angular- dependent, distribution function by a two-stream simulation, where the streams are respectively outwardly and inwardly directed, with angles $\cos \theta=\pm 1/\sqrt{3}$ to the vertical. In this approximation the full energy dependence of the distribution function is retained, as are the energy and temperature dependences of the scattering rates. Approximation #2, used in recent works on the subject, replaces the distribution function by an intensity function and the scattering rates by temperature-energy-averaged quantities. We compare the approximations to the results of solving the full Boltzmann equation. Under some interesting conditions, approximation #1 passes the test; approximation #2 does not. We utilize the results of our analysis to construct a toy model of a disc at a temperature and density such that relativistic particles are more abundant than nucleons, and dominate both the opacity and pressure. The nucleons will still provide most of the energy density. In the toy model we take the rate of heat generation (which drives the radiative transfer problem) to be proportional to the nucleon density. The model allows the simultaneous solution of the neutrino transport and hydrostatic equilibrium problems in a disk in which the nucleon density decreases approximately linearly as one moves from the median plane of the disk upwards, reaching zero on the upper boundary.Comment: 8 pages, 5 figures Parentheses added in eqs. 10-1

Magnetic Stress at the Marginally Stable Orbit: Altered Disk Structure, Radiation, and Black Hole Spin Evolution

Magnetic connections to the plunging region can exert stresses on the inner edge of an accretion disk around a black hole. We recompute the relativistic corrections to the thin-disk dynamics equations when these stresses take the form of a time-steady torque on the inner edge of the disk. The additional dissipation associated with these stresses is concentrated relatively close outside the marginally stable orbit, scaling as r to the -7/2 at large radius. As a result of these additional stresses: spin-up of the central black hole is retarded; the maximum spin-equilibrium accretion efficiency is 36%, and occurs at a/M=0.94; the disk spectrum is extended toward higher frequencies; line profiles (such as Fe K-alpha) are broadened if the line emissivity scales with local flux; limb-brightening, especially at the higher frequencies, is enhanced; and the returning radiation fraction is substantially increased, up to 58%. This last effect creates possible explanations for both synchronized continuum fluctuations in AGN, and polarization rises shortward of the Lyman edge in quasars. We show that no matter what additional stresses occur, when a/M < 0.36, the second law of black hole dynamics sets an absolute upper bound on the accretion efficiency.Comment: 11 pages, 15 figures, accepted for publication in the Astrophysical Journa

Driver and Passenger Identification from Smartphone Data

The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics and Electronics (CAPE) agreement

How does money influence health?

Why do people in poverty tend to have poorer health? This study looks at hundreds of theories to consider how income influences health. There is a graded association between money and health ? increased income equates to better health. But the reasons are debated. Researchers have reviewed theories from 272 wide-ranging papers, most of which examined the complex interactions between people?s income and their health throughout their lives. Key points This research identifies four main ways money affects people?s wellbeing: Material: Money buys goods and services that improve health. The more money families have, the better the goods they can buy. Psychosocial: Managing on a low income is stressful. Comparing oneself to others and feeling at the bottom of the social ladder can be distressing, which can lead to biochemical changes in the body, eventually causing ill health. Behavioural: For various reasons, people on low incomes are more likely to adopt unhealthy behaviours ? smoking and drinking, for example ? while those on higher incomes are more able to afford healthier lifestyles. Reverse causation (poor health leads to low income): Health may affect income by preventing people from taking paid employment. Childhood health may also affect educational outcomes, limiting job opportunities and potential earnings. The research is part of our programme of work on poverty in the UK

Warwick-JLR driver monitoring dataset (DMD) : statistics and early findings

Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced

Data mining for vehicle telemetry

This article presents a data mining methodology for driving-condition monitoring via CAN-bus data that is based on the general data mining process. The approach is applicable to many driving condition problems, and the example of road type classification without the use of location information is investigated. Location information from Global Positioning Satellites and related map data are often not available (for business reasons), or cannot represent the full dynamics of road conditions. In this work, Controller Area Network (CAN)-bus signals are used instead as inputs to models produced by machine learning algorithms. Road type classification is formulated as two related labeling problems: Road Type (A, B, C, and Motorway) and Carriageway Type (Single or Dual). An investigation is presented into preprocessing steps required prior to applying machine learning algorithms, that is, signal selection, feature extraction, and feature selection. The selection methods used include principal components analysis (PCA) and mutual information (MI), which are used to determine the relevance and redundancy of extracted features and are performed in various combinations. Finally, because there is an inherent bias toward certain road and carriageway labelings, the issue of class imbalance in classification is explained and investigated. A system is produced, which is demonstrated to successfully ascertain road type from CAN-bus data, and it is shown that the classification correlates well with input signals such as vehicle speed, steering wheel angle, and suspension height