Comparing Feature Detectors: A bias in the repeatability criteria, and how to correct it

Most computer vision application rely on algorithms finding local correspondences between different images. These algorithms detect and compare stable local invariant descriptors centered at scale-invariant keypoints. Because of the importance of the problem, new keypoint detectors and descriptors are constantly being proposed, each one claiming to perform better (or to be complementary) to the preceding ones. This raises the question of a fair comparison between very diverse methods. This evaluation has been mainly based on a repeatability criterion of the keypoints under a series of image perturbations (blur, illumination, noise, rotations, homotheties, homographies, etc). In this paper, we argue that the classic repeatability criterion is biased towards algorithms producing redundant overlapped detections. To compensate this bias, we propose a variant of the repeatability rate taking into account the descriptors overlap. We apply this variant to revisit the popular benchmark by Mikolajczyk et al., on classic and new feature detectors. Experimental evidence shows that the hierarchy of these feature detectors is severely disrupted by the amended comparator.Comment: Fixed typo in affiliation

Dust Measurements in the Outer Solar System

Dust measurements in the outer solar system are reviewed. Only the plasma wave instrument on board Voyagers 1 and 2 recorded impacts in the Edgeworth-Kuiper belt (EKB). Pioneers 10 and 11 measured a constant dust flux of 10-micron-sized particles out to 20 AU. Dust detectors on board Ulysses and Galileo uniquely identified micron-sized interstellar grains passing through the planetary system. Impacts of interstellar dust grains onto big EKB objects generate at least about a ton per second of micron-sized secondaries that are dispersed by Poynting-Robertson effect and Lorentz force. We conclude that impacts of interstellar particles are also responsible for the loss of dust grains at the inner edge of the EKB. While new dust measurements in the EKB are in an early planning stage, several missions (Cassini and STARDUST) are en route to analyze interstellar dust in much more detail.Comment: 10 pages, 5 figures, Proceedings of the ESO workshop on ``Minor bodies in the outer solar system'

CMB Telescopes and Optical Systems

The cosmic microwave background radiation (CMB) is now firmly established as a fundamental and essential probe of the geometry, constituents, and birth of the Universe. The CMB is a potent observable because it can be measured with precision and accuracy. Just as importantly, theoretical models of the Universe can predict the characteristics of the CMB to high accuracy, and those predictions can be directly compared to observations. There are multiple aspects associated with making a precise measurement. In this review, we focus on optical components for the instrumentation used to measure the CMB polarization and temperature anisotropy. We begin with an overview of general considerations for CMB observations and discuss common concepts used in the community. We next consider a variety of alternatives available for a designer of a CMB telescope. Our discussion is guided by the ground and balloon-based instruments that have been implemented over the years. In the same vein, we compare the arc-minute resolution Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT). CMB interferometers are presented briefly. We conclude with a comparison of the four CMB satellites, Relikt, COBE, WMAP, and Planck, to demonstrate a remarkable evolution in design, sensitivity, resolution, and complexity over the past thirty years.Comment: To appear in: Planets, Stars and Stellar Systems (PSSS), Volume 1: Telescopes and Instrumentatio

PLASMON EFFECTS IN SOLID-STATE RADIATION DETECTORS

We have examined the role of plasmons on the electron energy response of solid-state (Si and Ge) radiation detectors. We found that at the level of parts per thousand, internal-conversion electron calibration techniques do not suffice to yield an adequate response function. In particular, spectral distortions in the detection of low-energy beta-particles have been found which are not accounted for by the usual calibration methods. Thus, a small but significant error can arise from energy loss to low-energy plasmons in Si and Ge detectors. The proximity of the plasmon energy to the end-point singularity and the quadratic form of the beta-decay spectrum may account for the effect interpreted as a 17 keV neutrino. Similar errors can also arise in other subtle solid-state measurements as, for example, in the X-ray edge absorption and emission spectra of metals and semiconductors

Neural-Attention-Based Deep Learning Architectures for Modeling Traffic Dynamics on Lane Graphs

Deep neural networks can be powerful tools, but require careful application-specific design to ensure that the most informative relationships in the data are learnable. In this paper, we apply deep neural networks to the nonlinear spatiotemporal physics problem of vehicle traffic dynamics. We consider problems of estimating macroscopic quantities (e.g., the queue at an intersection) at a lane level. First-principles modeling at the lane scale has been a challenge due to complexities in modeling social behaviors like lane changes, and those behaviors' resultant macro-scale effects. Following domain knowledge that upstream/downstream lanes and neighboring lanes affect each others' traffic flows in distinct ways, we apply a form of neural attention that allows the neural network layers to aggregate information from different lanes in different manners. Using a microscopic traffic simulator as a testbed, we obtain results showing that an attentional neural network model can use information from nearby lanes to improve predictions, and, that explicitly encoding the lane-to-lane relationship types significantly improves performance. We also demonstrate the transfer of our learned neural network to a more complex road network, discuss how its performance degradation may be attributable to new traffic behaviors induced by increased topological complexity, and motivate learning dynamics models from many road network topologies.Comment: To appear at 2019 IEEE Conference on Intelligent Transportation System