Calibration by correlation using metric embedding from non-metric similarities

This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?) and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature) as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case, on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional), and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm performs as theoretically predicted for all corner cases of the observability analysis

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

sensor augmented pump and down syndrome a new tool in tricky patients

W e read with great interest the paper by Piccini and cols. (1) published in the July issue of this Journal. Some years ago, we published the first report ever (to the best of our knowledge) of successful treatment of a girl with Down syndrome, Hashimoto's thyroiditis and celiac disease with continuous subcutaneous insulin infusion (2). Since then, her glycemic control was kept constant and, most of the time, in the target range (HbA1c in 2009: 7.75 ± 0.21%; HbA1c in 2010: 7.35 ± 0.19%; HbA1c in 2011: 7.42 ± 0.30%). At the end of 2011, sensor-augmented pump was initiated (Animas® VibeTM, West Chester, PA, USA) because of both a quite high glycemic variability and the parents' request, and her HbA1c kept improving (HbA1c in 2012: 7.30 ± 0.20%; HbA1c in 2013: 7.10 ± 0.28%). CSII has been recognized as effective and safe in pediatric (3) and in adult patients (4), not only in the short run, but even after many years (5). In patients with Down syndrome and type 1 diabetes, glycemic control may sometimes be particularly tricky (6,7). In our patient, as well as in the one of Piccini and cols. (1), CSII was a safe and effective way to manage diabetes. For a successful CSII therapy in a patient with Down syndrome, whose mental function may be impaired, the collaboration of a highly motivated and compliant family is essential, as well as a skilled multidisciplinary diabetes team (8). Given all of this, pump increased the patient's and family's flexibility, as we had previously reported (2). The significant improvement in the glycemic control observed, and the high level of acceptance of CSII therapy observed in both our case and in that of Piccini and cols. is worth the effort of the patient's family and of the diabetes team in ensuring that the patient has a flexible life. Perhaps CSII therapy might be taken into account when considering insulin therapy in patients with Down's syndrome

Necrobiosis Lipoidica Diabeticorum

Necrobiosis lipoidica is a rare disorder that usually appears in the lower extremities and it is often related to diabetes mellitus. There are few reported cases of necrobiosis lipoidica in children. We present an interesting case in that the patient developed lesions on the abdomen, which is an unusual location

Strategic approaches to restoring ecosystems can triple conservation gains and halve costs.

International commitments for ecosystem restoration add up to one-quarter of the world's arable land. Fulfilling them would ease global challenges such as climate change and biodiversity decline but could displace food production and impose financial costs on farmers. Here, we present a restoration prioritization approach capable of revealing these synergies and trade-offs, incorporating ecological and economic efficiencies of scale and modelling specific policy options. Using an actual large-scale restoration target of the Atlantic Forest hotspot, we show that our approach can deliver an eightfold increase in cost-effectiveness for biodiversity conservation compared with a baseline of non-systematic restoration. A compromise solution avoids 26% of the biome's current extinction debt of 2,864 plant and animal species (an increase of 257% compared with the baseline). Moreover, this solution sequesters 1 billion tonnes of CO2-equivalent (a 105% increase) while reducing costs by US$28 billion (a 57% decrease). Seizing similar opportunities elsewhere would offer substantial contributions to some of the greatest challenges for humankind