Synchronization of user-generated videos through trajectory correspondence and a refinement procedure

Temporal synchronization of multiple video recordings of the same dynamic event is a critical task in many computer vision applications e.g. novel view synthesis and 3D reconstruction. Typically this information is implied through the time-stamp information embedded in the video streams. User-generated videos shot using consumer grade equipment do not contain this information; hence, there is a need to temporally synchronize signals using the visual information itself. Previous work in this area has either assumed good quality data with relatively simple dynamic content or the availability of precise camera geometry. Our first contribution is a synchronization technique which tries to establish correspondence between feature trajectories across views in a novel way, and specifically targets the kind of complex content found in consumer generated sports recordings, without assuming precise knowledge of fundamental matrices or homographies. We evaluate performance using a number of real video recordings and show that our method is able to synchronize to within 1 sec, which is significantly better than previous approaches. Our second contribution is a robust and unsupervised view-invariant activity recognition descriptor that exploits recurrence plot theory on spatial tiles. The descriptor is individually shown to better characterize the activities from different views under occlusions than state-of-the-art approaches. We combine this descriptor with our proposed synchronization method and show that it can further refine the synchronization index. © 2013 ACM.

A preliminary study on methods for retaining data quality problems in automatically generated test data

Data in an organisation often contains business secrets that organisations do not want to release. However, there are occasions when it is necessary for an organisation to release its data such as when outsourcing work or using the cloud for Data Quality (DQ) related tasks like data cleansing. Currently, there is no mechanism that allows organisations to release their data for DQ tasks while ensuring that it is suitably protected from releasing business related secrets. The aim of this paper is therefore to present our current progress on determining which methods are able to modify secret data and retain DQ problems. So far we have identified the ways in which data swapping and the SHA-2 hash function alterations methods can be used to preserve missing data, incorrectly formatted values, and domain violations DQ problems while minimising the risk of disclosing secrets. © (2012) by the AIS/ICIS Administrative Office All rights reserved.

Reusable, robust, and accurate laser-generated photonic nanosensor.

Developing noninvasive and accurate diagnostics that are easily manufactured, robust, and reusable will provide monitoring of high-risk individuals in any clinical or point-of-care environment. We have developed a clinically relevant optical glucose nanosensor that can be reused at least 400 times without a compromise in accuracy. The use of a single 6 ns laser (λ = 532 nm, 200 mJ) pulse rapidly produced off-axis Bragg diffraction gratings consisting of ordered silver nanoparticles embedded within a phenylboronic acid-functionalized hydrogel. This sensor exhibited reversible large wavelength shifts and diffracted the spectrum of narrow-band light over the wavelength range λpeak ≈ 510-1100 nm. The experimental sensitivity of the sensor permits diagnosis of glucosuria in the urine samples of diabetic patients with an improved performance compared to commercial high-throughput urinalysis devices. The sensor response was achieved within 5 min, reset to baseline in ∼10 s. It is anticipated that this sensing platform will have implications for the development of reusable, equipment-free colorimetric point-of-care diagnostic devices for diabetes screening.