ChESS - Quick and robust detection of chess-board features

Localization of chess-board vertices is a common task in computer vision, underpinning many applications, but relatively little work focusses on designing a specific feature detector that is fast, accurate and robust. In this paper the 'Chess-board Extraction by Subtraction and Summation' (ChESS) feature detector, designed to exclusively respond to chess-board vertices, is presented. The method proposed is robust against noise, poor lighting and poor contrast, requires no prior knowledge of the extent of the chess-board pattern, is computationally very efficient, and provides a strength measure of detected features. Such a detector has significant application both in the key field of camera calibration, as well as in structured light 3D reconstruction. Evidence is presented showing its superior robustness, accuracy, and efficiency in comparison to other commonly used detectors, including Harris & Stephens and SUSAN, both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects. © 2013 Elsevier Inc. All rights reserved.

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.

Robust recognition of chess-boards under deformation

Current methods for formation of detected chess-board vertices into a grid structure tend to be weak in situations with a warped grid, and false and missing vertex-features. In this paper we present a highly robust, yet efficient, scheme suitable for inference of regular 2D square mesh structure from vertices recorded both during projection of a chess-board pattern onto 3D objects, and in the more simple case of camera calibration. Examples of the method's performance in a lung function measuring application, observing chess-boards projected on to patients' chests, are given. The method presented is resilient to significant surface deformation, and tolerates inexact vertex-feature detection. This robustness results from the scheme's novel exploitation of feature orientation information. © 2013 IEEE.

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.