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 robustness, accuracy, and efficiency in comparison to other commonly used detectors both under simulation and in experimental 3D reconstruction of flat plate and cylindrical objects

Ballasted & electrically steerable carbon nanotube field emitters

Here we present our on-going efforts toward the development of stable ballasted carbon nanotube-based field emitters employing hydrothermally synthesized zinc oxide nanowires and thin film silicon-on-insulator substrates. The semiconducting channel in each controllably limits the emission current thereby preventing detrimental burn-out of individual emitters that occurs due to unavoidable statistical variability in emitter characteristics, particularly in their length. Fabrication details and emitter characterization are discussed in addition to their field emission performance. The development of a beam steerable triode electron emitter formed from hexagonal carbon nanotube arrays with central focusing nanotube electrodes, is also described. Numerical ab-initio simulations are presented to account for the empirical emission characteristics. Our engineered ballasted emitters have shown some of the lowest reported lifetime variations (< 0.7%) with on-times of < 1 ms, making them ideally-suited for next-generation displays, environmental lighting and portable x-rays sources. © 2012 SPIE.

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.

An expressive text-driven 3D talking head

Creating a realistic talking head, which given an arbitrary text as input generates a realistic looking face speaking the text, has been a long standing research challenge. Talking heads which cannot express emotion have been made to look very realistic by using concatenative approaches [Wang et al. 2011], however allowing the head to express emotion creates a much more challenging problem and model based approaches have shown promise in this area. While 2D talking heads currently look more realistic than their 3D counterparts, they are limited both in the range of poses they can express and in the lighting conditions that they can be rendered under. Previous attempts to produce videorealistic 3D expressive talking heads [Cao et al. 2005] have produced encouraging results but not yet achieved the level of realism of their 2D counterparts.

Novel nanostructured carbon nanotube electron sources

In this chapter, we present a review of our continuing efforts toward the development of discrete, low-dimensional nanostructured carbon-based electron emitters. Carbon nanotubes and nanofibers, herein referred to simply as CNTs, are one-dimensional carbon allotropes formed from cylindrically rolled and nested graphene sheets, have diameters between 1 and 500 nm and lengths of up to several millimeters, and are perfect candidates for field emission (FE) applications. By virtue of their extremely strong sp2 C-C bonding, intrinsic to the graphene hexagonal lattice, CNTs have demonstrated impressive chemical inertness, unprecedented thermal stabilities, significant resistance to electromigration, and exceptionally high axial current carrying capacities, even at elevated temperatures. These near ideal cold cathode electron emitters have incredibly high electric field enhancing aspect ratios combined with virtual point sources of the order of a few nanometers in size. The correct integration and judicious development of suitable FE platforms based on these extraordinary molecules is critical and will ultimately enable enhanced technologies. This chapter will review some of the more recent platforms, devices and structures developed by our group, as well as our contributions towards the development of industry-scalable technologies for ultra-high-resolution electron microscopy, portable x-ray sources, and flexible environmental lighting technologies. © 2012 by Pan Stanford Publishing Pte. Ltd. All rights reserved.

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.

Switch mode constant current LED Driver with high efficiency, high precision and wide dimming ratio

A high efficiency hard switching constant current LED driver is presented with high overall efficiency, high current precision, high LED efficacy, flicker-free and wide constant current dimming ratio. The high stable lighting source provides the best solution for office light, reading light and LCD backlight. © 2013 IEEE.

Obtaining the shape of a moving object with a specular surface

This paper addresses the basic problem of recovering the 3D surface of an object that is observed in motion by a single camera and under a static but unknown lighting condition. We propose a method to establish pixelwise correspondence between input images by way of depth search by investigating optimal subsets of intensities rather than employing all the relevant pixel values. The thrust of our algorithm is that it is capable of dealing with specularities which appear on the top of shading variance that is caused due to object motion. This is in terms of both stages of finding sparse point correspondence and dense depth search. We also propose that a linearised image basis can be directly computed by the procudure of finding the correspondence. We illustrate the performance of the theoretical propositions using images of real objects. © 2009. The copyright of this document resides with its authors.