An automated calibration method for non-see-through head mounted displays

Accurate calibration of a head mounted display (HMD) is essential both for research on the visual system and for realistic interaction with virtual objects. Yet, existing calibration methods are time consuming and depend on human judgements, making them error prone. The methods are also limited to optical see-through HMDs. Building on our existing HMD calibration method [1], we show here how it is possible to calibrate a non-see-through HMD. A camera is placed inside an HMD displaying an image of a regular grid, which is captured by the camera. The HMD is then removed and the camera, which remains fixed in position, is used to capture images of a tracked calibration object in various positions. The locations of image features on the calibration object are then re-expressed in relation to the HMD grid. This allows established camera calibration techniques to be used to recover estimates of the display’s intrinsic parameters (width, height, focal length) and extrinsic parameters (optic centre and orientation of the principal ray). We calibrated a HMD in this manner in both see-through and in non-see-through modes and report the magnitude of the errors between real image features and reprojected features. Our calibration method produces low reprojection errors and involves no error-prone human measurements.

Case study: object and ground point separation on a river bank using airborne based LIDAR data

In the past decade, airborne based LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sectors as a reliable and accurate source for land surveying in environmental, engineering and civil applications. Commonly, the first task to investigate LIDAR point clouds is to separate ground and object points. Skewness Balancing has been proven to be an efficient non-parametric unsupervised classification algorithm to address this challenge. Initially developed for moderate terrain, this algorithm needs to be adapted to handle sloped terrain. This paper addresses the difficulty of object and ground point separation in LIDAR data in hilly terrain. A case study on a diverse LIDAR data set in terms of data provider, resolution and LIDAR echo has been carried out. Several sites in urban and rural areas with man-made structure and vegetation in moderate and hilly terrain have been investigated and three categories have been identified. A deeper investigation on an urban scene with a river bank has been selected to extend the existing algorithm. The results show that an iterative use of Skewness Balancing is suitable for sloped terrain.

Quasi Monte Carlo approach with uniform separation for solving of the rendering equation, ICNAAM 2007

This paper is directed to the advanced parallel Quasi Monte Carlo (QMC) methods for realistic image synthesis. We propose and consider a new QMC approach for solving the rendering equation with uniform separation. First, we apply the symmetry property for uniform separation of the hemispherical integration domain into 24 equal sub-domains of solid angles, subtended by orthogonal spherical triangles with fixed vertices and computable parameters. Uniform separation allows to apply parallel sampling scheme for numerical integration. Finally, we apply the stratified QMC integration method for solving the rendering equation. The superiority our QMC approach is proved.

Oxidation of polycrystalline Ni studied by spectromicroscopy: Phase separation in the early stages of crystallite growth

Low-energy and photoemission electron microscopy enables the determination of facet planes of polycrystalline surfaces and the study of their chemical composition at the sub-m scale. Using these techniques the early oxidation stages of nickel were studied. After exposing the surface to 20 L of oxygen at 373 K a uniform layer of chemisorbed oxygen was found on all facets. After oxygen exposure at 473–673 K, small NiO crystallites are formed on all facets but not in the vicinity of all grain boundaries. The crystallites are separated by areas of bare Ni without significant oxygen coverage.

First-dimension separation with the MicroRotoforTM cell prior to SDS-PAGE and LC-MS/MS analysis

Free-flow isoelectric focusing (IEF) is a gel-free method for separating proteins based on their isoelectric point (pl) in a liquid environment and in the presence of carrier ampholytes. this method has been used with the RotoforTM cell at the preparative scale to fractionate proteins from samples containing several hundred milligrams of protein; see the refeences listed in Bio-Rad bulletin 3152. the MicroRotofor cell applies the same method to much sl=maller protein samples without dilution, separating and recoverng milligram quantities of protein in a total volume of about 2 ml.

Threshold-free object and ground point separation in LIDAR data

Light Detection And Ranging (LIDAR) is an important modality in terrain and land surveying for many environmental, engineering and civil applications. This paper presents the framework for a recently developed unsupervised classification algorithm called Skewness Balancing for object and ground point separation in airborne LIDAR data. The main advantages of the algorithm are threshold-freedom and independence from LIDAR data format and resolution, while preserving object and terrain details. The framework for Skewness Balancing has been built in this contribution with a prediction model in which unknown LIDAR tiles can be categorised as “hilly” or “moderate” terrains. Accuracy assessment of the model is carried out using cross-validation with an overall accuracy of 95%. An extension to the algorithm is developed to address the overclassification issue for hilly terrain. For moderate terrain, the results show that from the classified tiles detached objects (buildings and vegetation) and attached objects (bridges and motorway junctions) are separated from bare earth (ground, roads and yards) which makes Skewness Balancing ideal to be integrated into geographic information system (GIS) software packages.

Ultrafast charge separation in a photoreactive rhenium-appended porphyrin assembly monitored by picosecond transient infrared spectroscopy

Rhenium(bipyridine)(tricarbonyl)(picoline) units have been linked covalently to tetraphenylmetalloporphyrins of magnesium and zinc via an amide bond between the bipyridine and one phenyl substituent of the porphyrin. The resulting complexes, abbreviated as [Re(CO)(3)(Pic)Bpy-MgTPP][OTf] and [Re(CO)(3)(Pic)Bpy-ZnTPP][OTf], exhibit no signs of electronic interaction between the Re(CO)(3)(bpy) units and the metalloporphyrin units in their ground states. However, emission spectroscopy reveals solvent-dependent quenching of porphyrin emission on irradiation into the long-wavelength absorption bands localized on the porphyrin. The characteristics of the excited states have been probed by picosecond time-resolved absorption (TRVIS) spectroscopy and time-resolved infrared (TRIR) spectroscopy in nitrile solvents. The presence of the charge-separated state involving electron transfer from MgTPP or ZnTPP to Re(bpy) is signaled in the TRIR spectra by a low-frequency shift in the nu(CO) bands of the Re(CO)(3) moiety similar to that observed by spectroelectrochemical reduction. Long-wavelength excitation of [Re(CO)(3)(Pic)Bpy-MTPP][OTf] results in characteristic TRVIS spectra of the S-1 state of the porphyrin that decay with a time constant of 17 ps (M = Mg) or 24 ps (M = Zn). The IR bands of the CS state appear on a time scale of less than 1 ps (Mg) or ca. 5 ps (Zn) and decay giving way to a vibrationally excited (i.e., hot) ground state via back electron transfer. The IR bands of the precursors recover with a time constant of 35 ps (Mg) or 55 ps (Zn). The short lifetimes of the charge-transfer states carry implications for the mechanism of reaction in the presence of triethylamine.

Redox control of light-induced charge separation in a transition metal cluster: photochemistry of a methyl viologen-substituted [Os-3(CO)10(a-diimine)] cluster

Sub)picosecond transient absorption (TA) and time-resolved infrared (TRIR) spectra of the cluster [OS3(CO)(10-) (AcPy-MV)](2+) (the clication AcPy-MV = Acpy-MV2+ = [2-pyridylacetimine-N-(2-(1'-methyl-4,4'-bipyridine-1,1'-diium-1-yl) ethyl)] (PF6)(2)) (1(2+)) reveal that photoinduced electron transfer to the electron-accepting 4,4'-bipyridine-1,1'diium (MV2+) moiety competes with the fast relaxation of the initially populated sigmapi* excited state of the cluster to the ground state and/or cleavage of an Os-Os bond. The TA spectra of cluster 12 in acetone, obtained by irradiation into its lowest-energy absorption band, show the characteristic absorptions of the one-electron-reduced MV*(+) unit at 400 and 615 nm, in accordance with population of a charge-separated (CS) state in which a cluster-core electron has been transferred to the lowest pi* orbital of the remote MV2+ unit. This assignment is confirmed by picosecond TRIR spectra that show a large shift of the pilot highest-frequency nu(CO) band of 1(2+) by ca. +40 cm(-1), reflecting the photooxidation of the cluster core. The CS state is populated via fast (4.2 x 10(11) s(-1)) and efficient (88%) oxidative quenching of the optically populated sigmapi* excited state and decays biexponentially with lifetimes of 38 and 166 ps (1:2:1 ratio) with a complete regeneration of the parent cluster. About 12% of the cluster molecules in the sigmapi* excited state form long-lived open-core biradicals. In strongly coordinating acetonitrile, however, the cluster core-to-MV2+ electron transfer in cluster 12+ results in the irreversible formation of secondary photoproducts with a photooxidized cluster core. The photochemical behavior of the [Os-3(CO)(10)(alpha-diimine-MV)](2+) (donor-acceptor) dyad can be controlled by an externally applied electronic bias. Electrochemical one-electron reduction of the MV2+ moiety prior to the irradiation reduces its electron-accepting character to such an extent that the photoinduced electron transfer to MV*+ is no longer feasible. Instead, the irradiation of reduced cluster 1(.)+ results in the reversible formation of an open-core zwitterion, the ultimate photoproduct also observed upon irradiation of related nonsubstituted clusters [Os-3(CO)(10)(alpha-diimine)] in strongly coordinating solvents such as acetonitrile.

Proof of concept of automated mapping of weeds in arable fields

The project investigated whether it would be possible to remove the main technical hindrance to precision application of herbicides to arable crops in the UK, namely creating geo-referenced weed maps for each field. The ultimate goal is an information system so that agronomists and farmers can plan precision weed control and create spraying maps. The project focussed on black-grass in wheat, but research was also carried out on barley and beans and on wild-oats, barren brome, rye-grass, cleavers and thistles which form stable patches in arable fields. Farmers may also make special efforts to control them. Using cameras mounted on farm machinery, the project explored the feasibility of automating the process of mapping black-grass in fields. Geo-referenced images were captured from June to December 2009, using sprayers, a tractor, combine harvesters and on foot. Cameras were mounted on the sprayer boom, on windows or on top of tractor and combine cabs and images were captured with a range of vibration levels and at speeds up to 20 km h-1. For acceptability to farmers, it was important that every image containing black-grass was classified as containing black-grass; false negatives are highly undesirable. The software algorithms recorded no false negatives in sample images analysed to date, although some black-grass heads were unclassified and there were also false positives. The density of black-grass heads per unit area estimated by machine vision increased as a linear function of the actual density with a mean detection rate of 47% of black-grass heads in sample images at T3 within a density range of 13 to 1230 heads m-2. A final part of the project was to create geo-referenced weed maps using software written in previous HGCA-funded projects and two examples show that geo-location by machine vision compares well with manually-mapped weed patches. The consortium therefore demonstrated for the first time the feasibility of using a GPS-linked computer-controlled camera system mounted on farm machinery (tractor, sprayer or combine) to geo-reference black-grass in winter wheat between black-grass head emergence and seed shedding.

Final report on research project PS2010, proof of concept of automated mapping of weeds in arable fields

Many weeds occur in patches but farmers frequently spray whole fields to control the weeds in these patches. Given a geo-referenced weed map, technology exists to confine spraying to these patches. Adoption of patch spraying by arable farmers has, however, been negligible partly due to the difficulty of constructing weed maps. Building on previous DEFRA and HGCA projects, this proposal aims to develop and evaluate a machine vision system to automate the weed mapping process. The project thereby addresses the principal technical stumbling block to widespread adoption of site specific weed management (SSWM). The accuracy of weed identification by machine vision based on a single field survey may be inadequate to create herbicide application maps. We therefore propose to test the hypothesis that sufficiently accurate weed maps can be constructed by integrating information from geo-referenced images captured automatically at different times of the year during normal field activities. Accuracy of identification will also be increased by utilising a priori knowledge of weeds present in fields. To prove this concept, images will be captured from arable fields on two farms and processed offline to identify and map the weeds, focussing especially on black-grass, wild oats, barren brome, couch grass and cleavers. As advocated by Lutman et al. (2002), the approach uncouples the weed mapping and treatment processes and builds on the observation that patches of these weeds are quite stable in arable fields. There are three main aspects to the project. 1) Machine vision hardware. Hardware component parts of the system are one or more cameras connected to a single board computer (Concurrent Solutions LLC) and interfaced with an accurate Global Positioning System (GPS) supplied by Patchwork Technology. The camera(s) will take separate measurements for each of the three primary colours of visible light (red, green and blue) in each pixel. The basic proof of concept can be achieved in principle using a single camera system, but in practice systems with more than one camera may need to be installed so that larger fractions of each field can be photographed. Hardware will be reviewed regularly during the project in response to feedback from other work packages and updated as required. 2) Image capture and weed identification software. The machine vision system will be attached to toolbars of farm machinery so that images can be collected during different field operations. Images will be captured at different ground speeds, in different directions and at different crop growth stages as well as in different crop backgrounds. Having captured geo-referenced images in the field, image analysis software will be developed to identify weed species by Murray State and Reading Universities with advice from The Arable Group. A wide range of pattern recognition and in particular Bayesian Networks will be used to advance the state of the art in machine vision-based weed identification and mapping. Weed identification algorithms used by others are inadequate for this project as we intend to collect and correlate images collected at different growth stages. Plants grown for this purpose by Herbiseed will be used in the first instance. In addition, our image capture and analysis system will include plant characteristics such as leaf shape, size, vein structure, colour and textural pattern, some of which are not detectable by other machine vision systems or are omitted by their algorithms. Using such a list of features observable using our machine vision system, we will determine those that can be used to distinguish weed species of interest. 3) Weed mapping. Geo-referenced maps of weeds in arable fields (Reading University and Syngenta) will be produced with advice from The Arable Group and Patchwork Technology. Natural infestations will be mapped in the fields but we will also introduce specimen plants in pots to facilitate more rigorous system evaluation and testing. Manual weed maps of the same fields will be generated by Reading University, Syngenta and Peter Lutman so that the accuracy of automated mapping can be assessed. The principal hypothesis and concept to be tested is that by combining maps from several surveys, a weed map with acceptable accuracy for endusers can be produced. If the concept is proved and can be commercialised, systems could be retrofitted at low cost onto existing farm machinery. The outputs of the weed mapping software would then link with the precision farming options already built into many commercial sprayers, allowing their use for targeted, site-specific herbicide applications. Immediate economic benefits would, therefore, arise directly from reducing herbicide costs. SSWM will also reduce the overall pesticide load on the crop and so may reduce pesticide residues in food and drinking water, and reduce adverse impacts of pesticides on non-target species and beneficials. Farmers may even choose to leave unsprayed some non-injurious, environmentally-beneficial, low density weed infestations. These benefits fit very well with the anticipated legislation emerging in the new EU Thematic Strategy for Pesticides which will encourage more targeted use of pesticides and greater uptake of Integrated Crop (Pest) Management approaches, and also with the requirements of the Water Framework Directive to reduce levels of pesticides in water bodies. The greater precision of weed management offered by SSWM is therefore a key element in preparing arable farming systems for the future, where policy makers and consumers want to minimise pesticide use and the carbon footprint of farming while maintaining food production and security. The mapping technology could also be used on organic farms to identify areas of fields needing mechanical weed control thereby reducing both carbon footprints and also damage to crops by, for example, spring tines. Objective i. To develop a prototype machine vision system for automated image capture during agricultural field operations; ii. To prove the concept that images captured by the machine vision system over a series of field operations can be processed to identify and geo-reference specific weeds in the field; iii. To generate weed maps from the geo-referenced, weed plants/patches identified in objective (ii).

Pattern separation in digital learning nets

Pattern separation is a new technique in digital learning networks which can be used to detect state conflicts. This letter describes pattern separation in a simple single-layer network, and an application of the technique in networks with feedback.