Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Use of decision trees in colour feature selection: application to object recognition in outdoor scenes

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

Transformación de la política exterior venezolana en materia comercial, un análisis del sector automotor colombiano. Periodo 2004-2011

Conocer como se vio afectado el comercio en materia automotriz en Colombia por parte de Venezuela es un tema importante para las relaciones diplomáticas y comerciales del país. Al ser Venezuela el país que más importaba vehículos de Colombia, es necesario conocer por qué desde el cambio de la política exterior del presidente Chávez, en el 2004, este sector se ha visto afectado notoriamente y qué estrategias por su parte tendría la industria automotora y el gobierno colombiano para no dejar que éste sector baje su producción.

The pattern of fall of serum bilirubin after operative relief of obstructive jaundice. A preliminary report

This is the beginning of a prospective study on patients who have obstructive jaundice to see how the serum bilirubin falls after operative relief of the obstruction. Seven of such patients have been studied; four had carcinoma of the head of the pancreas while the other three had choledocholithiasis. The patients with carcinoma had relief of the jaundice through a biliary-enteric anastomosis and those with common bile duct stones had choledochotomy with stone extraction which was completed with insertion of a T-tube. Serial bilirubin estimations were then performed post-operatively to chart the pattern and rate of descent of this in each patient. Our observations suggest that the pattern of fall of serum bilirubin after successful decompression of the extra-hepatic biliary tree exhibit a distinct pattern regardless of the surgical procedure performed for the relief of the obstruction.

The construction of national identity as a developmental process from childhood to adolescence in Catalonia

La idea que per entendre alguna cosa hem d'entendre el procés pel qual s'ha produït va ser assumida des de l'origen de l'estudi "La construcció de la identitat nacional com a procés de desenvolupament des de la infància a l'adolescència a Catalunya". Per tal d'estudiar el procés de desenvolupament relacionat amb la construcció de la identitat nacional, no és suficient considerar-lo en el seu context social, sinó que és necessari considerar la seva construcció social. Tres objectius principals van orientar el nostre estudi . Primer, indagar si els processos de categorització, identificació, coneixement, imatge, avaluació i afecte són també elements implicats en la construcció de la identitat nacional des de la infància a l'adolescència. Segon investigar el procés de desenvolupament d'aquests elements des dels 6 anys fins als 15 anys per contribuir amb dades a I'explicació sobre com es desenvolupa del coneixement social. Tres són les explicacions principals; la primera que es basa en processos cognitius d'inclusió-decentració; la segona, si aquest desenvolupament es dóna en cercles concèntrics des de I'interior (contextos quotidians) fins a l'exterior (amb intercanvis més amples i una educació formal envers la vida social); i la tercera, si el desenvolupament del coneixement social d'allò més immediat i directe es reorganitzat i adquireix nous significats per la integració dels elements més generals i abstractes. Tercer, donat el fet que el 50% de les famílies tenen el català com a llengua materna, hem estudiat la influència de la llengua o llengües utilitzades en el context familiar en el procés de construcció de la identitat nacional. Es va dissenyar una entrevista individual seguint una estructura amb les parts següents: identificació subjectiva i identificació nacional; coneixement dels països; estereotips, avaluació i sentiments, i entorn social. La mostra del present estudi va estar formada per 495 nens i adolescents de 6, 9, 12 i 15 anys d'edat. Es va utilitzar per cada grup un número similar de nens i nenes. La mostra es va dividir en tres grups segons la o les llengües utilitzades pel nen o nena en el seu context familiar. Tres van ser les categones lingüístiques utilitzades: castellà, nens que només utilitzin el castellà a casa; català, nens que només usen el català en el context familiar; i bilingües, nens que utilitzin tots dos idiomes a casa. Aquestes categories lingüístiques s'han utilitzat com a indicador dels contextos familiars. Dues conclusions principals es poden extreure d'aquest estudi. Primer, I'ús de categories nacionals no és una conseqüència del procés cognitiu d'inclusió-decentració ni en cercles concentrics (concret/abstracte). La idea d'un procés del món en paral·lel, d'un coneixement que integra simultàniament creences i sentiments de l'ambient concret o proper i de l'abstracte o llunyà pot explicar millor els nostres resultats. Els nens aprenen i pensen sobre la vida quotidara, les característiques de l'ambient, la informació circulant en el seu entom social i la importància o els diferents nivells de coneixement depenent del context. Els nens integren la informació que està circulant en el seu ambient i construeixen un món que necessàriament no ha de coincidir amb el món deis adults, però que els ajuda a comunicar-se i a entendre les situacions en les quals estan immersos. Els nens més joves són capaços d'utilitzar categories nacionals de manera similar als adolescents. Segon, la identitat nacional a Catalunya es construeix al voltant del nucli de la llengua parlada a casa. A través de tot I'estudi es pot veure un resultat consistent i reiterat. Els infants d'un entom familiar en què s'usa només el castellà s'identifiquen com a pertanyents a tots dos grups nacionals, Espanyol i Català. Els infants d'un entom familiar que utilitzen només el català s'identifiquen com a pertanyents només al grup Català. Aquestes identificacions guien com aquests nens avaluen i senten envers el propi grup nacional i els altres. A més, sembla que el context sòcio-polític és un vehicle important en la transmissió de les estructures de semblances i que I'estructura d'afecte es transmet principalment a través de I'entom familiar.

Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling

Flood modelling of urban areas is still at an early stage, partly because until recently topographic data of sufficiently high resolution and accuracy have been lacking in urban areas. However, Digital Surface Models (DSMs) generated from airborne scanning laser altimetry (LiDAR) having sub-metre spatial resolution have now become available, and these are able to represent the complexities of urban topography. The paper describes the development of a LiDAR post-processor for urban flood modelling based on the fusion of LiDAR and digital map data. The map data are used in conjunction with LiDAR data to identify different object types in urban areas, though pattern recognition techniques are also employed. Post-processing produces a Digital Terrain Model (DTM) for use as model bathymetry, and also a friction parameter map for use in estimating spatially-distributed friction coefficients. In vegetated areas, friction is estimated from LiDAR-derived vegetation height, and (unlike most vegetation removal software) the method copes with short vegetation less than ~1m high, which may occupy a substantial fraction of even an urban floodplain. The DTM and friction parameter map may also be used to help to generate an unstructured mesh of a vegetated urban floodplain for use by a 2D finite element model. The mesh is decomposed to reflect floodplain features having different frictional properties to their surroundings, including urban features such as buildings and roads as well as taller vegetation features such as trees and hedges. This allows a more accurate estimation of local friction. The method produces a substantial node density due to the small dimensions of many urban features.

Improving flood models using LiDAR: progress and problems

Airborne scanning laser altimetry (LiDAR) is an important new data source for river flood modelling. LiDAR can give dense and accurate DTMs of floodplains for use as model bathymetry. Spatial resolutions of 0.5m or less are possible, with a height accuracy of 0.15m. LiDAR gives a Digital Surface Model (DSM), so vegetation removal software (e.g. TERRASCAN) must be used to obtain a DTM. An example used to illustrate the current state of the art will be the LiDAR data provided by the EA, which has been processed by their in-house software to convert the raw data to a ground DTM and separate vegetation height map. Their method distinguishes trees from buildings on the basis of object size. EA data products include the DTM with or without buildings removed, a vegetation height map, a DTM with bridges removed, etc. Most vegetation removal software ignores short vegetation less than say 1m high. We have attempted to extend vegetation height measurement to short vegetation using local height texture. Typically most of a floodplain may be covered in such vegetation. The idea is to assign friction coefficients depending on local vegetation height, so that friction is spatially varying. This obviates the need to calibrate a global floodplain friction coefficient. It’s not clear at present if the method is useful, but it’s worth testing further. The LiDAR DTM is usually determined by looking for local minima in the raw data, then interpolating between these to form a space-filling height surface. This is a low pass filtering operation, in which objects of high spatial frequency such as buildings, river embankments and walls may be incorrectly classed as vegetation. The problem is particularly acute in urban areas. A solution may be to apply pattern recognition techniques to LiDAR height data fused with other data types such as LiDAR intensity or multispectral CASI data. We are attempting to use digital map data (Mastermap structured topography data) to help to distinguish buildings from trees, and roads from areas of short vegetation. The problems involved in doing this will be discussed. A related problem of how best to merge historic river cross-section data with a LiDAR DTM will also be considered. LiDAR data may also be used to help generate a finite element mesh. In rural area we have decomposed a floodplain mesh according to taller vegetation features such as hedges and trees, so that e.g. hedge elements can be assigned higher friction coefficients than those in adjacent fields. We are attempting to extend this approach to urban area, so that the mesh is decomposed in the vicinity of buildings, roads, etc as well as trees and hedges. A dominant points algorithm is used to identify points of high curvature on a building or road, which act as initial nodes in the meshing process. A difficulty is that the resulting mesh may contain a very large number of nodes. However, the mesh generated may be useful to allow a high resolution FE model to act as a benchmark for a more practical lower resolution model. A further problem discussed will be how best to exploit data redundancy due to the high resolution of the LiDAR compared to that of a typical flood model. Problems occur if features have dimensions smaller than the model cell size e.g. for a 5m-wide embankment within a raster grid model with 15m cell size, the maximum height of the embankment locally could be assigned to each cell covering the embankment. But how could a 5m-wide ditch be represented? Again, this redundancy has been exploited to improve wetting/drying algorithms using the sub-grid-scale LiDAR heights within finite elements at the waterline.

Food consumption impacts of adherence to dietary norms in the United States: a quantitative assessment

Promotion of adherence to healthy-eating norms has become an important element of nutrition policy in the United States and other developed countries. We assess the potential consumption impacts of adherence to a set of recommended dietary norms in the United States using a mathematical programming approach. We find that adherence to recommended dietary norms would involve significant changes in diets, with large reductions in the consumption of fats and oils along with large increases in the consumption of fruits, vegetables, and cereals. Compliance with norms recommended by the World Health Organization for energy derived from sugar would involve sharp reductions in sugar intakes. We also analyze how dietary adjustments required vary across demographic groups. Most socio-demographic characteristics appear to have relatively little influence on the pattern of adjustment required to comply with norms, Income levels have little effect on required dietary adjustments. Education is the only characteristic to have a significant influence on the magnitude of adjustments required. The least educated rather than the poorest have to bear the highest burden of adjustment. Out- analysis suggests that fiscal measures like nutrient-based taxes may not be as regressive as commonly believed. Dissemination of healthy-eating norms to the less educated will be a key challenge for nutrition policy.

Food consumption impacts of adherence to dietary norms in the United States: a quantitative assessment

Promotion of adherence to healthy-eating norms has become an important element of nutrition policy in the United States and other developed countries. We assess the potential consumption impacts of adherence to a set of recommended dietary norms in the United States using a mathematical programming approach. We find that adherence to recommended dietary norms would involve significant changes in diets, with large reductions in the consumption of fats and oils along with large increases in the consumption of fruits, vegetables, and cereals. Compliance with norms recommended by the World Health Organization for energy derived from sugar would involve sharp reductions in sugar intakes. We also analyze how dietary adjustments required vary across demographic groups. Most socio-demographic characteristics appear to have relatively little influence on the pattern of adjustment required to comply with norms, Income levels have little effect on required dietary adjustments. Education is the only characteristic to have a significant influence on the magnitude of adjustments required. The least educated rather than the poorest have to bear the highest burden of adjustment. Out- analysis suggests that fiscal measures like nutrient-based taxes may not be as regressive as commonly believed. Dissemination of healthy-eating norms to the less educated will be a key challenge for nutrition policy.

Towards a definition of a crop wild relative

Crop wild relatives are an important socio-economic resource that is currently being eroded or even extinguished through careless human activities. If the Conference of the Parties (COP) to the CBD 2010 Biodiversity Target of achieving a significant reduction in the current rate of loss is to be achieved, we must first define what crop wild relatives are and how their conservation might be prioritised. A definition of a crop wild relative is proposed and illustrated in the light of previous Gene Pool concept theory. Where crossing and genetic diversity information is unavailable, the Taxon Group concept is introduced to assist recognition of the degree of crop wild relative relatedness by using the existing taxonomic hierarchy.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

An assessment of eye-gaze potential within immersive virtual environments

In collaborative situations, eye gaze is a critical element of behavior which supports and fulfills many activities and roles. In current computer-supported collaboration systems, eye gaze is poorly supported. Even in a state-of-the-art video conferencing system such as the access grid, although one can see the face of the user, much of the communicative power of eye gaze is lost. This article gives an overview of some preliminary work that looks towards integrating eye gaze into an immersive collaborative virtual environment and assessing the impact that this would have on interaction between the users of such a system. Three experiments were conducted to assess the efficacy of eye gaze within immersive virtual environments. In each experiment, subjects observed on a large screen the eye-gaze behavior of an avatar. The eye-gaze behavior of that avatar had previously been recorded from a user with the use of a head-mounted eye tracker. The first experiment was conducted to assess the difference between users' abilities to judge what objects an avatar is looking at with only head gaze being viewed and also with eye- and head-gaze data being displayed. The results from the experiment show that eye gaze is of vital importance to the subjects, correctly identifying what a person is looking at in an immersive virtual environment. The second experiment examined whether a monocular or binocular eye-tracker would be required. This was examined by testing subjects' ability to identify where an avatar was looking from their eye direction alone, or by eye direction combined with convergence. This experiment showed that convergence had a significant impact on the subjects' ability to identify where the avatar was looking. The final experiment looked at the effects of stereo and mono-viewing of the scene, with the subjects being asked to identify where the avatar was looking. This experiment showed that there was no difference in the subjects' ability to detect where the avatar was gazing. This is followed by a description of how the eye-tracking system has been integrated into an immersive collaborative virtual environment and some preliminary results from the use of such a system.

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).

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.