Individualized Models of Colour Differentiation through Situation-Specific Modelling

In digital environments, colour is used for many purposes: for example, to encode information in charts, signify missing field information on websites, and identify active windows and menus. However, many people have inherited, acquired, or situationally-induced Colour Vision Deficiency (CVD), and therefore have difficulties differentiating many colours. Recolouring tools have been developed that modify interface colours to make them more differentiable for people with CVD, but these tools rely on models of colour differentiation that do not represent the majority of people with CVD. As a result, existing recolouring tools do not help most people with CVD. To solve this problem, I developed Situation-Specific Modelling (SSM), and applied it to colour differentiation to develop the Individualized model of Colour Differentiation (ICD). SSM utilizes an in-situ calibration procedure to measure a particular user’s abilities within a particular situation, and a modelling component to extend the calibration measurements into a full representation of the user’s abilities. ICD applies in-situ calibration to measuring a user’s unique colour differentiation abilities, and contains a modelling component that is capable of representing the colour differentiation abilities of almost any individual with CVD. This dissertation presents four versions of the ICD and one application of the ICD to recolouring. First, I describe the development and evaluation of a feasibility implementation of the ICD that tests the viability of the SSM approach. Second, I present revised calibration and modelling components of the ICD that reduce the calibration time from 32 minutes to two minutes. Next, I describe the third and fourth ICD versions that improve the applicability of the ICD to recolouring tools by reducing the colour differentiation prediction time and increasing the power of each prediction. Finally, I present a new recolouring tool (ICDRecolour) that uses the ICD model to steer the recolouring process. In a comparative evaluation, ICDRecolour achieved 90% colour matching accuracy for participants – 20% better than existing recolouring tools – for a wide range of CVDs. By modelling the colour differentiation abilities of a particular user in a particular environment, the ICD enables the extension of recolouring tools to helping most people with CVD, thereby reducing the difficulties that people with CVD experience when using colour in digital environments

Evaluation of the damages caused by seismic events: First tests on supporting traditional multispectral classification with DSM

Seismic damages, as a roof entirely collapsed on the ground, are very difficult to be found using only multispectral classification algorithms. The availability of high resolution stereopairs from satellite disclose new possible fields of application to estimate changes and transformations of areas following catastrophic events. Combining both techniques it is obviously possible only when stereoscopic and multispectral images are available. In this case, as for all monitoring studies, it is necessary to compare the present situation to the pre-seismic one. The pre-seismic situation can be advantageously studied by classic photogrammetric techniques based on aerial frames, that are available in archives managed by photogrammetric companies and local government agencies. But it is also possible to extract the pre-seismic morphology from digital maps, containing the three-dimensional characteristics of the buildings. The present research tries to: a) improve the digital surface model extracted from Ikonos satellite images covering an area of central Italy (Foligno, Umbria), through a pre-treatment of images and a manual editing b) study the best DSM models to improve the detection of height difference, mainly in urban areas, and evaluate the results of the classification of land cover as further data to detect changes in building shape. DSM obtained by three-dimensional maps have been compared with DSM extracted directly from aerial stereo-pairs using different approaches. In the area under study a seismic event happened in September of the '97 causing relevant damages to different urbanized centres of the area

Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods

Variational methods are widely used for the analysis and control of computationally intensive spatially distributed systems. In particular, the adjoint state method enables a very efficient calculation of the derivatives of an objective function (response function to be analysed or cost function to be optimised) with respect to model inputs. In this contribution, it is shown that the potential of variational methods for distributed catchment scale hydrology should be considered. A distributed flash flood model, coupling kinematic wave overland flow and Green Ampt infiltration, is applied to a small catchment of the Thoré basin and used as a relatively simple (synthetic observations) but didactic application case. It is shown that forward and adjoint sensitivity analysis provide a local but extensive insight on the relation between the assigned model parameters and the simulated hydrological response. Spatially distributed parameter sensitivities can be obtained for a very modest calculation effort (~6 times the computing time of a single model run) and the singular value decomposition (SVD) of the Jacobian matrix provides an interesting perspective for the analysis of the rainfall-runoff relation. For the estimation of model parameters, adjoint-based derivatives were found exceedingly efficient in driving a bound-constrained quasi-Newton algorithm. The reference parameter set is retrieved independently from the optimization initial condition when the very common dimension reduction strategy (i.e. scalar multipliers) is adopted. Furthermore, the sensitivity analysis results suggest that most of the variability in this high-dimensional parameter space can be captured with a few orthogonal directions. A parametrization based on the SVD leading singular vectors was found very promising but should be combined with another regularization strategy in order to prevent overfitting

A predictive model of colour differentiation

The ability to differentiate between colours varies from individual to individual. This variation is attributed to factors such as the presence of colour blindness. Colour is used to encode information in information visualizations. An example of such an encoding is categorization using colour (e.g., green for land, blue for water). As a result of the variation in colour differentiation ability among individuals, many people experience difficulties when using colour-encoded information visualizations. These difficulties result from the inability to adequately differentiate between two colours, resulting in confusion, errors, frustration, and dissatisfaction. If a user-specific model of colour differentiation was available, these difficulties could be predicted and corrected. Prediction and correction of these difficulties would reduce the amount of confusion, errors, frustration, and dissatisfaction experienced by users. This thesis presents a model of colour differentiation that is tuned to the abilities of a particular user. To construct this model, a series of judgement tasks are performed by the user. The data from these judgement tasks is used to calibrate a general colour differentiation model to the user. This calibrated model is used to construct a predictor. This predictor can then be used to make predictions about the user's ability to differentiate between two colours. Two participant-based studies were used to evaluate this solution. The first study evaluated the basic approach used to model colour differentiation. The second study evaluated the accuracy of the predictor by comparing its performance to the performance of human participants. It was found that the predictor was as accurate as the human participants 86.3% of the time. Using such a predictor, the colour differentiation abilities of particular users can be accurately modeled

Object Classification in Astronomical Multi-Color Surveys

We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of >65000 color templates. The method aims for extracting the information content of object colors in a statistically correct way and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach. For the redshift estimation, we use an advanced version of the MEV estimator which determines the redshift error from the redshift dependent probability density function. The method was originally developed for the CADIS survey, where we checked its performance by spectroscopy. The method provides high reliability (6 errors among 151 objects with R<24), especially for quasar selection, and redshifts accurate within sigma ~ 0.03 for galaxies and sigma ~ 0.1 for quasars. We compare a few model surveys using the same telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. In practice, medium-band surveys show superior performance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, and is most critical for surveys with few, broad and deeply exposed filters, but less severe for many, narrow and less deep filters.Comment: 21 pages including 10 figures. Accepted for publication in Astronomy & Astrophysic

Markets, Institutions, and the Quality of Agricultural Products: Cotton Quality in India

The modern global textile industry requires cotton with strong and consistent fibers in order to produce high quality goods at the high speeds necessary to recover capital costs. The introduction of high volume instrument (HVI) measurement of cotton fiber quality has strengthened the link between cotton prices and attributes on world markets. The spread of genetically modified (GMO) cotton in India has driven India to the second ranked producer and exporter of cotton in the world. However, contamination and other quality problems are endemic to Indian cotton. Using a unique data set of Indian cotton prices and quality attributes from 5 Indian states, this study uses hedonic price modeling to demonstrate that the linkages between cotton quality and price are weaker in India than they are in the United States.Crop Production/Industries, Production Economics,

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques