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A Colour Alphabet and the Limits of Colour Coding
Abstract and Figures
This paper describes a series of studies designed to investigate the possible limits to the number of different colours that can be used in a colour code and the relative merits of colours and shapes for communicating information. The studies took their particular form in response to an observation by Rudolf Arnheim that an alphabet of 26 colours would be unusable. It was found that a text, with letters represented by coloured rectangles, can be read, first with the help of a key and then without. The colour alphabet, tested in competition with other alphabets made up of unfamiliar shapes and faces, was read more quickly than the others. Speed of reading was only matched with an alphabet made up of shapes that were familiar and nameable. Colours are most helpful for quick identification and for clarifying complex information, but where more than 26 distinctions must be made colours must be supplemented by shapes, typically in the form of letters and numbers.
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... Figs. 2 to 5 show the ground truths of the test datasets and the segmentations found by our method. The colors used for visualization are adapted from[32]. The segment boundaries given by the ground truths are laid over the full segmentations inFigs. ...
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectral variability and noise in hyperspectral data make this task very challenging and define special requirements for such methods. Here, we present a novel unsupervised hyperspectral segmentation framework. It starts with a denoising and dimensionality reduction step by the well-established Minimum Noise Fraction (MNF) transform. Then, the Mumford-Shah (MS) segmentation functional is applied to segment the data. We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data. To optimize our objective function with respect to the parameters for which no closed form solution is available, we propose an efficient fixed point iteration scheme. Numerical experiments on four public benchmark datasets show that our method produces competitive results, which outperform two state-of-the-art methods substantially on three of these datasets.
... Especially the different types of so-called covered areas (i.e., roads, railways, and other covered areas) are more accurately approximated by the clusters' centroids. Finally, the third remediation approach to improve the clustering method's accuracy was to replace the colours of the ground cover map with a set of 14 colours from the list of most contrasting colours as presented by Kelly and Judd (1976); Green-Armytage (2010). The colours of the original ground cover map are namely chosen in such a way that the link between the used colour and the ground cover it represents, is intuitive. ...
As the chemical and energy producing industries are steadily transitioning towards more sustainable processing practices, renewable biomass resources are becoming increasingly more valuable. Recently, following the realisation that renewable resources for the chemical and energy industry should not compete with food supplies, the use of plant-based biowaste has significantly gained in interest. Due to its inherently variable composition, diffuse distribution, and seasonality, it is of the utmost importance that (potential) biorefinery exploiters are well informed of the biowaste resources that are available in the vicinity of their (planned) biorefinery. Designing a biorefinery in such a way that it can tailor for the locally available biowaste resources, exhibits several compelling advantages. Apart from significantly reduced logistics costs, the usage of local biowaste can be a reciprocal advantage for both the involved community and the biorefinery. In this paper, a GIS-based (Geo-Information System) bio-inventory toolbox is presented. The toolbox is developed to aid the biorefinery designers and decision makers, e.g., governmental bodies, to get an adequate overview of the locally available plant-based biowaste resources and, linked to this, the expected periodical amounts, their composition, and their seasonality. The toolbox presented in this contribution is the first part of a decision support tool for the development of a locally embedded flexi-feed and small-scale biorefinery, additionally consisting out of a process modelling tool, and an optimisation tool. Both of these additional tools will employ the information obtained from the bio-inventory toolbox to simulate and optimise several suitable biorefinery designs. The eventual goal of the decision support tool is to provide users with several optimised biorefinery designs that are tailored for their local setting. The additional toolboxes are detailed elsewhere.
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectral variability and noise in hyperspectral data make this task very challenging and define special requirements for such methods. Here, we present a novel unsupervised hyperspectral segmentation framework. It starts with a denoising and dimensionality reduction step by the well-established Minimum Noise Fraction (MNF) transform. Then, the Mumford-Shah (MS) segmentation functional is applied to segment the data. We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data. To optimize our objective function with respect to the parameters for which no closed form solution is available, we propose an efficient fixed point iteration scheme. Numerical experiments on four public benchmark datasets show that our method produces competitive results, which outperform three state-of-the-art methods substantially on three of these datasets.
Eye tracking measurements taken while watching a wide field screen are challenging to perform. Commercially available remote eye trackers typically do not measure more than 35 degrees in eccentricity. Analysis software was developed using the Pupil Core Eye Tracking data to analyze viewing behavior under circumstances as natural as possible, on a 1.55-m-wide screen allowing free head movements. Additionally, dynamic area of interest (AOI) analyses were performed on data of participants viewing traffic scenes. A toolkit was created including software for simple allocation of dynamic AOIs (semi-automatically and manually), measurement of parameters such as dwell times and time to first entry, and overlaying gaze and AOIs on video. Participants (n =11) were asked to look at 13 dynamic AOIs in traffic scenes from appearance to disappearance in order to validate the setup and software. Different AOI margins were explored for the included objects. The median ratio between total appearance time and dwell time was about 90% for most objects when appropriate margins were chosen. This validated open-source toolkit is readily available for researchers who want to perform dynamic AOI analyses with the Pupil Core eye tracker, especially when measurements are desired on a wide screen, in various fields such as psychology, transportation, and low vision research.
Previous work has shown how different interfaces (i.e., route navigation, maps, or a combination of the two) influence spatial knowledge and recollection. To test for the existence of intermediate representations along an egocentric-to-allocentric continuum, we developed a novel task, tabletop navigation, to provide a mixture of cues that inform the emergence of egocentric and allocentric representations or strategies. In this novel tabletop task, participants navigated a remote-controlled avatar through a tabletop scale model of the virtual city. Participants learned virtual cities from either navigating routes, studying maps, or our new tabletop navigation task. We interleaved these learning tasks with either an in situ pointing task (the scene- and orientation-dependent pointing [SOP] task) or imagined judgements of relative direction (JRD) pointing. In Experiment 1, performance on each memory task was similar across learning tasks and performance on the route and map learning tasks correlated with more precise spatial recall on both the JRD and SOP tasks. Tabletop learning performance correlated with SOP performance only, suggesting a reliance on egocentric strategies, although increased utilization of the affordances of the tabletop task were related to JRD performance. In Experiment 2, using a modified criterion map learning task, participants who learned using maps provided more precise responses on the JRD compared to route or tabletop learning. Together, these findings provide mixed evidence for both optimization and egocentric predominance after learning from the novel tabletop navigation task.
The process of mapping neuroanatomy at multiple scales is defined as neurocartography and has the ultimate goal of revealing a complete wiring diagram of synaptic connections. Neurocartography is an important pursuit because brain function is intricately linked to neuroanatomy, similar to how the function of a protein depends on its structure. Here, we developed new light microscopy approaches for neurocartography at both microscale and nanoscale levels to map single neuron morphologies and synaptic connectivity respectively. Spatially sparse labeling of neurons has been necessary when studying neuron morphologies to minimize overlap and avoid ambiguities during reconstructions. We developed two strategies to overcome this limitation and achieve spatially dense labeling. First, we used a multicolor genetic labeling (Brainbow) approach to stochastically express fluorescent proteins in a spatially dense population of neurons, facilitating reconstruction of single neuron morphologies. We extend the Brainbow viral toolbox by 1) introducing 12 fluorescent proteins in the form of 6 new viruses, 2) using a membrane and cytoplasmic dual labeling strategy, and 3) adopting the AAV.PHP.eB capsid to systemically induce expression and better control color diversity. Second, we used expansion microscopy to increase confocal imaging resolution by physically magnifying brain samples. We developed a multi-round immunostaining Expansion Microscopy (miriEx) protocol that enables multiplexed protein detection at multiple imaging resolutions. We then combined Brainbow with miriEx to simultaneously map morphology, molecular markers, and connectivity in the same brain section. We define the derivation of these properties from hyperspectral fluorescent channels as spectral connectomics, a light microscopy based approach towards mapping neuroanatomy and connectivity with molecular specificity. We applied our multimodal profiling strategy to directly link inhibitory neuron cell types with their network morphologies. Furthermore, we showed that correlative Brainbow and endogenous synaptic machinery immunostaining can be used to define putative synaptic connections between spectrally unique neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy across multiple animals and time points. We hope that the light microscopy approaches developed in this dissertation will facilitate the extraction of new biological insights from neurocartography.
The number of online news articles available nowadays is rapidly increasing. When exploring articles on online news portals, navigation is mostly limited to the most recent ones. The spatial context and the history of topics are not immediately accessible. To support readers in the exploration or research of articles in large datasets, we developed an interactive 3D globe visualization. We worked with datasets from multiple online news portals containing up to 45000 articles. Using agglomerative hierarchical clustering, we represent the referenced locations of news articles on a globe with different levels of detail. We employ two interaction schemes for navigating the viewpoint on the visualization, including support for hand-held devices and desktop PCs, and provide search functionality and interactive filtering. Based on this framework, we explore additional modules for jointly exploring the spatial and temporal domain of the dataset and incorporating live news into the visualization.
Visualization systems belong to supervised tools that can make noticeable the intrinsic structures of defects on data. However, despite the significant number of these systems that assist Data Quality Assessment, few provide resources to examine these structures deeply. This situation prevents data quality appraisers from using their contextual knowledge to confirm or refute any data defect. This article explores a visualisation system’s additional features and design characteristics (named V is4DD) that uses visual-interactive properties to support data quality visual assessment on abstract and timeless data (e.g., Customer, Billing). Additionally, we conduct a full review and outline the state-of-art visualization systems related to data quality assessment and fit Vis4DD into this scenario.
Healthy Ageing represents one of the most crucial topics that our society will face in the next decades, due to the progressive demographic ageing of the population, (e.g., in Italy old-age dependency ratio is estimated to reach 59,7% by 2065). These statistics highlights the need of a new paradigm for active and healthy ageing, as well as a healthcare system able to support elderly population in age-related diseases management. In this work we present Let’s dance, an innovative exergame that aims to provide a new way to deliver physiotherapy for mild or moderate Parkinson’s disease patients. Within the game, the patients are asked to reproduce the choreography proposed, whose dance moves are aimed toward the achievement of specific therapeutic goals (e.g., optimize motor coordination, physical endurance, etc.). The game is part of a broader integrated platform that through sensors and actuators located on a robot device in the clinical environment and on patients themselves, can track in real-time both the game performance and the clinical KPIs and determine the assessment of patient’s therapeutic progress. In this paper we present the first prototype of Let’s dance and a use-case scenario.
The Observer's Book of Heraldry
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Reinventing Color, 1950 to Today
- A Temkin
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