Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Colour Communication Within Different Languages

For computational methods aiming to reproduce colour names that are meaningful to speakers of different languages, the mapping between perceptual and linguistic aspects of colour is a problem of central information processing. This thesis advances the field of computational colour communication within different languages in five main directions. First, we show that web-based experimental methodologies offer considerable advantages in obtaining a large number of colour naming responses in British and American English, Greek, Russian, Thai and Turkish. We continue with the application of machine learning methods to discover criteria in linguistic, behavioural and geometric features of colour names that distinguish classes of colours. We show that primary colour terms do not form a coherent class, whilst achromatic and basic classes do. We then propose and evaluate a computational model trained by human responses in the online experiment to automate the assignment of colour names in different languages across the full three-dimensional colour gamut. Fourth, we determine for the first time the location of colour names within a physiologically-based cone excitation space through an unconstrained colour naming experiment using a calibrated monitor under controlled viewing conditions. We show a good correspondence between online and offline datasets; and confirm the validity of both experimental methodologies for estimating colour naming functions in laboratory and real-world monitor settings. Finally, we present a novel information theoretic measure, called dispensability, for colour categories that predicts a gradual scale of basicness across languages from both web- and laboratory- based unconstrained colour naming datasets. As a result, this thesis contributes experimental and computational methodologies towards the development of multilingual colour communication schemes

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of autumn, can be derived from sensor-based time series data at the near-surface and remote scales, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, as well as to perform digital image analysis for time series-based estimates of phenology dates. We then compare these near-surface results to remote sensing metrics of phenology at the landscape scale, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit, using a generalized sigmoid, to estimate phenology dates. We quantify the statistical uncertainty of phenophase transition dates estimated using this method and show that the generalized sigmoid results in less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than remote sensing metrics of phenology, and that dates derived from the remotely-sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time series estimates for the start of spring are found to closely match visual assessment of leaf out, as well as remote sensing-derived estimates of the start of spring. However late spring and autumn phenology exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.Organismic and Evolutionary Biolog

Mitigating the effects of undersampling in weak lensing shear estimation with metacalibration

Metacalibration is a state-of-the-art technique for measuring weak gravitational lensing shear from well-sampled galaxy images. We investigate the accuracy of shear measured with metacalibration from fitting elliptical Gaussians to undersampled galaxy images. In this case, metacalibration introduces aliasing effects leading to an ensemble multiplicative shear bias about 0.01 for Euclid, and even larger for the Roman Space Telescope, well exceeding the missions' requirements. We find that this aliasing bias can be mitigated by computing shapes from weighted moments with wider Gaussians as weight functions, thereby trading bias for a slight increase in variance of the measurements. We show that this approach is robust to the point-spread function in consideration and meets the stringent requirements of Euclid for galaxies with moderate to high signal-to-noise ratios. We therefore advocate metacalibration as a viable shear measurement option for weak lensing from upcoming space missions.Comment: 17 pages, 12 figures, 3 tables; matches the published version in MNRA

TOWARDS A COMPUTATIONAL MODEL OF RETINAL STRUCTURE AND BEHAVIOR

Human vision is our most important sensory system, allowing us to perceive our surroundings. It is an extremely complex process that starts with light entering the eye and ends inside of the brain, with most of its mechanisms still to be explained. When we observe a scene, the optics of the eye focus an image on the retina, where light signals are processed and sent all the way to the visual cortex of the brain, enabling our visual sensation. The progress of retinal research, especially on the topography of photoreceptors, is often tied to the progress of retinal imaging systems. The latest adaptive optics techniques have been essential for the study of the photoreceptors and their spatial characteristics, leading to discoveries that challenge the existing theories on color sensation. The organization of the retina is associated with various perceptive phenomena, some of them are straightforward and strictly related to visual performance like visual acuity or contrast sensitivity, but some of them are more difficult to analyze and test and can be related to the submosaics of the three classes of cone photoreceptors, like how the huge interpersonal differences between the ratio of different cone classes result in negligible differences in color sensation, suggesting the presence of compensation mechanisms in some stage of the visual system. In this dissertation will be discussed and addressed issues regarding the spatial organization of the photoreceptors in the human retina. A computational model has been developed, organized into a modular pipeline of extensible methods each simulating a different stage of visual processing. It does so by creating a model of spatial distribution of cones inside of a retina, then applying descriptive statistics for each photoreceptor to contribute to the creation of a graphical representation, based on a behavioral model that determines the absorption of photoreceptors. These apparent color stimuli are reconstructed in a representation of the observed scene. The model allows the testing of different parameters regulating the photoreceptor's topography, in order to formulate hypothesis on the perceptual differences arising from variations in spatial organization

Spatio-temporal variability analysis of territorial resistance and resilience to risk assessment

Natural materials, such as soils, are influenced by many factors acting during their formative and evolutionary process: atmospheric agents, erosion and transport phenomena, sedimentation conditions that give soil properties a non-reducible randomness by using sophisticated survey techniques and technologies. This character is reflected not only in the spatial variability of soil properties which differ punctually, but also in their multivariate correlation as function of reciprocal distance. Cognitive enrichment, offered by the response of soils associated with their spatial variability, implies an increase in the evaluative capacity of contributing causes and potential effects in the field of failure phenomena. Stability analysis of natural slopes is well suited to stochastic treatment of the uncertainty which characterized landslide risk. In particular, the research activity has been carried out in back-analysis to a slope located in Southern Italy that was subject to repeated phenomena of hydrogeological instability - extended for several kilometers and recently reactivated - applying spatial analysis to the controlling factors and quantifying the hydrogeological susceptibility through unbiased estimators and indicators. A natural phenomenon, defined as geo-stochastic process, is indeed characterized by interacting variables leading to identifying the most critical areas affected by instability. Through a sensitivity analysis of the local variability as well as a reliability assessment of the time-based scenarios, an improvement of the forecasting content has been obtained. Moreover, the phenomenological characterization will allow to optimize the attribution of the levels of risk to the wide territory involved, supporting decision-making process for intervention priorities as well as the effective allocation of the available resources in social, environmental and economic contexts

Chromatic processing in the zebrafish (Danio rerio) inner retina: bipolar cell physiology and open hardware designs for spectrally accurate stimulation under two-photon

Colour vision describes the ability of animals to differentiate objects based on their spectral reflectance properties independent of light intensity. It is an essential evolutionary trait that allows species to efficiently forage for food, avoid predation, break camouflage, communicate with conspecifics, or to find mates. Zebrafish is a powerful model for studying colour vision as it possesses four cone-photoreceptor types which can be categorised as Red-, Green-, Blue- and UV-sensitive. From first principles, its retina therefore holds the potential to process diverse chromatic computations. In the presented work, the focus was on retinal bipolar cells (BC). These are the retina’s first projection neurons. They receive inputs from the photoreceptors in the outer retina, and send their axon terminals to the inner retina, the inner plexiform layer (IPL). Diverse types within this class of interneuron shape light responses collected by the photoreceptor array into parallel channels with diverse spectral properties. BCs also make connections with all other neuron types within the retina, including horizontal cells in the outer retina, and amacrine as well as retinal ganglion cells in the inner retina. This makes them a central hub for spectral processing within the retina. By combining genetically encoded calcium indicator and two-photon microscopy, light-driven activity from larval zebrafish BC synaptic terminals was systematically recorded in vivo. Synaptic responses to tetrachromatic light stimulation unveiled an unprecedented degree of visual specialisation, including retinal regions dedicated to distinct light-guided behaviours. These regional characteristics were further correlated to functional BC types which were strongly associated with specific retinal positions and axonal stratification depths. Overall, BC projections to the inner plexiform layer displayed a sophisticated level of organisation, structured into chromatic and achromatic functional layers which systematically adjusted their response profiles across the eye to match natural spectral input statistics. Together, these findings bolster our understanding of “colour-processing” in this animal’s inner retina and suggest that unlike in mammals, teleost fish BCs already encode complex chromatic responses in the inner plexiform layer before driving retinal ganglion cells. Additionally, the study of colour vision from an organism requires precise control over the light stimuli’s temporal, spatial and spectral features. Therefore, chromatic stimulators, designed to be combined with two-photon microscopy, were developed throughout this work. These devices allowed circumventing experimental limitations, such as spectral crosstalk between the microscope and the stimulus light. Furthermore, they were conceived as open source projects to be easily replicated and adapted to any organism’s retina with different spectral sensitivities through the free control over the number and spectra of stimulation light sources. These open source projects originated from the desire to set up a stimulation standard for the field of visual neuroscience

Probabilistic Model for Laser Damage to the Human Retina

Understanding how lasers interact with media during propagation is a premiere field of physics. The subject area known as laser bioeffects explores laser interactions with biological cells, tissues, organs, and bodies. This research includes laser applications used in medicine, establishes safe exposure limits for industry and academia, and generally studies the many effects of laser light on living creatures. The bioeffects community relies heavily on deterministic modeling and simulation tools to support experimental research into damage thresholds and laser effects. However, recent laser applications require a probabilistic approach to support risk management and analyses methodologies. Some probabilistic models exist, but their assumptions are largely biased due to sampling and reporting techniques. This research focuses on building the first-ever population-based probabilistic model for retinal damage using a statistical model of the optical properties and dimensions of the human eye. Simulated population distributions are used as input to propagation and thermal damage models for analysis. The results of this research are intended to provide a foundation for future probabilistic models and applications. The format of this document is two separate papers. The first is the development of the statistical eye model based on human covariance data: An Analysis of the Influences of Biological Variance, Measurement Error, and Uncertainty on Retinal Photothermal Damage Threshold Studies. The paper examines trends in wavelength and time dependencies of damage thresholds. The second paper, Biological Variance-Based Dose Response Model for 514 to 1064 Nanometer Laser Exposures, is the application of the statistical eye model in the creation of the dose-response model. The model can be used to establish the design space in the development of future laser systems. It provides the foundation for a true population-based risk analysis tool for safety standards development