84 research outputs found

    Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows

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    A method for estimating the Camera Response Function (CRF) of an electronic motion picture camera is presented in this work. The accurate estimation of the CRF allows for proper encoding of camera exposures into motion picture post-production workflows, like the Academy Color Encoding Specification (ACES), this being a necessary step to correctly combine images from different capture sources into one cohesive final production and minimize non-creative manual adjustments. Although there are well known standard CRFs implemented in typical video camera workflows, motion picture workflows and newer High Dynamic Range (HDR) imaging workflows have introduced new standard CRFs as well as custom and proprietary CRFs that need to be known for proper post-production encoding of the camera footage. Current methods to estimate this function rely on the use of measurement charts, using multiple static images taken under different exposures or lighting conditions, or assume a simplistic model of the function’s shape. All these methods become problematic and tough to fit into motion picture production and post-production workflows where the use of test charts and varying camera or scene setups becomes impractical and where a method based solely on camera footage, comprised of a single image or a series of images, would be advantageous. This work presents a methodology initially based on the work of Lin, Gu, Yamazaki and Shum that takes into account edge color mixtures in an image or image sequence, that are affected by the non-linearity introduced by a CRF. In addition, a novel feature based on image noise is introduced to overcome some of the limitations of edge color mixtures. These features provide information that is included in the likelihood probability distribution in a Bayesian framework to estimate the CRF as the expected value of a posterior probability distribution, which is itself approximated by a Markov Chain Monte Carlo (MCMC) sampling algorithm. This allows for a more complete description of the CRF over methods like Maximum Likelihood (ML) and Maximum A Posteriori (MAP). The CRF function is modeled by Principal Component Analysis (PCA) of the Database of Response Functions (DoRF) compiled by Grossberg and Nayar, and the prior probability distribution is modeled by a Gaussian Mixture Model (GMM) of the PCA coefficients for the responses in the DoRF. CRF estimation results are presented for an ARRI electronic motion picture camera, showing the improved estimation accuracy and practicality of this method over previous methods for motion picture post-production workflows

    Evaluation and optimal design of spectral sensitivities for digital color imaging

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    The quality of an image captured by color imaging system primarily depends on three factors: sensor spectral sensitivity, illumination and scene. While illumination is very important to be known, the sensitivity characteristics is critical to the success of imaging applications, and is necessary to be optimally designed under practical constraints. The ultimate image quality is judged subjectively by human visual system. This dissertation addresses the evaluation and optimal design of spectral sensitivity functions for digital color imaging devices. Color imaging fundamentals and device characterization are discussed in the first place. For the evaluation of spectral sensitivity functions, this dissertation concentrates on the consideration of imaging noise characteristics. Both signal-independent and signal-dependent noises form an imaging noise model and noises will be propagated while signal is processed. A new colorimetric quality metric, unified measure of goodness (UMG), which addresses color accuracy and noise performance simultaneously, is introduced and compared with other available quality metrics. Through comparison, UMG is designated as a primary evaluation metric. On the optimal design of spectral sensitivity functions, three generic approaches, optimization through enumeration evaluation, optimization of parameterized functions, and optimization of additional channel, are analyzed in the case of the filter fabrication process is unknown. Otherwise a hierarchical design approach is introduced, which emphasizes the use of the primary metric but the initial optimization results are refined through the application of multiple secondary metrics. Finally the validity of UMG as a primary metric and the hierarchical approach are experimentally tested and verified

    N-colour separation methods for accurate reproduction of spot colours

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    In packaging, spot colours are used to print key information like brand logos and elements for which the colour accuracy is critical. The present study investigates methods to aid the accurate reproduction of these spot colours with the n-colour printing process. Typical n-colour printing systems consist of supplementary inks in addition to the usual CMYK inks. Adding these inks to the traditional CMYK set increases the attainable colour gamut, but the added complexity creates several challenges in generating suitable colour separations for rendering colour images. In this project, the n-colour separation is achieved by the use of additional sectors for intermediate inks. Each sector contains four inks with the achromatic ink (black) common to all sectors. This allows the extension of the principles of the CMYK printing process to these additional sectors. The methods developed in this study can be generalised to any number of inks. The project explores various aspects of the n-colour printing process including the forward characterisation methods, gamut prediction of the n-colour process and the inverse characterisation to calculate the n-colour separation for target spot colours. The scope of the study covers different printing technologies including lithographic offset, flexographic, thermal sublimation and inkjet printing. A new method is proposed to characterise the printing devices. This method, the spot colour overprint (SCOP) model, was evaluated for the n-colour printing process with different printing technologies. In addition, a set of real-world spot colours were converted to n-colour separations and printed with the 7-colour printing process to evaluate against the original spot colours. The results show that the proposed methods can be effectively used to replace the spot coloured inks with the n-colour printing process. This can save significant material, time and costs in the packaging industry

    Colour prediction for sustainable fibre blending

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    The blending of coloured fibre is explored as a sustainable method of colouration when coupled with sustainable fibre and dyeing choices such as spun-dyed Lenzing Viscose Austria. It was found that a selection of spun-dyed colours (primaries) can be used to create homogenous 4-colour blends when mixed in specific groups. The use of 4-colour blends ensures that the optimal amount of colours within a gamut are produced with the lowest possible number of primaries depending on the acceptable mean colour difference of the 4-colour blends. The acceptable mean colour difference of a blend (measured by averaging each pair of colour differences between the primaries in a blend) can be derived using example 4-colour blends and participant observations at a set viewing distance. Using MATLAB, a method of estimating the number of primaries required to fill a given gamut in CIELAB colour space was developed. Primaries can be distributed across CIELAB colour space and grouped into tetrahedral groups of four for blending. The mean colour difference of the tetrahedral 4-colour blends can be increased or decreased by varying the number of primaries within a gamut. It was also found that the maximum mean colour difference of blends in order for them to appear solid (when viewed at a specific viewing distance) was transferable to blends in knit form. Comparisons of existing blend prediction models with the prediction possibilities of a standard neural network and novel neural network were undertaken using data gathered from 333 blended samples. The results showed that neural networks outperformed the existing prediction models and can be successfully used to predict the colour of blends to an industry standard. The investigations of this thesis have shown that a sustainable colouration system using spun-dyed viscose blends is possible and that accurate colour predictions of these blends can be made

    Colour prediction for sustainable fibre blending

    Get PDF
    The blending of coloured fibre is explored as a sustainable method of colouration when coupled with sustainable fibre and dyeing choices such as spun-dyed Lenzing Viscose Austria. It was found that a selection of spun-dyed colours (primaries) can be used to create homogenous 4-colour blends when mixed in specific groups. The use of 4-colour blends ensures that the optimal amount of colours within a gamut are produced with the lowest possible number of primaries depending on the acceptable mean colour difference of the 4-colour blends. The acceptable mean colour difference of a blend (measured by averaging each pair of colour differences between the primaries in a blend) can be derived using example 4-colour blends and participant observations at a set viewing distance. Using MATLAB, a method of estimating the number of primaries required to fill a given gamut in CIELAB colour space was developed. Primaries can be distributed across CIELAB colour space and grouped into tetrahedral groups of four for blending. The mean colour difference of the tetrahedral 4-colour blends can be increased or decreased by varying the number of primaries within a gamut. It was also found that the maximum mean colour difference of blends in order for them to appear solid (when viewed at a specific viewing distance) was transferable to blends in knit form. Comparisons of existing blend prediction models with the prediction possibilities of a standard neural network and novel neural network were undertaken using data gathered from 333 blended samples. The results showed that neural networks outperformed the existing prediction models and can be successfully used to predict the colour of blends to an industry standard. The investigations of this thesis have shown that a sustainable colouration system using spun-dyed viscose blends is possible and that accurate colour predictions of these blends can be made

    Realistic visualisation of cultural heritage objects

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    This research investigation used digital photography in a hemispherical dome, enabling a set of 64 photographic images of an object to be captured in perfect pixel register, with each image illuminated from a different direction. This representation turns out to be much richer than a single 2D image, because it contains information at each point about both the 3D shape of the surface (gradient and local curvature) and the directionality of reflectance (gloss and specularity). Thereby it enables not only interactive visualisation through viewer software, giving the illusion of 3D, but also the reconstruction of an actual 3D surface and highly realistic rendering of a wide range of materials. The following seven outcomes of the research are claimed as novel and therefore as representing contributions to knowledge in the field: A method for determining the geometry of an illumination dome; An adaptive method for finding surface normals by bounded regression; Generating 3D surfaces from photometric stereo; Relationship between surface normals and specular angles; Modelling surface specularity by a modified Lorentzian function; Determining the optimal wavelengths of colour laser scanners; Characterising colour devices by synthetic reflectance spectra

    Segmentation d'images couleurs et multispectrales de la peau

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    La délimitation précise du contour des lésions pigmentées sur des images est une première étape importante pour le diagnostic assisté par ordinateur du mélanome. Cette thèse présente une nouvelle approche de la détection automatique du contour des lésions pigmentaires sur des images couleurs ou multispectrales de la peau. Nous présentons d'abord la notion de minimisation d'énergie par coupes de graphes en terme de Maxima A-Posteriori d'un champ de Markov. Après un rapide état de l'art, nous étudions l'influence des paramètres de l'algorithme sur les contours d'images couleurs. Dans ce cadre, nous proposons une fonction d'énergie basée sur des classifieurs performants (Machines à support de vecteurs et Forêts aléatoires) et sur un vecteur de caractéristiques calculé sur un voisinage local. Pour la segmentation de mélanomes, nous estimons une carte de concentration des chromophores de la peau, indices discriminants du mélanomes, à partir d'images couleurs ou multispectrales, et intégrons ces caractéristiques au vecteur. Enfin, nous détaillons le schéma global de la segmentation automatique de mélanomes, comportant une étape de sélection automatique des "graines" utiles à la coupure de graphes ainsi que la sélection des caractéristiques discriminantes. Cet outil est comparé favorablement aux méthodes classiques à base de coupure de graphes en terme de précision et de robustesse.Accurate border delineation of pigmented skin lesion (PSL) images is a vital first step in computer-aided diagnosis (CAD) of melanoma. This thesis presents a novel approach of automatic PSL border detection on color and multispectral skin images. We first introduce the concept of energy minimization by graph cuts in terms of maximum a posteriori estimation of a Markov random field (MAP-MRF framework). After a brief state of the art in interactive graph-cut based segmentation methods, we study the influence of parameters of the segmentation algorithm on color images. Under this framework, we propose an energy function based on efficient classifiers (support vector machines and random forests) and a feature vector calculated on a local neighborhood. For the segmentation of melanoma, we estimate the concentration maps of skin chromophores, discriminating indices of melanomas from color and multispectral images, and integrate these features in a vector. Finally, we detail an global framework of automatic segmentation of melanoma, which comprises two main stages: automatic selection of "seeds" useful for graph cuts and the selection of discriminating features. This tool is compared favorably to classic graph-cut based segmentation methods in terms of accuracy and robustness.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF

    Colour coded

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    This 300 word publication to be published by the Society of Dyers and Colourists (SDC) is a collection of the best papers from a 4-year European project that has considered colour from the perspective of both the arts and sciences.The notion of art and science and the crossovers between the two resulted in application and funding for cross disciplinary research to host a series of training events between 2006 and 2010 Marie Curie Conferences & Training Courses (SCF) Call Identifier: FP6-Mobility-4, Euros 532,363.80 CREATE – Colour Research for European Advanced Technology Employment. The research crossovers between the fields of art, science and technology was also a subject that was initiated through Bristol’s Festival if Ideas events in May 2009. The author coordinated and chaired an event during which the C.P Snow lecture “On Two Cultures’ (1959) was re-presented by Actor Simon Cook and then a lecture made by Raymond Tallis on the notion of the Polymath. The CREATE project has a worldwide impact for researchers, academics and scientists. Between January and October 2009, the site has received 221, 414 visits. The most popular route into the site is via the welcome page. The main groups of visitors originate in the UK (including Northern Ireland), Italy, France, Finland, Norway, Hungary, USA, Finland and Spain. A basic percentage breakdown of the traffic over ten months indicates: USA -15%; UK - 16%; Italy - 13%; France -12%; Hungary - 10%; Spain - 6%; Finland - 9%; Norway - 5%. The remaining approximate 14% of visitors are from other countries including Belgium, The Netherlands and Germany (approx 3%). A discussion group has been initiated by the author as part of the CREATE project to facilitate an ongoing dialogue between artists and scientists. http://createcolour.ning.com/group/artandscience www.create.uwe.ac.uk.Related papers to this research: A report on the CREATE Italian event: Colour in cultural heritage.C. Parraman, A. Rizzi, ‘Developing the CREATE network in Europe’, in Colour in Art, Design and Nature, Edinburgh, 24 October 2008.C. Parraman, “Mixing and describing colour”. CREATE (Training event 1), France, 2008

    Expanding Dimensionality in Cinema Color: Impacting Observer Metamerism through Multiprimary Display

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    Television and cinema display are both trending towards greater ranges and saturation of reproduced colors made possible by near-monochromatic RGB illumination technologies. Through current broadcast and digital cinema standards work, system designs employing laser light sources, narrow-band LED, quantum dots and others are being actively endorsed in promotion of Wide Color Gamut (WCG). Despite artistic benefits brought to creative content producers, spectrally selective excitations of naturally different human color response functions exacerbate variability of observer experience. An exaggerated variation in color-sensing is explicitly counter to the exhaustive controls and calibrations employed in modern motion picture pipelines. Further, singular standard observer summaries of human color vision such as found in the CIE’s 1931 and 1964 color matching functions and used extensively in motion picture color management are deficient in recognizing expected human vision variability. Many researchers have confirmed the magnitude of observer metamerism in color matching in both uniform colors and imagery but few have shown explicit color management with an aim of minimized difference in observer perception variability. This research shows that not only can observer metamerism influences be quantitatively predicted and confirmed psychophysically but that intentionally engineered multiprimary displays employing more than three primaries can offer increased color gamut with drastically improved consistency of experience. To this end, a seven-channel prototype display has been constructed based on observer metamerism models and color difference indices derived from the latest color vision demographic research. This display has been further proven in forced-choice paired comparison tests to deliver superior color matching to reference stimuli versus both contemporary standard RGB cinema projection and recently ratified standard laser projection across a large population of color-normal observers

    Colour Characterisation of LCD Display Systems

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    The main purpose of this research is to study the colour characterisation of digital display systems. Three distinct models for characterisation (GOG, PLCC and PLVC) are evaluated and compared and for two of these models (GOG and PLCC) two different sets of linearisation samples (either colour-ramps or grey-ramp samples) are used to perform the linearisation. To evaluate these models’ colorimetric measurements are made for 20 different display devices and colour characterization performance is reported as the main measure. Characterisation performance is calculated using several sets of samples including the widely used Macbeth ColorChecker chart and two new charts called Chart4 and Matlab60 (one of which was based on a method previously published by Cheung and Westland and another was based on a new method). A key aspect of this work is that all 256 levels of intensity were measured for the colour-ramps and for the grey-ramp linearisation samples for each of the 20 displays to allow subsampling of these data to explore the effect of the number of linearisation samples on characterisation performance. When the number of linearisation samples used was small (less than 10) the GOG model sometimes resulted in the smallest characterisation colour differences. However, for the PLCC and PLVC models performance tended to increase with the number of linearization samples and both of these models outperformed GOG with more 10 linearisation samples. For the PLCC model, better performance was usually obtained using the grey-ramp linearisation samples rather than using the colour-ramps linearization samples. It was possible, for each of the 20 displays, to reach average ab values that are less than 1.5 (ab <1.5, 90%) or ab < 1.0 (75%); however, the model that yields the best performance is difficult to ascertain in advance (a good strategy would be to evaluate all five models and select the one that performs best for the characterisation of any particular display). However, in the majority of cases, lowest colour differences (ab) were obtained using the PLCC model and all 256 of the grey-ramp samples for linearisation. This work has compared the performance of five different models using a large number of displays and has allowed a number of recommendations to be made about display characterisation. Although the majority of the work in this thesis was based on stationary displays the effect of motion on characterization performance was also explored. This is important since moving images are now commonplace in many applications. The results showed that a moving background has a small, but statistically significant, effect on the colour of patches
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