Evaluation and improvement of the workflow of digital imaging of fine art reproduction in museums

Fine arts refer to a broad spectrum of art formats, ie~painting, calligraphy, photography, architecture, and so forth. Fine art reproductions are to create surrogates of the original artwork that are able to faithfully deliver the aesthetics and feelings of the original. Traditionally, reproductions of fine art are made in the form of catalogs, postcards or books by museums, libraries, archives, and so on (hereafter called museums for simplicity). With the widespread adoption of digital archiving in museums, more and more artwork is reproduced to be viewed on a display. For example, artwork collections are made available through museum websites and Google Art Project for art lovers to view on their own displays. In the thesis, we study the fine art reproduction of paintings in the form of soft copy viewed on displays by answering four questions: (1) what is the impact of the viewing condition and original on image quality evaluation? (2) can image quality be improved by avoiding visual editing in current workflows of fine art reproduction? (3) can lightweight spectral imaging be used for fine art reproduction? and (4) what is the performance of spectral reproductions compared with reproductions by current workflows? We started with evaluating the perceived image quality of fine art reproduction created by representative museums in the United States under controlled and uncontrolled environments with and without the presence of the original artwork. The experimental results suggest that the image quality is highly correlated with the color accuracy of the reproduction only when the original is present and the reproduction is evaluated on a characterized display. We then examined the workflows to create these reproductions, and found that current workflows rely heavily on visual editing and retouching (global and local color adjustments on the digital reproduction) to improve the color accuracy of the reproduction. Visual editing and retouching can be both time-consuming and subjective in nature (depending on experts\u27 own experience and understanding of the artwork) lowering the efficiency of artwork digitization considerably. We therefore propose to improve the workflow of fine art reproduction by (1) automating the process of visual editing and retouching in current workflows based on RGB acquisition systems and by (2) recovering the spectral reflectance of the painting with off-the-shelf equipment under commonly available lighting conditions. Finally, we studied the perceived image quality of reproductions created by current three-channel (RGB) workflows with those by spectral imaging and those based on an exemplar-based method

Fluorescence and Adaptation of Color Images

Color plays a vitally important role in the world we live in. It surrounds us everywhere we go. Achromatic life, restricted to black, white and grey, is extremely dull. Color fascinates artists, for it adds enormously to aesthetic appreciation, directly invoking thoughts, emotions and feelings. Color fascinates scientists. For decades, scientists in color imaging, printing and digital photography have striven to satisfy increasing demands for accuracy in color reproduc- tion. Fluorescence is a very common phenomenon observed in many objects such as gems and corals, writing paper, clothes, and even laundry detergent. Traditional color imaging algo- rithms exclude fluorescence by assuming that all objects have only an ordinary reflective com- ponent. The first part of the thesis shows that the color appearance of an object with both reflective and fluorescent components can be represented as a linear combination of the two components. A linear model allows us to separate the two components using independent component analysis (ICA). We can then apply different algorithms to each component, and combine the results to form images with more accurate color. Displaying color images accurately is as important as reproducing color images accurately. The second part of the thesis presents a new, practical model for displaying color images on self-luminous displays such as LCD monitors. It shows that the model accounts for human visual system’s mixed adaptation condition and produces results comparable to many existing algorithms

Modelling of colour appearance of textured colours and smartphones using CIECAM02

The international colour committee recommended a colour appearance model, CIECAM02 in 2002, to help to predict colours under various viewing conditions from a colour appearance point of view, which has the accuracy of an averaged observer. In this research, an attempt is made to extend this model to predict colours on mobile telephones, which is not covered in the model. Despite the limited size and capacity of a mobile telephone, the urge to apply it to meet quotidian needs has never been unencumbered due to its appealing appearance, versatility, and readiness, such as viewing/taking pictures and shopping online. While a smartphone can act as a mini-computer, it does not always offer the same functionality as a desktop computer. For example, the RGB values on a smartphone normally cannot be modified nor can white balance be checked. As a result, performing online shopping using a mobile telephone can be difficult, especially when buying colour sensitive items. Therefore, this research takes an initiative to investigate the variations of colours for a number of smartphones while making an effort to predict their colour appearance using CIECAM02, benefiting both telephone users and makers. This thesis studies the Apple iPhone 5, LG Nexus 4, Samsung, and Huawei models, and compares their performance with a CRT colour monitor that has been calibrated using the D65 standard, to be consistent with the normal way of viewing online colours. As expected, all the telephones tested present more colourful images than a CRT. Work was also undertaken to investigate colours with a degree of texture. It was found that, on CRT monitors, a colour with a texture appears to be darker but more colourful to a human observer. Linear modifications have been proposed and implemented to the CIECAM02 model to accommodate these textured colours

Crowd-sourced data and its applications for new algorithms in photographic imaging

This thesis comprises two main themes. The first of these is concerned primarily with the validity and utility of data acquired from web-based psychophysical experiments. In recent years web-based experiments, and the crowd-sourced data they can deliver, have been rising in popularity among the research community for several key reasons – primarily ease of administration and easy access to a large population of diverse participants. However, the level of control with which traditional experiments are performed, and the severe lack of control we have over web-based alternatives may lead us to believe that these benefits come at the cost of reliable data. Indeed, the results reported early in this thesis support this assumption. However, we proceed to show that it is entirely possible to crowd-source data that is comparable with lab-based results. The second theme of the thesis explores the possibilities presented by the use of crowd-sourced data, taking a popular colour naming experiment as an example. After using the crowd-sourced data to construct a model for computational colour naming, we consider the value of colour names as image descriptors, with particular relevance to illuminant estimation and object indexing. We discover that colour names represent a particularly useful quantisation of colour space, allowing us to construct compact image descriptors for object indexing. We show that these descriptors are somewhat tolerant to errors in illuminant estimation and that their perceptual relevance offers even further utility. We go on to develop a novel algorithm which delivers perceptually-relevant, illumination-invariant image descriptors based on colour names

Algorithms for the enhancement of dynamic range and colour constancy of digital images & video

One of the main objectives in digital imaging is to mimic the capabilities of the human eye, and perhaps, go beyond in certain aspects. However, the human visual system is so versatile, complex, and only partially understood that no up-to-date imaging technology has been able to accurately reproduce the capabilities of the it. The extraordinary capabilities of the human eye have become a crucial shortcoming in digital imaging, since digital photography, video recording, and computer vision applications have continued to demand more realistic and accurate imaging reproduction and analytic capabilities. Over decades, researchers have tried to solve the colour constancy problem, as well as extending the dynamic range of digital imaging devices by proposing a number of algorithms and instrumentation approaches. Nevertheless, no unique solution has been identified; this is partially due to the wide range of computer vision applications that require colour constancy and high dynamic range imaging, and the complexity of the human visual system to achieve effective colour constancy and dynamic range capabilities. The aim of the research presented in this thesis is to enhance the overall image quality within an image signal processor of digital cameras by achieving colour constancy and extending dynamic range capabilities. This is achieved by developing a set of advanced image-processing algorithms that are robust to a number of practical challenges and feasible to be implemented within an image signal processor used in consumer electronics imaging devises. The experiments conducted in this research show that the proposed algorithms supersede state-of-the-art methods in the fields of dynamic range and colour constancy. Moreover, this unique set of image processing algorithms show that if they are used within an image signal processor, they enable digital camera devices to mimic the human visual system s dynamic range and colour constancy capabilities; the ultimate goal of any state-of-the-art technique, or commercial imaging device

A Study of Colour Rendering in the In-Camera Imaging Pipeline

Consumer cameras such as digital single-lens reflex camera (DSLR) and smartphone cameras have onboard hardware that applies a series of processing steps to transform the initial captured raw sensor image to the final output image that is provided to the user. These processing steps collectively make up the in-camera image processing pipeline. This dissertation aims to study the processing steps related to colour rendering which can be categorized into two stages. The first stage is to convert an image's sensor-specific raw colour space to a device-independent perceptual colour space. The second stage is to further process the image into a display-referred colour space and includes photo-finishing routines to make the image appear visually pleasing to a human. This dissertation makes four contributions towards the study of camera colour rendering. The first contribution is the development of a software-based research platform that closely emulates the in-camera image processing pipeline hardware. This platform allows the examination of the various image states of the captured image as it is processed from the sensor response to the final display output. Our second contribution is to demonstrate the advantage of having access to intermediate image states within the in-camera pipeline that provide more accurate colourimetric consistency among multiple cameras. Our third contribution is to analyze the current colourimetric method used by consumer cameras and to propose a modification that is able to improve its colour accuracy. Our fourth contribution is to describe how to customize a camera imaging pipeline using machine vision cameras to produce high-quality perceptual images for dermatological applications. The dissertation concludes with a summary and future directions

Color Analysis and Image Processing Applied in Agriculture

Color and appearance are perhaps the first attributes that attract us to a fruit or vegetable. Since the appearance of the product generally determines whether a product is accepted or rejected, measuring the color characteristics becomes an important task. To carry out the analysis of this key attribute for agriculture, it is recommended to use an artificial vision system to capture the images of the samples and then to process them by applying colorimetric routines to extract color parameters in an efficient and nondestructive manner, which makes it a suitable tool for a wide range of applications. The purpose of this chapter is to give an overview on recent development of image processing applied to color analysis from horticultural products, more specifically the practical usage of color image analysis in agriculture. As an example, quantitative values of color are extracted from Habanero Chili Peppers using image processing; the images from the samples were obtained using a desktop configuration of machine vision system. The material presented should be useful for students starting on the field, as well as for researchers looking for state-of-the-art studies and practical applications

Colorimetric and spectral analysis of rock art by means of the characterization of digital sensors

Tesis por compendio[ES] Las labores de documentación de arte rupestre son arduas y delicadas, donde el color desempeña un papel fundamental, proporcionando información vital a nivel descriptivo, técnico y cuantitativo . Tradicionalmente los métodos de documentación en arqueología quedaban restringidos a procedimientos estrictamente subjetivos. Sin embargo, esta metodología conlleva limitaciones prácticas y técnicas, afectando a los resultados obtenidos en la determinación del color. El empleo combinado de técnicas geomáticas, como la fotogrametría o el láser escáner, junto con técnicas de procesamiento de imágenes digitales, ha supuesto un notable avance. El problema es que, aunque las imágenes digitales permiten capturar el color de forma rápida, sencilla, y no invasiva, los datos RGB registrados por la cámara no tienen un sentido colorimétrico riguroso. Se requiere la aplicación de un proceso riguroso de tranformación que permita obtener datos fidedignos del color a través de imágenes digitales. En esta tesis se propone una solución científica novedosa y de vanguardia, en la que se persigue integrar el análisis espectrofotométrico y colorimétrico como complemento a técnicas fotogramétricas que permitan una mejora en la identificación del color y representación de pigmentos con máxima fiabilidad en levantamientos, modelos y reconstrucciones tridimensionales (3D). La metodología propuesta se basa en la caracterización colorimétrica de sensores digitales, que es de novel aplicación en pinturas rupestres. La caracterización pretende obtener las ecuaciones de transformación entre los datos de color registrados por la cámara, dependientes del dispositivo, y espacios de color independientes, de base física, como los establecidos por la Commission Internationale de l'Éclairage (CIE). Para el tratamiento de datos colorimétricos y espectrales se requiere disponer de un software de características técnicas muy específicas. Aunque existe software comercial, lo cierto es que realizan por separado el tratamiento digital de imágenes y las operaciones colorimétricas. No existe software que integre ambas, ni que además permita llevar a cabo la caracterización. Como aspecto fundamental, presentamos en esta tesis el software propio desarrollado, denominado pyColourimetry, siguiendo las recomendaciones publicadas por la CIE, de código abierto, y adaptado al flujo metodológico propuesto, de modo que facilite la independencia y el progreso científico sin ataduras comerciales, permitiendo el tratamiento de datos colorimétricos y espectrales, y confiriendo al usuario pleno control del proceso y la gestión de los datos obtenidos. Adicinalmente, en este estudio se expone el análisis de los principales factores que afectan a la caracterización tales como el sensor empleado, los parámetros de la cámara durante la toma, la iluminación, el modelo de regresión, y el conjunto de datos empleados como entrenamiento del modelo. Se ha aplicado un modelo de regresión basado en procesos Gaussianos, y se ha comparado con los resultados obtenidos mediante polinomios. También presentamos un nuevo esquema de trabajo que permite la selección automática de muestras de color, adaptado al rango cromático de la escena, que se ha denominado P-ASK, basado en el algoritmo de clasificación K-means. Los resultados obtenidos en esta tesis demuestran que el proceso metodológico de caracterización propuesto es altamente aplicable en tareas de documentación y preservación del patrimonio cultural en general, y en arte rupestre en particular. Se trata de una metodología de bajo coste, no invasiva, que permite obtener el registro colorimétrico de escenas completas. Una vez caracterizada, una cámara digital convencional puede emplearse para la determinación del color de forma rigurosa, simulando un colorímetro, lo que permitirá trabajar en un espacio de color de base física, independiente del dispositivo y comparable con[CA] Les tasques de documentació gràfica d'art rupestre són àrdues i delicades, on el color compleix un paper fonamental, proporcionant informació vital a nivell descriptiu, t\`ecnic i quantitatiu.Tradicionalment els mètodes de documentació en arqueologia quedaven restringits a procediments estrictament subjectius, comportant limitacions pràctiques i tècniques, afectant els resultats obtinguts en la determinació de la color. L'ús combinat de tècniques geomàtiques, com la fotogrametria o el làser escàner, juntament amb tècniques de processament i realç d'imatges digitals, ha suposat un notable avanç. Tot i que les imatges digitals permeten capturar el color de forma ràpida, senzilla, i no invasiva, les dades RGB proporcionades per la càmera no tenen un sentit colorimètric rigorós. Es requereix l'aplicació d'un procés rigorós de transformació que permeti obtenir dades fidedignes de la color a través d'imatges digitals. En aquesta tesi es proposa una solució científica innovadora i d'avantguarda, en la qual es persegueix integrar l'anàlisi espectrofotomètric i colorimètric com a complement a tècniques fotogramètriques que permetin una millora en la identificació de la color i representació de pigments amb màxima fiabilitat en aixecaments, models i reconstruccions tridimensionals 3D. La metodologia proposada es basa en la caracterització colorimètrica de sensors digitals, que és de novell aplicació en pintures rupestres. La caracterització pretén obtenir les equacions de transformació entre les dades de color registrats per la càmera, dependents d'el dispositiu, i espais de color independents, de base física, com els establerts per la Commission Internationale de l'Éclairage (CIE). Per al tractament de dades colorimètriques i espectrals de forma rigorosa es requereix disposar d'un programari de característiques tècniques molt específiques. Encara que hi ha programari comercial, fan per separat el tractament digital d'imatges i les operacions colorimètriques. No hi ha programari que integri totes dues, ni que permeti dur a terme la caracterització. Com a aspecte addicional i fonamental, vam presentar el programari propi que s'ha desenvolupat, denominat pyColourimetry, segons les recomanacions publicades per la CIE, de codi obert, i adaptat al flux metodológic proposat, de manera que faciliti la independència i el progrés científic sense lligams comercials, permetent el tractament de dades colorimètriques i espectrals, i conferint a l'usuari ple control del procés i la gestió de les dades obtingudes. A més, s'exposa l'anàlisi dels principals factors que afecten la caracterització tals com el sensor emprat, els paràmetres de la càmera durant la presa, il¿luminació, el model de regressió, i el conjunt de dades emprades com a entrenament d'el model. S'ha aplicat un model de regressió basat en processos Gaussians, i s'han comparat els resultats obtinguts mitjançant polinomis. També vam presentar un nou esquema de treball que permet la selecció automàtica de mostres de color, adaptat a la franja cromàtica de l'escena, que s'ha anomenat P-ASK, basat en l'algoritme de classificació K-means. Els resultats obtinguts en aquesta tesi demostren que el procés metodològic de caracterització proposat és altament aplicable en tasques de documentació i preservació de el patrimoni cultural en general, i en art rupestre en particular. Es tracta d'una metodologia de baix cost, no invasiva, que permet obtenir el registre colorimètric d'escenes completes. Un cop caracteritzada, una càmera digital convencional pot emprar-se per a la determinació de la color de forma rigorosa, simulant un colorímetre, el que permetrà treballar en un espai de color de base física, independent d'el dispositiu i comparable amb dades obtingudes mitjançant altres càmeres que tambè estiguin caracteritzades.[EN] Cultural heritage documentation and preservation is an arduous and delicate task in which color plays a fundamental role. The correct determination of color provides vital information on a descriptive, technical and quantitative level. Classical color documentation methods in archaeology were usually restricted to strictly subjective procedures. However, this methodology has practical and technical limitations, affecting the results obtained in the determination of color. Nowadays, it is frequent to support classical methods with geomatics techniques, such as photogrammetry or laser scanning, together with digital image processing. Although digital images allow color to be captured quickly, easily, and in a non-invasive way, the RGB data provided by the camera does not itself have a rigorous colorimetric sense. Therefore, a rigorous transformation process to obtain reliable color data from digital images is required. This thesis proposes a novel technical solution, in which the integration of spectrophotometric and colorimetric analysis is intended as a complement to photogrammetric techniques that allow an improvement in color identification and representation of pigments with maximum reliability in 3D surveys, models and reconstructions. The proposed methodology is based on the colorimetric characterization of digital sensors, which is of novel application in cave paintings. The characterization aims to obtain the transformation equations between the device-dependent color data recorded by the camera and the independent, physically-based color spaces, such as those established by the Commission Internationale de l'Éclairage (CIE). The rigorous processing of color and spectral data requires software packages with specific colorimetric functionalities. Although there are different commercial software options, they do not integrate the digital image processing and colorimetric computations together. And more importantly, they do not allow the camera characterization to be carried out. Therefore, as a key aspect in this thesis is our in-house pyColourimetry software that was developed and tested taking into account the recommendations published by the CIE. pyColourimetry is an open-source code, independent without commercial ties; it allows the treatment of colorimetric and spectral data and the digital image processing, and gives full control of the characterization process and the management of the obtained data to the user. On the other hand, this study presents a further analysis of the main factors affecting the characterization, such as the camera built-in sensor, the camera parameters, the illuminant, the regression model, and the data set used for model training. For computing the transformation equations, the literature recommends the use of polynomial equations as a regression model. Thus, polynomial models are considered as a starting point in this thesis. Additionally, a regression model based on Gaussian processes has been applied, and the results obtained by means of polynomials have been compared. Also, a new working scheme was reported which allows the automatic selection of color samples, adapted to the chromatic range of the scene. This scheme is called P-ASK, based on the K-means classification algorithm. The results achieved in this thesis show that the proposed framework for camera characterization is highly applicable in documentation and conservation tasks in general cultural heritage applications, and particularly in rock art painting. It is a low-cost and non-invasive methodology that allows for the colorimetric recording from complete image scenes. Once characterized, a conventional digital camera can be used for rigorous color determination, simulating a colorimeter. Thus, it is possible to work in a physical color space, independent of the device used, and comparable with data obtained from other cameras that are also characterized.Thanks to the Universitat Politècnica de València for the FPI scholarshipMolada Tebar, A. (2020). Colorimetric and spectral analysis of rock art by means of the characterization of digital sensors [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160386TESISCompendi

Multispectral Imaging For Face Recognition Over Varying Illumination

This dissertation addresses the advantage of using multispectral narrow-band images over conventional broad-band images for improved face recognition under varying illumination. To verify the effectiveness of multispectral images for improving face recognition performance, three sequential procedures are taken into action: multispectral face image acquisition, image fusion for multispectral and spectral band selection to remove information redundancy. Several efficient image fusion algorithms are proposed and conducted on spectral narrow-band face images in comparison to conventional images. Physics-based weighted fusion and illumination adjustment fusion make good use of spectral information in multispectral imaging process. The results demonstrate that fused narrow-band images outperform the conventional broad-band images under varying illuminations. In the case where multispectral images are acquired over severe changes in daylight, the fused images outperform conventional broad-band images by up to 78%. The success of fusing multispectral images lies in the fact that multispectral images can separate the illumination information from the reflectance of objects which is impossible for conventional broad-band images. To reduce the information redundancy among multispectral images and simplify the imaging system, distance-based band selection is proposed where a quantitative evaluation metric is defined to evaluate and differentiate the performance of multispectral narrow-band images. This method is proved to be exceptionally robust to parameter changes. Furthermore, complexity-guided distance-based band selection is proposed using model selection criterion for an automatic selection. The performance of selected bands outperforms the conventional images by up to 15%. From the significant performance improvement via distance-based band selection and complexity-guided distance-based band selection, we prove that specific facial information carried in certain narrow-band spectral images can enhance face recognition performance compared to broad-band images. In addition, both algorithms are proved to be independent to recognition engines. Significant performance improvement is achieved by proposed image fusion and band selection algorithms under varying illumination including outdoor daylight conditions. Our proposed imaging system and image processing algorithms lead to a new avenue of automatic face recognition system towards a better recognition performance than the conventional peer system over varying illuminations