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

Optimising Light Source Spectrum to Reduce the Energy Absorbed by Objects

Light is used to illuminate objects in the built environment. Humans can only observe light reflected from an object. Light absorbed by an object turns into heat and does not contribute to visibility. Since the spectral output of the new lighting technologies can be tuned, it is possible to imagine a lighting system that detects the colours of objects and emits customised light to minimise the absorbed energy. Previous optimisation studies investigated the use of narrowband LEDs to maximise the efficiency and colour quality of a light source. While these studies aimed to tune a white light source for general use, the lighting system proposed here minimises the energy consumed by lighting by detecting colours of objects and emitting customised light onto each coloured part of the object. This thesis investigates the feasibility of absorption-minimising light source spectra and their impact on the colour appearance of objects and energy consumption. Two computational studies were undertaken to form the theoretical basis of the absorption-minimising light source spectra. Computational simulations show that the theoretical single-peak spectra can lower the energy consumption up to around 38 % to 62 %, and double-peak test spectra can result in energy savings up to 71 %, without causing colour shifts. In these studies, standard reference illuminants, theoretical test spectra and coloured test samples were used. These studies are followed by the empirical evidence collected from two psychophysical experiments. Data from the experiments show that observers find the colour appearance of objects equally natural and attractive under spectrally optimised spectra and reference white light sources. An increased colour difference, to a certain extent, is found acceptable, which allows even higher energy savings. However, the translucent nature of some objects may negatively affect the results

A Physiological and Psychometric Evaluation of Human Subconscious Visual Response and Its Application in Health Promoting Lighting.

Subconscious vision is a recent focus of the vision science community, brought on by the discovery of a previously unknown photoreceptor in the retina dedicated to driving non-image-forming responses, intrinsically photosensitive retinal ganglion cells (ipRGCs). In addition to accepting inputs from rod and cone photoreceptors, ipRGCs contain their own photopigment, melanopsin, and are considered true photoreceptors. ipRGCs drive various non-image-forming photoresponses, including circadian photoentrainment, melatonin suppression, and pupil constriction. In order to understand more about ipRGC function in humans, we studied its sensitivity to light stimuli in the evening and day. First, we measured the sensitivity threshold of melatonin suppression at night. Using a protocol that enhances data precision, we have found the threshold for human melatonin suppression to be two orders of magnitude lower than previously reported. This finding has far-reaching implications since there is mounting evidence that nocturnal activation of the circadian system can be harmful. Paradoxically, ipRGCs are understimulated during the day. Optimizing daytime non-image-forming photostimulation has health benefits, such as increased alertness, faster reaction times, better sleep quality, and treatment of depression. In order to enhance ipRGC excitation, we aimed to circumvent adaptation (i.e. desensitization) of the photoresponse by using flickering instead of steady light. We find that properly timed flickering light enhances pupillary light reflex significantly when compared to steady light with 9-fold more energy density. Employing our findings, a new form of LED light is proposed to enhance subconscious visual responses at a typical indoor illuminance level. Using the silent substitution technique, a melanopsin-selective flicker is introduced into the light. A linear optimization algorithm is used to maximize the contrast of the subconscious, melanopsin-based response function while keeping conscious, cone-driven responses to the pulsing light fixed. Additional boundary conditions utilizing test color samples as an environmental mimic are introduced to limit the amount of perceived color change in a simulated environment. Two examples of lights are given to illustrate potential applications for general illumination and therapeutic purposes. For the lighting and electronics industry, we hope our study of subconscious-stimulative thresholds at night will better inform their design guidelines for health conscious products.PhDMacromolecular Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133226/1/garenv_1.pd

Chromatic filters for color vision deficiencies

Dissertação de mestrado em Optometria AvançadaAbout 10% of the population have some form of color vision deficiency. One of the most sever deficiencies is dichromacy. Dichromacy impairs color vision and impoverishes the discrimination of surface colors in natural scenes. Computational estimates based on hyperspectral imaging data from natural scenes suggest that dichromats can discriminate only about 7% of the number of colors discriminated by normal observers on natural scenes. These estimates, however, assume that the colors are equally frequent. Yet, pairs of color confused by dichromats may be rare and thus have small impact on the overall perceived chromatic diversity. By using an experimental setup that allows visual comparation between different spectra selected form hyperspectral images of natural scenes, it was estimated that the number of pairs that dichromats could discriminate was almost 70% of those discriminated by normal observers, a fraction much higher than anticipated from estimates of the number of discernible colors on natural scenes. Therefore, it may be rare for a dichromat to encounter two objects of different colors that he confounds. Thus, chromatic filters for color vision deficiencies intended to improve all colors in general may constitute low practical value. On this work it is proposed a method to compute filters specialized for a specific color-detection task, by taking into account the user’s color vision type, the local illuminant, and the reflectance spectra of the objects intended to be distinguished during that task. This method was applied on a case of a medical practitioner with protanopia to idealize a filter to improve detection of erythema on the skin of its patients. The filter improved the mean color difference between erythema and normal skin by 44%.Cerca de 10% da população possui alguma forma de deficiência de visão de cor. Uma das deficiências mais severas é a dicromacia. Dicromacia prejudica a visão das cores e empobrece a discriminação de superficies coloridas em cenas naturais. Estimativas computacionais baseadas em dados de imagens hiperespectrais de cenas naturais sugerem que dicromatas só pode discriminar cerca de 7% do número de cores discriminadas por observadores normais em cenas naturais. Estas estimativas, no entanto, assumem que todas as cores são igualmente frequentes. Contudo, pares de cores confundidos por dichromats podem ser raros e, portanto, têm pequeno impacto na diversidade cromática global percebida. Ao usar uma montagem experimental que permite comparação visual entre espectros diferentes selecionados a partir de imagens hiperespectrais de cenas naturais, estimou-se que o número de pares que dicromatas poderiam discriminar era quase 70% dos discriminados por observadores normais, uma fração muito maior do que o antecipado a partir de estimativas do número de cores percebidas em cenas naturais. Portanto, pode ser raro para um dicromat para encontrar dois objetos cujas cores ele confunda. Assim, filtros cromático para deficiências de visão das cores pretendidos para melhorar todas as cores em geral podem constituir baixo valor prático. Neste trabalho é proposto um método para calcular filtros especializados para uma tarefa específica de detecção de cor, tendo em conta o tipo de visão de cor do utilizador, o iluminante local, e os espectros de reflectancia dos objetos pretendidos a serem distinguidos durante essa tarefa. Este método foi aplicado em um caso de um médico com Protanopia para idealizar um filtro para melhorar a detecção de eritema na pele de seus pacientes. O filtro melhorou a diferença média de cor entre o eritema e a pele normal por 44%

Investigations into colour constancy by bridging human and computer colour vision

PhD ThesisThe mechanism of colour constancy within the human visual system has long been of great interest to researchers within the psychophysical and image processing communities. With the maturation of colour imaging techniques for both scientific and artistic applications the importance of colour capture accuracy has consistently increased. Colour offers a great deal more information for the viewer than grayscale imagery, ranging from object detection to food ripeness and health estimation amongst many others. However these tasks rely upon the colour constancy process in order to discount scene illumination to allow these tasks to be carried out. Psychophysical studies have attempted to uncover the inner workings of this mechanism, which would allow it to be reproduced algorithmically. This would allow the development of devices which can eventually capture and perceive colour in the same manner as a human viewer. These two communities have approached this challenge from opposite ends, and as such very different and largely unconnected approaches. This thesis investigates the development of studies and algorithms which bridge the two communities. Utilising findings from psychophysical studies as inspiration to firstly improve an existing image enhancement algorithm. Results are then compared to state of the art methods. Then, using further knowledge, and inspiration, of the human visual system to develop a novel colour constancy approach. This approach attempts to mimic and replicate the mechanism of colour constancy by investigating the use of a physiological colour space and specific scene contents to estimate illumination. Performance of the colour constancy mechanism within the visual system is then also investigated. The performance of the mechanism across different scenes and commonly and uncommonly encountered illuminations is tested. The importance of being able to bridge these two communities, with a successful colour constancy method, is then further illustrated with a case study investigating the human visual perception of the agricultural produce of tomatoes.EPSRC DTA: Institute of Neuroscience, Newcastle University

Illuminant Estimation By Deep Learning

Computational color constancy refers to the problem of estimating the color of the scene illumination in a color image, followed by color correction of the image through a white balancing process so that the colors of the image will be viewed as if the image was captured under a neutral white light source, and hence producing a plausible natural looking image. The illuminant estimation part is still a challenging task due to the ill-posed nature of the problem, and many methods have been proposed in the literature while each follows a certain approach in an attempt to improve the performance of the Auto-white balancing system for accurately estimating the illumination color for better image correction. These methods can typically be categorized into static-based and learning-based methods. Most of the proposed methods follow the learning-based approach because of its higher estimation accuracy compared to the former which relies on simple assumptions. While many of those learning-based methods show a satisfactory performance in general, they are built upon extracting handcrafted features which require a deep knowledge of the color image processing. More recent learning-based methods have shown higher improvements in illuminant estimation through using Deep Learning (DL) systems presented by the Convolutional Neural Networks (CNNs) that automatically learned to extract useful features from the given image dataset. In this thesis, we present a highly effective Deep Learning approach which treats the illuminant estimation problem as an illuminant classification task by learning a Convolutional Neural Network to classify input images belonging to certain pre-defined illuminant classes. Then, the output of the CNN which is in the form of class probabilities is used for computing the illuminant color estimate. Since training a deep CNN requires large number of training examples to avoid the “overfitting” problem, most of the recent CNN-based illuminant estimation methods attempted to overcome the limited number of images in the benchmark illuminant estimation dataset by sampling input images to multiple smaller patches as a way of data augmentation, but this can adversely affect the CNN training performance because some of these patches may not contain any semantic information and therefore, can be considered as noisy examples for the CNN that can lead to estimation ambiguity. However, in this thesis, we propose a novel approach for dataset augmentation through synthesizing images with different illuminations using the ground-truth illuminant color of other training images, which enhanced the performance of the CNN training compared to similar previous methods. Experimental results on the standard illuminant estimation benchmark dataset show that the proposed solution outperforms most of the previous illuminant estimation methods and show a competitive performance to the state-of-the-art methods

Improving field management by machine vision - a review

Growing population of people around the world and thus increasing demand to food products as well as high tendency for declining the cost of operations and environmental preserving cares intensify inclination toward the application of variable rate systems for agricultural treatments, in which machine vision as a powerful appliance has been paid vast attention by agricultural researchers and farmers as this technology consumers. Various applications have introduced for machine vision in different fields of agricultural and food industry till now that confirms the high potential of this approach for inspection of different parameters affecting productivity. Computer vision has been utilized for quantification of factors affecting crop growth in field; such as, weed, irrigation, soil quality, plant nutrients and fertilizers in several cases. This paper presents some of these successful applications in addition to representing an introduction to machine vision