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

Digital color image processing and psychophysics within the framework of a human visual model

Journal ArticleA three-dimensional homomorphic model of human color vision based on neurophysiological and psychophysical evidence is presented. This model permits the quantitative definition of perceptually important parameters such as brightness. saturation, huo and strength. By modelling neural interaction in the human visual system as three linear filters operating on perceptual quantities, this model accounts for the automatic gain control properties of the eye and for brightness and color contrast effects. In relation to color contrast effects, a psychophysical experiment was performed. It utilized a high quality color television monitor driven by a general purpose digital computer. This experiment, based on the cancellation by human subjects of simultaneous color contrast illusions, allowed the measurement of the low spatial frequency part of the frequency responses of the filters operating on the two chromatic channels of the human visual system. The experiment is described and its results are discussed. Next, the model is shown to provide a suitable framework in which to perform digital images processing tasks. First, applications to color image enhancement are presented and discussed in relation to photographic masking techniques and to the handling of digital color images. Second, application of the model to the definition of a distortion measure between color images (in the sense of Shannon's rate-distortion theory), meaningful in terms of human evaluation, is shown. Mathematical norms in the "perceptual" space defined by the model are used to evaluate quantitatively the amount of subjective distortion present in artificially distorted color presented. Results of a coding experiment yielding digital color images coded at an average bit rate of 1 bit/pixel are shown. Finally conclusions are drawn about the implications of this research from the standpoints of psychophysics and of digital image processing

Traffic sign recognition based on human visual perception.

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions

Biological object representation for identification

This thesis is concerned with the problem of how to represent a biological object for computerised identification. Images of biological objects have been generally characterised by shapes and colour patterns in the biology domain and the pattern recognition domain. Thus, it is necessary to represent the biological object using descriptors for the shape and the colour pattern. The basic requirements which a description method should satisfy are those such as invariance of scale, location and orientation of an object; direct involvement in the identification stage; easy assessment of results. The major task to deal with in this thesis was to develop a shape-description method and a colour-pattern description method which could accommodate all of the basic requirements and could be generally applied in both domains. In the colour-pattern description stage, an important task was to segment a colour image into meaningful segments. The most efficient method for this task is to apply Cluster Analysis. In the image analysis and pattern recognition domains, the majority of approaches to this method have been constrained by the problem of dealing with inordinate amounts of data, i.e. a large number of pixels of an image. In order to directly apply Cluster Analysis to the colour image segmentation, data structure, the Auxiliary Means is developed in this thesis

자율주행을 위한 카메라 기반 거리 측정 및 측위

학위논문 (박사)-- 서울대학교 대학원 공과대학 전기·컴퓨터공학부, 2017. 8. 서승우.Automated driving vehicles or advanced driver assistance systems (ADAS) have continued to be an important research topic in transportation area. They can promise to reduce road accidents and eliminate traffic congestions. Automated driving vehicles are composed of two parts. On-board sensors are used to observe the environments and then, the captured sensor data are processed to interpret the environments and to make appropriate driving decisions. Some sensors have already been widely used in existing driver-assistance systems, e.g., camera systems are used in lane-keeping systems to recognize lanes on roadsradars (Radio Detection And Ranging) are used in adaptive cruise systems to measure the distance to a vehicle ahead such that a safe distance can be guaranteedLIDAR (Light Detection And Ranging) sensors are used in the autonomous emergency braking system to detect other vehicles or pedestrians in the vehicle path to avoid collisionaccelerometers are used to measure vehicle speed changes, which are especially useful for air-bagswheel encoder sensors are used to measure wheel rotations in a vehicle anti-lock brake system and GPS sensors are embedded on vehicles to provide the global positions of the vehicle for path navigation. In this dissertation, we cover three important application for automated driving vehicles by using camera sensors in vehicular environments. Firstly, precise and robust distance measurement is one of the most important requirements for driving assistance systems and automated driving systems. We propose a new method for providing accurate distance measurements through a frequency-domain analysis based on a stereo camera by exploiting key information obtained from the analysis of captured images. Secondly, precise and robust localization is another important requirement for safe automated driving. We propose a method for robust localization in diverse driving situations that measures the vehicle positions using a camera with respect to a given map for vision based navigation. The proposed method includes technology for removing dynamic objects and preserving features in vehicular environments using a background model accumulated from previous frames and we improve image quality using illuminant invariance characteristics of the log-chromaticity. We also propose a vehicle localization method using structure tensor and mutual information theory. Finally, we propose a novel algorithm for estimating the drivable collision-free space for autonomous navigation of on-road vehicles. In contrast to previous approaches that use stereo cameras or LIDAR, we solve this problem using a sensor fusion of cameras and LIDAR.1 Introduction 1 1.1 Background and Motivations 1 1.2 Contributions and Outline of the Dissertation 3 1.2.1 Accurate Object Distance Estimation based on Frequency-Domain Analysis with a Stereo Camera 3 1.2.2 Visual Map Matching based on Structural Tensor and Mutual Information using 3D High Resolution Digital Map 3 1.2.3 Free Space Computation using a Sensor Fusion of LIDAR and RGB camera in Vehicular Environment 4 2 Accurate Object Distance Estimation based on Frequency-Domain Analysis with a Stereo Camera 5 2.1 Introduction 5 2.2 Related Works 7 2.3 Algrorithm Description 10 2.3.1 Overall Procedure 10 2.3.2 Preliminaries 12 2.3.3 Pre-processing 12 2.4 Frequency-domain Analysis 15 2.4.1 Procedure 15 2.4.2 Contour-based Cost Computation 20 2.5 Cost Optimization and Distance Estimation 21 2.5.1 Disparity Optimization 21 2.5.2 Post-processing and Distance Estimation 23 2.6 Experimental Results 24 2.6.1 Test Environment 24 2.6.2 Experiment on KITTI Dataset 25 2.6.3 Performance Evaluation and Analysis 28 2.7 Conclusion 32 3 Visual Map Matching Based on Structural Tensor and Mutual Information using 3D High Resolution Digital Map 33 3.1 Introduction 33 3.2 Related Work 35 3.3 Methodology 37 3.3.1 Sensor Calibration 37 3.3.2 Digital Map Generation and Synthetic View Conversion 39 3.3.3 Dynamic Object Removal 41 3.3.4 Illuminant Invariance 43 3.3.5 Visual Map Matching using Structure Tensor and Mutual Information 43 3.4 Experiments and Result 49 3.4.1 Methodology 49 3.4.2 Quantitative Results 53 3.5 Conclusions and Future Works 54 4 Free Space Computation using a Sensor Fusion of LIDAR and RGB Camera in Vehicular Environments 55 4.1 Introduction 55 4.2 Methodology 57 4.2.1 Dense Depth Map Generation 57 4.2.2 Color Distribution Entropy 58 4.2.3 Edge Extraction 60 4.2.4 Temporal Smoothness 61 4.2.5 Spatial Smoothness 62 4.3 Experiment and Evaluation 63 4.3.1 Evaluated Methods 63 4.3.2 Experiment on KITTI Dataset 64 4.4 Conclusion 68 5 Conclusion 70 Abstract (In Korean) 87Docto

Retina-Inspired and Physically Based Image Enhancement

Images and videos with good lightness and contrast are vital in several applications, where human experts make an important decision based on the imaging information, such as medical, security, and remote sensing applications. The well-known image enhancement methods include spatial and frequency enhancement techniques such as linear transformation, gamma correction, contrast stretching, histogram equalization and homomorphic filtering. Those conventional techniques are easy to implement but do not recover the exact colour of the images; hence they have limited application areas. Conventional image/video enhancement methods have been widely used with their different advantages and drawbacks; since the last century, there has been increased interest in retina-inspired techniques, e.g., Retinex and Cellular Neural Networks (CNN) as they attempt to mimic the human retina. Despite considerable advances in computer vision techniques, the human eye and visual cortex by far supersede the performance of state-of-the-art algorithms. This research aims to propose a retinal network computational model for image enhancement that mimics retinal layers, targeting the interconnectivity between the Bipolar receptive field and the Ganglion receptive field. The research started by enhancing two state-of-the-art image enhancement methods through their integration with image formation models. In particular, physics-based features (e.g. Spectral Power Distribution of the dominant illuminate in the scene and the Surface Spectral Reflectance of the objects contained in the image are estimated and used as inputs for the enhanced methods). The results show that the proposed technique can adapt to scene variations such as a change in illumination, scene structure, camera position and shadowing. It gives superior performance over the original model. The research has successfully proposed a novel Ganglion Receptive Field (GRF) computational model for image enhancement. Instead of considering only the interactions between each pixel and its surroundings within a single colour layer, the proposed framework introduces the interaction between different colour layers to mimic the retinal neural process; to better mimic the centre-surround retinal receptive field concept, different photoreceptors' outputs are combined. Additionally, this thesis proposed a new contrast enhancement method based on Weber's Law. The objective evaluation shows the superiority of the proposed Ganglion Receptive Field (GRF) method over state-of-the-art methods. The contrast restored image generated by the GRF method achieved the highest performance in contrast enhancement and luminance restoration; however, it achieved less performance in structure preservation, which confirms the physiological studies that observe the same behaviour from the human visual system

Traffic sign recognition based on human visual perception

This thesis presents a new approach, based on human visual perception, for detecting and recognising traffic signs under different viewing conditions. Traffic sign recognition is an important issue within any driver support system as it is fundamental to traffic safety and increases the drivers' awareness of situations and possible decisions that are ahead. All traffic signs possess similar visual characteristics, they are often the same size, shape and colour. However shapes may be distorted when viewed from different viewing angles and colours are affected by overall luminosity and the presence of shadows. Human vision can identify traffic signs correctly by ignoring this variance of colours and shapes. Consequently traffic sign recognition based on human visual perception has been researched during this project. In this approach two human vision models are adopted to solve the problems above: Colour Appearance Model (CIECAM97s) and Behavioural Model of Vision (BMV). Colour Appearance Model (CIECAM97s) is used to segment potential traffic signs from the image background under different weather conditions. Behavioural Model of Vision (BMV) is used to recognize the potential traffic signs. Results show that segmentation based on CIECAM97s performs better than, or comparable to, other perceptual colour spaces in terms of accuracy. In addition, results illustrate that recognition based on BMV can be used in this project effectively to detect a certain range of shape transformations. Furthermore, a fast method of distinguishing and recognizing the different weather conditions within images has been developed. The results show that 84% recognition rate can be achieved under three weather and different viewing conditions.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Factors affecting brightness and colour vision under water

Both theoretical and practical importance can be attached to attempts to model human threshold and supra-threshold visual performance under water. Previously, emphasis has been given to the integration of visual data from experiments conducted in air with data of the physical specification of the underwater light field. However, too few underwater studies have been undertaken for the validity of this approach to be assessed. The present research therefore was concerned with the acquisition of such data. Four experiments were carried out: (a) to compare the predicted and obtained detection thresholds of achromatic targets, (b) to measure the relative recognition thresholds of coloured targets, (c) to compare the predicted and obtained supra-threshold appearance of coloured targets at various viewing distances and under different experimental instructions, (d) to compare the predicted and obtained detection thresholds for achromatic targets under realistic search conditions. Within each experiment, observers were tested on visual tasks in the field and in laboratory simulations. Physical specifications of targets and backgrounds were determined by photometry and spectroradiometry. The data confirmed that: (a) erroneous predictions of the detection threshold could occur when the contributions of absorption and scattering to the attenuation of light were not differentiated, (b) the successful replication of previous findings for the relative recognition thresholds of colours depended on the brightness of the targets, (c) the perceived change in target colour with increasing viewing distance was less than that measured physically, implying the presence of a colour constancy mechanism other than chromatic adaptation and simultaneous colour contrast; the degree of colour constancy also varied with the type of target and experimental instructions, (d) the successful prediction of the effects of target-observer motion and target location uncertainty required more than simple numerical corrections to the basic detection threshold model. It was concluded that further progress in underwater visibility modelling is possible provided that the tendency to oversimplify human visual performance is suppressed

Modeling the emergence of perceptual color space in the primary visual cortex

Humans’ perceptual experience of color is very different from what one might expect, given the light reaching the eye. Identical patterns of light are often perceived as different colors, and different patterns of light are often perceived as the same color. Even more strikingly, our perceptual experience is that hues are arranged circularly (with red similar to violet), even though single-wavelength lights giving rise to perceptions of red and violet are at opposite ends of the wavelength spectrum. The goal of this thesis is to understand how perceptual color space arises in the brain, focusing on the arrangement of hue. To do this, we use computational modeling to integrate findings about light, physiology of the visual system, and color representation in the brain. Recent experimental work shows that alongside spatially contiguous orientation preference maps, macaque primary visual cortex (V1) represents color in isolated patches, and within those patches hue appears to be spatially organized according to perceptual color space. We construct a model of the early visual system that develops based on natural input, and we demonstrate that several factors interact to prevent this first model from developing a realistic representation of hue. We show these factors as independent dimensions and relate them to problems the brain must be overcoming in building a representation of perceptual color space: physiological and environmental variabilities to which the brain is relatively insensitive (surprisingly, given the importance of input in driving development). We subsequently show that a model with a certain position on each dimension develops a hue representation matching the range and spatial organization found in macaque V1—the first time a model has done so. We also show that the realistic results are part of a spectrum of possible results, indicating other organizations of color and orientation that could be found in animals, depending on physiological and environmental factors. Finally, by analyzing how the models work, we hypothesize that well-accepted biological mechanisms such as adaptation, typically omitted from models of both luminance and color processing, can allow the models to overcome these variabilities, as the brain does. These results help understand how V1 can develop a stable, consistent representation of color despite variabilities in the underlying physiology and input statistics. This in turn suggests how the brain can build useful, stable representations in general based on visual experience, despite irrelevant variabilities in input and physiology. The resulting models form a platform to investigate various adult color visual phenomena, as well as to predict results of rearing experiments