Bookâ˘
Color Imaging: Fundamentals and Applications
22 Jul 2008-
TL;DR: In this article, color theory is explained from its origin to the current state of the art, including image capture and display as well as the practical use of color in disciplines such as computer graphics, computer vision, photography, and film.
Abstract: This book provides the reader with an understanding of what color is, where color comes from, and how color can be used correctly in many different applications. The authors first treat the physics of light and its interaction with matter at the atomic level, so that the origins of color can be appreciated. The intimate relationship between energy levels, orbital states, and electromagnetic waves helps to explain why diamonds shimmer, rubies are red, and the feathers of the Blue Jay are blue. Then, color theory is explained from its origin to the current state of the art, including image capture and display as well as the practical use of color in disciplines such as computer graphics, computer vision, photography, and film.
Citations
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Proceedings Articleâ˘
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TL;DR: A scheme is developed for classifying the types of motion perceived by a humanlike robot and equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented.
Abstract: A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >
2,000Â citations
Journal Articleâ˘
TL;DR: An algorithm of color image understanding which segments the image after analyzing sur-faces with color variations due to lighting condition and object colors and gives forth a physical des-cription of imaging process, including intrinsic images, segmented image, the color of light and objects.
Abstract: We present an algorithm of color image understanding which segments the image after analyzing sur-faces with color variations due to lighting condition and object colors. The work is based on dichromaticreflection model according to the strategy of hypothesis plus test, following the continuity of image and thefeature of color clusters, the algorithm completes the image segmentation and gives forth a physical des-cription of imaging process, including intrinsic images, segmented image, the color of light and objects. Re-flecting respectively the propertics of light condition and every objects, both matte image and highlight im-age compose the intrinsic images.
183Â citations
TL;DR: This work presents a novel histogram reshaping technique which allows significantly better control than previous methods and transfers the color palette between images of arbitrary dynamic range and achieves this by manipulating histograms at different scales.
144Â citations
TL;DR: This survey analyzes advances in automultiscopic displays, categorize them along the dimensions of the plenoptic function, and presents the relevant aspects of human perception on which they rely.
141Â citations
Cites background or methods from "Color Imaging: Fundamentals and App..."
...So-called âstandardizedâ observers exist [37,116], based on measurements of a set of observers, and are used as a reference for...
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...Right: the contrast sensitivity function, represented by a CampbellâRobson chart [37];...
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...The dual-process theory is the commonly accepted theory that describes the processing of color by the HVS [37]....
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TL;DR: An image-difference framework that comprises image normalization, feature extraction, and feature combination is presented that shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
Abstract: Existing image-difference measures show excellent accuracy in predicting distortions, such as lossy compression, noise, and blur. Their performance on certain other distortions could be improved; one example of this is gamut mapping. This is partly because they either do not interpret chromatic information correctly or they ignore it entirely. We present an image-difference framework that comprises image normalization, feature extraction, and feature combination. Based on this framework, we create image-difference measures by selecting specific implementations for each of the steps. Particular emphasis is placed on using color information to improve the assessment of gamut-mapped images. Our best image-difference measure shows significantly higher prediction accuracy on a gamut-mapping dataset than all other evaluated measures.
110Â citations
References
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01 Jan 1967
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Abstract: The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i = 1, 2, * , k, is the conditional mean of p over the set Si, then W2(S) = ff=ISi f z u42 dp(z) tends to be low for the partitions S generated by the method. We say 'tends to be low,' primarily because of intuitive considerations, corroborated to some extent by mathematical analysis and practical computational experience. Also, the k-means procedure is easily programmed and is computationally economical, so that it is feasible to process very large samples on a digital computer. Possible applications include methods for similarity grouping, nonlinear prediction, approximating multivariate distributions, and nonparametric tests for independence among several variables. In addition to suggesting practical classification methods, the study of k-means has proved to be theoretically interesting. The k-means concept represents a generalization of the ordinary sample mean, and one is naturally led to study the pertinent asymptotic behavior, the object being to establish some sort of law of large numbers for the k-means. This problem is sufficiently interesting, in fact, for us to devote a good portion of this paper to it. The k-means are defined in section 2.1, and the main results which have been obtained on the asymptotic behavior are given there. The rest of section 2 is devoted to the proofs of these results. Section 3 describes several specific possible applications, and reports some preliminary results from computer experiments conducted to explore the possibilities inherent in the k-means idea. The extension to general metric spaces is indicated briefly in section 4. The original point of departure for the work described here was a series of problems in optimal classification (MacQueen [9]) which represented special
24,320Â citations
"Color Imaging: Fundamentals and App..." refers methods in this paper
...Here, the image is segmented using K-means segmentation [98,724]....
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TL;DR: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented and it is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a random chosen non-diseased subject.
Abstract: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect difference...
19,398Â citations
"Color Imaging: Fundamentals and App..." refers methods in this paper
...equivalent to the Wilcoxon statistic and computes the probability of correct classification [421]....
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TL;DR: In this article, a double-layer structure of organic thin films was prepared by vapor deposition, and efficient injection of holes and electrons was provided from an indium-tinoxide anode and an alloyed Mg:Ag cathode.
Abstract: A novel electroluminescent device is constructed using organic materials as the emitting elements. The diode has a doubleâlayer structure of organic thin films, prepared by vapor deposition. Efficient injection of holes and electrons is provided from an indiumâtinâoxide anode and an alloyed Mg:Ag cathode. Electronâhole recombination and green electroluminescent emission are confined near the organic interface region. High external quantum efficiency (1% photon/electron), luminous efficiency (1.5 lm/W), and brightness (>1000 cd/m2) are achievable at a driving voltage below 10 V.
13,185Â citations
"Color Imaging: Fundamentals and App..." refers background in this paper
...Such devices are known as organic light-emitting diodes (OLEDs) [1117]....
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...5 The interest in OLEDs dates back to the mid-1960s [451, 452], although the breakthrough in OLED technology came after Tang and van Slyke [1117], recognized that the efficiency of an OLED can be greatly increased by using a bilayered structure where each layer is responsible for either electron or hole transport and injection (Figure 14....
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TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Abstract: Publisher Summary This chapter provides an account of different neural network architectures for pattern recognition. A neural network consists of several simple processing elements called neurons. Each neuron is connected to some other neurons and possibly to the input nodes. Neural networks provide a simple computing paradigm to perform complex recognition tasks in real time. The chapter categorizes neural networks into three types: single-layer networks, multilayer feedforward networks, and feedback networks. It discusses the gradient descent and the relaxation method as the two underlying mathematical themes for deriving learning algorithms. A lot of research activity is centered on learning algorithms because of their fundamental importance in neural networks. The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue. It closes with the discussion of performance and implementation issues.
13,033Â citations
"Color Imaging: Fundamentals and App..." refers methods in this paper
...Here, the image is segmented using K-means segmentation [98,724]....
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Proceedings Articleâ˘
24 Aug 1981TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Abstract: Image registration finds a variety of applications in computer vision. Unfortunately, traditional image registration techniques tend to be costly. We present a new image registration technique that makes use of the spatial intensity gradient of the images to find a good match using a type of Newton-Raphson iteration. Our technique is taster because it examines far fewer potential matches between the images than existing techniques Furthermore, this registration technique can be generalized to handle rotation, scaling and shearing. We show how our technique can be adapted tor use in a stereo vision system.
12,944Â citations