Author
Ahmet Oguz Akyz
Bio: Ahmet Oguz Akyz is an academic researcher. The author has contributed to research in topics: Color theory. The author has an hindex of 1, co-authored 1 publications receiving 175 citations.
Topics: Color theory
Papers
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Book•
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.
182 citations
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Proceedings Article•
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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. >
2,000 citations
Journal Article•
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.
183 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.
144 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.
141 citations
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.
110 citations