Letter Circular 987: Color Harmony: an Annotated Bibliography
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Color Theory for Painting Video: Color Perception
Color Theory For Painting Video: Color Perception • http://www.ted.com/talks/lang/eng/beau_lotto_optical_illusions_show_how_we_see.html • Experiment • http://www.youtube.com/watch?v=y8U0YPHxiFQ Intro to color theory • http://www.youtube.com/watch?v=059-0wrJpAU&feature=relmfu Color Theory Principles • The Color Wheel • Color context • Color Schemes • Color Applications and Effects The Color Wheel The Color Wheel • A circular diagram displaying the spectrum of visible colors. The Color Wheel: Primary Colors • Primary Colors: Red, yellow and blue • In traditional color theory, primary colors can not be mixed or formed by any combination of other colors. • All other colors are derived from these 3 hues. The Color Wheel: Secondary Colors • Secondary Colors: Green, orange and purple • These are the colors formed by mixing the primary colors. The Color Wheel: Tertiary Colors • Tertiary Colors: Yellow- orange, red-orange, red-purple, blue-purple, blue-green & yellow-green • • These are the colors formed by mixing a primary and a secondary color. • Often have a two-word name, such as blue-green, red-violet, and yellow-orange. Color Context • How color behaves in relation to other colors and shapes is a complex area of color theory. Compare the contrast effects of different color backgrounds for the same red square. Color Context • Does your impression od the center square change based on the surround? Color Context Additive colors • Additive: Mixing colored Light Subtractive Colors • Subtractive Colors: Mixing colored pigments Color Schemes Color Schemes • Formulas for creating visual unity [often called color harmony] using colors on the color wheel Basic Schemes • Analogous • Complementary • Triadic • Split complement Analogous Color formula used to create color harmony through the selection of three related colors which are next to one another on the color wheel. -
C-316: a Guide to Color
COLLEGE OF AGRICULTURAL, CONSUMER AND ENVIRONMENTAL SCIENCES A Guide to Color Revised by Jennah McKinley1 aces.nmsu.edu/pubs • Cooperative Extension Service • Guide C-316 The College of Agricultural, Consumer and Environmental Sciences is an engine for economic and community development in New Figure 1. Sample color wheel. Mexico, improving the lives of New Color is one of the most important stimuli in the world. It affects our moods and personal characteristics. We speak of blue Mondays, being Mexicans through in the pink, seeing red, and everything coming up roses. Webster de- fines color as the sensation resulting from stimulating the eye’s retina with light waves of certain wavelengths. Those sensations have been academic, research, given names such as red, green, and purple. Color communicates. It tells others about you. What determines and extension your choice of colors in your clothing? In your home? In your office? In your car? Your selection of color is influenced by age, personality, programs. experiences, the occasion, the effect of light, size, texture, and a variety of other factors. Some people have misconceptions about color. They may feel cer- tain colors should never be used together, certain colors are always unflattering, or certain colors indicate a person’s character. These ideas will limit their enjoyment of color and can cause them a great deal of frustration in life. To get a better understanding of color, look at na- ture. Consider these facts: All About Discovery!TM • The prettiest gardens have a wide variety of reds, oranges, pinks, New Mexico State University violets, purples, and yellows all mixed together. -
Colornet--Estimating Colorfulness in Natural Images
COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner. -
Color Harmonization Daniel Cohen-Or Olga Sorkine Ran Gal Tommer Leyvand Ying-Qing Xu Tel Aviv University∗ Microsoft Research Asia†
Color Harmonization Daniel Cohen-Or Olga Sorkine Ran Gal Tommer Leyvand Ying-Qing Xu Tel Aviv University∗ Microsoft Research Asia† original image harmonized image Figure 1: Harmonization in action. Our algorithm changes the colors of the background image to harmonize them with the foreground. Abstract colors are sets of colors that hold some special internal relation- ship that provides a pleasant visual perception. Harmony among Harmonic colors are sets of colors that are aesthetically pleasing colors is not determined by specific colors, but rather by their rel- in terms of human visual perception. In this paper, we present a ative position in color space. Generating harmonic colors has been method that enhances the harmony among the colors of a given an open problem among artists and scientists [Holtzschue 2002]. photograph or of a general image, while remaining faithful, as much Munsell [1969] and Goethe [1971] have defined color harmony as as possible, to the original colors. Given a color image, our method balance, in an effort to transfer the concept of color harmony from finds the best harmonic scheme for the image colors. It then allows a subjective perspective to an objective one. Although currently a graceful shifting of hue values so as to fit the harmonic scheme there is no formulation that defines a harmonic set, there is a con- while considering spatial coherence among colors of neighboring sensus among artists that defines when a set is harmonic, and there pixels using an optimization technique. The results demonstrate are some forms, schemes and relations in color space that describe that our method is capable of automatically enhancing the color a harmony of colors [Matsuda 1995; Tokumaru et al. -
Color Appearance Models Today's Topic
Color Appearance Models Arjun Satish Mitsunobu Sugimoto 1 Today's topic Color Appearance Models CIELAB The Nayatani et al. Model The Hunt Model The RLAB Model 2 1 Terminology recap Color Hue Brightness/Lightness Colorfulness/Chroma Saturation 3 Color Attribute of visual perception consisting of any combination of chromatic and achromatic content. Chromatic name Achromatic name others 4 2 Hue Attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors Often refers red, green, blue, and yellow 5 Brightness Attribute of a visual sensation according to which an area appears to emit more or less light. Absolute level of the perception 6 3 Lightness The brightness of an area judged as a ratio to the brightness of a similarly illuminated area that appears to be white Relative amount of light reflected, or relative brightness normalized for changes in the illumination and view conditions 7 Colorfulness Attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic 8 4 Chroma Colorfulness of an area judged as a ratio of the brightness of a similarly illuminated area that appears white Relationship between colorfulness and chroma is similar to relationship between brightness and lightness 9 Saturation Colorfulness of an area judged as a ratio to its brightness Chroma – ratio to white Saturation – ratio to its brightness 10 5 Definition of Color Appearance Model so much description of color such as: wavelength, cone response, tristimulus values, chromaticity coordinates, color spaces, … it is difficult to distinguish them correctly We need a model which makes them straightforward 11 Definition of Color Appearance Model CIE Technical Committee 1-34 (TC1-34) (Comission Internationale de l'Eclairage) They agreed on the following definition: A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue. -
Color Harmony: Experimental and Computational Modeling
Color harmony : experimental and computational modeling Christel Chamaret To cite this version: Christel Chamaret. Color harmony : experimental and computational modeling. Image Processing [eess.IV]. Université Rennes 1, 2016. English. NNT : 2016REN1S015. tel-01382750 HAL Id: tel-01382750 https://tel.archives-ouvertes.fr/tel-01382750 Submitted on 17 Oct 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ANNEE´ 2016 THESE` / UNIVERSITE´ DE RENNES 1 sous le sceau de l'Universit´eBretagne Loire pour le grade de DOCTEUR DE L'UNIVERSITE´ DE RENNES 1 Mention : Informatique Ecole doctorale Matisse pr´esent´eepar Christel Chamaret pr´epar´ee`al'IRISA (Institut de recherches en informatique et syst`emesal´eatoires) et Technicolor Th`esesoutenue `aRennes Color Harmony: le 28 Avril 2016 experimental and devant le jury compos´ede : Pr Alain Tr´emeau Professeur, Universit´ede Saint-Etienne / rapporteur computational Dr Vincent Courboulay Ma^ıtre de conf´erences HDR, Universit´e de La modeling. Rochelle / rapporteur Pr Patrick Le Callet Professeur, Universit´ede Nantes / examinateur Dr Frederic Devinck Ma^ıtrede conf´erencesHDR, Universit´ede Rennes 2 / examinateur Pr Luce Morin Professeur, INSA Rennes / examinateur Dr Olivier Le Meur Ma^ıtrede conf´erencesHDR, Universit´ede Rennes 1 / directeur de th`ese Abstract While the consumption of digital media exploded in the last decade, consequent improvements happened in the area of medical imaging, leading to a better understanding of vision mechanisms. -
TEKNIK GRAFIKA DAN INDUSTRI GRAFIKA JILID 1 Untuk SMK
Antonius Bowo Wasono, dkk. TEKNIK GRAFIKA DAN INDUSTRI GRAFIKA JILID 1 SMK Direktorat Pembinaan Sekolah Menengah Kejuruan Direktorat Jenderal Manajemen Pendidikan Dasar dan Menengah Departemen Pendidikan Nasional Hak Cipta pada Departemen Pendidikan Nasional Dilindungi Undang-undang TEKNIK GRAFIKA DAN INDUSTRI GRAFIKA JILID 1 Untuk SMK Penulis : Antonius Bowo Wasono Romlan Sujinarto Perancang Kulit : TIM Ukuran Buku : 17,6 x 25 cm WAS WASONO, Antonius Bowo t Teknik Grafika dan Industri Grafika Jilid 1 untuk SMK /oleh Antonius Bowo Wasono, Romlan, Sujinarto---- Jakarta : Direktorat Pembinaan Sekolah Menengah Kejuruan, Direktorat Jenderal Manajemen Pendidikan Dasar dan Menengah, Departemen Pendidikan Nasional, 2008. iii, 271 hlm Daftar Pustaka : Lampiran. A Daftar Istilah : Lampiran. B ISBN : 978-979-060-067-6 ISBN : 978-979-060-068-3 Diterbitkan oleh Direktorat Pembinaan Sekolah Menengah Kejuruan Direktorat Jenderal Manajemen Pendidikan Dasar dan Menengah Departemen Pendidikan Nasional Tahun 2008 KATA SAMBUTAN Puji syukur kami panjatkan kehadirat Allah SWT, berkat rahmat dan karunia Nya, Pemerintah, dalam hal ini, Direktorat Pembinaan Sekolah Menengah Kejuruan Direktorat Jenderal Manajemen Pendidikan Dasar dan Menengah Departemen Pendidikan Nasional, telah melaksanakan kegiatan penulisan buku kejuruan sebagai bentuk dari kegiatan pembelian hak cipta buku teks pelajaran kejuruan bagi siswa SMK. Karena buku-buku pelajaran kejuruan sangat sulit di dapatkan di pasaran. Buku teks pelajaran ini telah melalui proses penilaian oleh Badan Standar Nasional Pendidikan sebagai buku teks pelajaran untuk SMK dan telah dinyatakan memenuhi syarat kelayakan untuk digunakan dalam proses pembelajaran melalui Peraturan Menteri Pendidikan Nasional Nomor 45 Tahun 2008 tanggal 15 Agustus 2008. Kami menyampaikan penghargaan yang setinggi-tingginya kepada seluruh penulis yang telah berkenan mengalihkan hak cipta karyanya kepada Departemen Pendidikan Nasional untuk digunakan secara luas oleh para pendidik dan peserta didik SMK. -
Colornet - Estimating Colorfulness in Natural Images
COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner. -
Graphic Standard Guidelinesview
Maricopa County Graphic Standard Guidelines basic standards The updated Maricopa County seal is the basic building block of our new visual image. It is a symbol of many things our County represents. The goal is to establish an image that is credible, “ownable” and that with proper use will promote the County as a well-integrated organization. This graphic standards manual was prepared to ensure that we speak to all with a common “voice,” projecting a distinctive and relevant image of Maricopa County, while allowing the necessary flexibility for individual departmental messages. These guidelines provide an objective set of boundaries to ensure consistent quality in the application of the seal and safeguard against potential problems that could dilute efforts to build the Maricopa County identity. addendum: typefaces Garamond (replaces Minion Regular) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Italic (replaces Minion Italic) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Bold (replaces Minion Semibold and Bold) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Do not artificially italicize Garamond Bold. Microsoft Garamond has only three faces included in its set (roman, italic and bold). This face is licensed from the AGFA/Monotype corporation. An additional two weights (Monotype Alternate Italic and Bold Italic) are available from the AGFA/Monotype web site (www.fonts.com). Please check with your department before purchasing. Tahoma (replaces Avenir Book) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Tahoma Bold (replaces Avenir Medium and Heavy) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Tahoma does not have italic faces in its family. Do not artificially italicize this face. a.1 typeface update:It has come to the attention of the Public Information Office that the typefaces specified for use on Maricopa County materials are not widely available throughout the County computer network, and are cost prohibitive to purchase. -
Chroma and Hue Variation in Color Images of Natural Scenes
Naturalness and Image Quality: Chroma and Hue Variation in Color Images of Natural Scenes Huib de Ridder and Frans J.J. Blommaert Institute for Perception Research, Eindhoven, The Netherlands; Elena A. Fedorovskaya, Department of Psychophysiology, Moscow State University, Mokhovaya street 8, 103009 Moscow, Russia Abstract these parameters (e.g. blur, periodic structure, noise) it proved possible to derive explicit expressions for their The relation between perceptual image quality and natural- relation with the corresponding image attributes.3,4 ness was investigated by varying the colorfulness and hue Scaling experiments using multiply impaired images of color images of natural scenes. These variations were have shown that image attributes can be represented by a set created by digitizing the images, subsequently determining of orthogonal vectors in a Euclidean space.3,5-8 Accord- their color point distributions in the CIELUV color space ingly, the attributes can be said to be the orthogonal dimen- and finally multiplying either the chroma value or the hue- sions of a multidimensional psychological space underlying angle of each pixel by a constant. During the chroma/hue- image quality. The sensorial image is represented in this angle transformation the lightness and hue-angle/chroma space by a point with the perceived strengths of the at- value of each pixel were kept constant. Ten subjects rated tributes as coordinates. Image quality has sometimes been quality and naturalness on numerical scales. The results identified as a direction in this space, with the angle to a show that both quality and naturalness deteriorate as soon as dimension indicating how relevant that attribute is for the hues start to deviate from the ones in the original image. -
The Circle of Fifths
The Circle of Fifths Why a circle? The cyclical nature of musical notes Using a circle diagram to study music can be extremely helpful because of the mathematical beauty of music. In previous lessons, we have touched on the fact that musical notes repeat themselves in predictable patterns going up in pitch. When the sound wave frequency of any particular note is doubled, that same note is produced but at a higher pitch. That note is what we call an octave note; it is one octave above the note below which is half its frequency. You can view this in a linear way, visualizing the notes from left to right in a straight line, as we have done from time to time using the piano keyboard as a diagram: Fig. 1, the seven octaves of the 88-key piano keyboard. In this diagram, I have marked some C notes and A notes on the piano keyboard to illustrate how they repeat themselves, going higher in pitch as you move from left to right. The C note with a blue (cyan) background color is known as “middle C” or also as C4, because it appears in the fourth full octave contained on a standard 88 key keyboard. This C note is twice the sound frequency of the next lower C to the left (C3). It is half the sound frequency of the next highest C to the right (C5). The A note with a yellow background (A4) is important because it is the note most often used these days as the “concert pitch” – it is the note that is traditionally used for all instruments in an orchestra to tune to, so that everyone plays in tune together. -
23 / Color, Additive & Subtractive1
MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013 23 / COLOR, ADDITIVE & SUBTRACTIVE1 In this section and the sections that follow we will explore different aspects of color as it pertains to Photoshop, digital images and printing. You will understand concepts such as RGB and CMYK color space and how to use Photoshop to adjust and correct color in your images. COLOR VOCABULARY Terms describing color are often familiar, but their exact meaning is not. Clarifying some of these terms will give you a context for viewing, choosing and addressing color in your work. When we talk about color in the context of computer displays and digital imaging systems, you’ll notice we often use different terms and a different set of primary and complimentary colors. For ANALOGOUS COLORS example, on the right is an RGB color Colors that sit side-by-side on wheel,2 which shows the relative mix of the wheel. They are often red, green, and blue primaries that are pleasing to the eye and provide mixed to produce the color along the a visual harmony. color wheel. COMPLEMENTARY COLORS HUE Opposite colors on the color Refers to color (e.g. red, blue, wheel. Used together these green, yellow) colors provide high contrast making things easy to notice. 1 Adapted from Digital Foundations, Chapter 05 2 Image from Wikimedia Commons, http://commons.wikimedia.org/wiki/File:RGB_color_wheel_10.svg 23 COLOR, ADDITIVE & SUBTRACTIVE 1 MassArt Studio Foundation: Visual Language Digital Media Cookbook, Fall 2013 SATURATION smart phones. This system combines Intensity, chroma and brilliance varying amounts of the primaries to all refer to how vivid a color is.