colour (ترکیب و کاربرد رنگ)

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Oolour (0۵0990 ۱ Rod OoParkrnd 

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‎Ovlouw Gpave - C1Chw‏ ماه( ‎= Gpepficaicd of colour tolercac ‎= Oo vine (cantar deer) ‎- Peeudoorour sequences ‏و‎ ‎represed ‏مد اس‎ ‏ا جا ‎spar‏ * وكاس ‏ته كه كه 04 03 ‎a2‏ له ‎oo‏ بموحن و ليو 201 2 ‎chrowaticty dragron‏ ‎ 

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۰ ‏حول بیان‎ owt solve dl problews: =) nN — Goll colour patches: dPPicdt to disttrguish oolours it 

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Oppovett provess thepry © Ohoch-white (huwicrare), red- reed, ood bhue-pelow vppoorcts ٠ Wes basis ia biology cod mule © Ghoud use oppooedt colours Por vodicg data 

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وه ماو مان ۰ Gterevsvvpic depth is unt detectable wits isvhucviecnt colours ۱ it uppeur to be slower theo the soe coisvaticg to ‏سوواط‎ ‎white ۰ Skope ued Por we best show ‏حاكن‎ “ee 

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* ‏منم ماو‎ — Osturd Ovtour Gystew (DOG) ey. 090-0700 * Okockeess OO, ntevsiy OO, yer OO, pelo CO — Posivar, Ouasell: stradad colvur chips 

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5 Colour Por ‏ماه اممنمیم) باه‎ Purvis) — Osiuctcess * @ rapidly distrquished colour lies ‏وه عاا عون‎ poled dePiced by the other colours ic O18 space 

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5 Colour Por tabetticy (C) — Ovique hues! “usiversuly revoysized” hues (red, reed, blue, yellow, black, white) should be used — Contrast with backyvudd: border anu obievts 

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5 0 ‏باه ۲ عروام‎ )9( - ‏سای وه وروی :دوعصلصناط عسموان()‎ people pont distiocuish ned-qreru, but wost people vod EE 111111 = Durcber! ody GID codes raniy dotecnished 

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© Colour Por labeticg (P) — Gre ۰ ‏لاله واه لجوج سواو0)‎ wt be very sual (about 2 deqrer wicivun size). * Goooler objevis should be wore high) scturcied, barge volou-voded reqivas should have bow soturaica. Text ‏لته رن‎ be ‏.وصاسف رها روم‎ عمطاهورمول) - رمک و و2 باه ۲و وم ‎Cowon‏ * .. .لامصمخصاط 

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5 Colour Por ‏بجناعماما‎ )©( خاج له )ماه لول مومس ©) دده () - ‎prePerewer):‏ ‏0 9 000 6 

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۱ — Pseudocolouriey is the procice oP ussiqnicgy colour to wup udues thot do ot represeut colour ٠ Dediodt imagery ° Ostrowwird mayer * Quppicg ‏سوت موه ارو‎ to the visible spevinny (usirvorwy, Prored inaes) — Gray soe best Por skhowiey surPuce shupe — Colour best Por chissPication 

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(9) موه ۲ ون ‎٠‏ - )۳ ۳ ‏هوجو طط‎ black-white, red-grerd, bhue- ‏(لششانل) مه اه نامر‎ sequeure cod be used. — or detailed dota, the sequeue should be bused woidy oo hospice. (Por tow tetal, cholic or soturcive se queWwEs vad be used. — OvPors colour spuwes coo be used to oredte color sequewes where equ percept steps correspoud to equal — Okere itis ispportodt to be oble to read oPP dues Prow a volour wup, 0 sequedce thot cycles through wos colours is prePercble. 

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سره ۰ Colour Por wappicry (O) — 8 “opr! thr cob spare (evoke ‏اجه‎ Ane 0, 90,... C30, #9, 99. lawn 0, 29, 90... 889 

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5 ‏موه ۲ اون‎ )4( — Pervepiod ‏مره‎ P the sequeue is swootk, people tecd to see ‏سول‎ colours, potest ‏سای‎ ‎dota. * Op persond diisivg ito blue, yreru, yellow, crane, red, ‏اه رو سم‎ 

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* Colour ۳ ‏رومض‎ (S) — Osiag colour Por O-O ‏و موه‎ * OR Rica to reed accurately * Oa be wed to death ‏مزر‎ ° Gotelite ‏موز موم‎ oP fovisible spectre wupped to red, grees, blue choooels 

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* Colour Por wutidioecsiccd discrete data —S-O plot sien (x, v) ‏روصم‎ red, yreru, blue — Possible to identiPy clusters — @oobiquous: is 3 potet ‏لها‎ or ‏#موص ره‎ — Other wethods ceeded to codlae chisters ‏وحمت‎ ‏سل‎ 

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oxyowitz ef of. Wow Oot to Lie wit Oisuctzaicd * Oise represeotaivd of dota oPPevts the perceived ‎feds:‏ اد ‎ 

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Colour erveptud ismpact oP oo oolour is unt predictable Prow the red/qrecd/blue vowpocrcts oP the olor ‏لاد مرو(‎ uspevis oP colour to diPPereut data is OePout colour waps! robow — Percept avotaeaniy — Case coviours — Yellow otras ‏ماه‎ 

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‎whe‏ وموم ای ۲و اوه ها تون ‎user based put‏ ‎— Ota type ‎— Onte spotid Prequeccy ‎= Ovolzaica task ‎= Other desiqa choices wade by ser 

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Represeuticcy © ‎data‏ اومی() ‎— Objert should be distiocpuishably dPRerecot but oct ‏موسوم‎ ‎ordered ‏سل امن ‎wit perveptud orderiagy‏ ۱۳ سمل موه ‎— qu steps io dota porrespoud to equi steps ia perceived ‏عل موص‎ ‎Ratio data — ‘Dero poidt disttocuishuble it oolour sequewe 

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صرق * Dugoitude oP ‏اون‎ ot every spoil positive - ‏ما بو‎ (gray soe) or sctucatiza 

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)۳۵۵ © Perceptud Rue-Bused @rchitevture Por © Pant vf 100s Oisvatizatiod Data Cxplbrer ( * @rouides choices Por colour wups based oa ‏هم‎ Prequeuy, data 1/۳, ۱ ‏امه و‎ (structure-preservicny), SEyoeutativa, hichtcfsticc 

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5 Fle EAL Crete demain Oper Tales iw Widow Hee اه تكاسلا اهلف عع اضغ لعافلة إقاهاق عله 15 1.2.840.113619.2.5.1762880271.--380.1034174039.129-1 Fie pes الق > | 

Colour CPSC 533C February 3, 2003 Rod McFarland Ware, Chapter 4 • • • • The science of colour vision Colour measurement systems and standards Opponent process theory Applications The science of colour vision • Receptors and trichromacy theory Red  Blue  n ee r G  Colour measurement systems and standards • Any colour can be matched using a combination of three “primaries”. C rR gG bB • The primaries are not necessarily red, green, and blue. Any three different colours can be used. The range of colours that can be produced from a given set of primaries is the gamut. Colour standards • CIE (Commission Internationale d’Éclairage) – Primaries chosen for mathematical properties: do not actually correspond to colours. These “virtual” colours X, Y, and Z are called tristimulus values. – Y is the same as luminance CIE – Chromaticity • Chromaticity is derived from tristimulus values: • Since x+y+z=1, just use x, y values and luminance (Y). • Chromaticity diagram:  x  X 1  y  Y   X Y  Z    z  Z  Uniform Colour Space - CIEluv • Uniform colour space: a representation where equal distances in space correspond to equal distances in perception • Useful for: – Specification of colour tolerances – Color coding (maximum distinction) – Pseudocolour sequences to represent ordered data values • CIE XYZ color space is not uniform • CIEluv is a transformation of the chromaticity diagram • CIEluv does not solve all problems: – Contrast effects – Small colour patches: difficult to distinguish colours in the yellow-blue direction Opponent process theory • Black-white (luminance), redgreen, and blue-yellow opponents • Has basis in biology and culture • Should use opponent colours for coding data Properties of Colour Channels • Isoluminant / Equiluminous patterns: a colour pattern whose components do not differ in luminance • Red-green and yellow-blue channels carry only about 1/3 of the detail carried by black-white. Yellow Text on a Blue Background • Is fairly easy to read unless the text is isoluminant with the background colour. As the luminance of the background becomes the same as the luminance of the text, it is very difficult to make out what the text says. So much so, that at this point I can write just about anything I want here and hardly anyone would want to put in the effort to see what it was I had written. Other isoluminance effects • Stereoscopic depth is not detectable with isoluminant colours • Isoluminance in animation makes it appear to be slower than the same animation in black-andwhite • Shape and form are best shown using luminance: Colour appearance • Contrast • Saturation • Brown low high Applications • Colour selection interfaces • Colour naming – Natural Colour System (NCS) e.g. 0030-G80Y20 • Blackness 00, intensity 30, green 80, yellow 20 – Pantone, Munsell: standard colour chips Applications • Colour for labelling (nominal information encoding) – Distinctness • A rapidly distinguished colour lies outside the convex polygon defined by the other colours in CIE space Applications • Colour for labelling (2) – Unique hues: “universally recognized” hues (red, green, blue, yellow, black, white) should be used – Contrast with background: border around objects Applications • Colour for labelling (3) – Colour blindness: majority of colour-blind people cannot distinguish red-green, but most people can distinguish blue-yellow – Number: only 5-10 codes easily distinguished Applications • Colour for labelling (4) – Size • Colour-coded objects should not be very small (about ½ degree minimum size). • Smaller objects should be more highly saturated, large colour-coded regions should have low saturation. Text highlighting should be high-luminance, low-saturation. – Conventions • Common usage of colours, e.g. red=stop, green=ready, blue=cold… Applications • Colour for labelling (5) – Ware’s 12 recommended colours (in order of preference): Applications • Pseudocolour sequences for mapping – Pseudocolouring is the practice of assigning colour to map values that do not represent colour • Medical imaging • Astronomical images • Mapping nonvisible spectrum information to the visible spectrum (astronomy, infrared images) – Gray scale best for showing surface shape – Colour best for classification Applications • Colour for mapping (2) – For orderable sequences, black-white, red-green, blueyellow, or saturation (dull-vivid) sequence can be used. – For detailed data, the sequence should be based mainly on luminance. For low letail, chromatic or saturation sequences can be used. – Uniform colour spaces can be used to create colour sequences where equal perceptual steps correspond to equal metric steps. – Where it is important to be able to read off values from a colour map, a sequence that cycles through many colours is preferable. Applications • Colour for mapping (3) – A “spiral” through colour space (cycling through several colours while continuously increasing in luminance) is often a good choice. Hue 0, 50,…250, 45, 95… Luminance 0, 25, 50… 225 Applications • Colour for mapping (4) – Perception: even if the sequence is smooth, people tend to see discrete colours, potentially miscategorizing data. • My personal division into blue, green, yellow, orange, red, purple: very nonlinear Applications • Colour for mapping (5) – Using colour for 3-D information mapping • Difficult to read accurately • May be used to identify regions • Satellite images: regions of invisible spectrum mapped to red, green, blue channels Applications • Colour for multidimensional discrete data – 5-D plot using (x, y) position, red, green, blue – Possible to identify clusters – Ambiguous: is a point low-red or high-green? – Other methods needed to analyze clusters once identified Rogowitz et al. How Not to Lie with Visualization • Visual representation of data affects the perceived structure of the data. Enhancing data interpretation Colour using • Perceptual impact of a colour is not predictable from the red/green/blue components of the colour • Mapping different aspects of colour to different data is not intuitively decodable by users. • Default colour maps: rainbow – Perceptual nonlinearity – False contours – Yellow attracts attention Guiding colour map selection • Constrain the set of colour maps available to the user based on: – Data type – Data spatial frequency – Visualization task – Other design choices made by user Representing Structure • Nominal data – Object should be distinguishably different but not perceptually ordered • Ordinal data – Distinguishable with perceptual ordering • Interval data – Equal steps in data correspond to equal steps in perceived magnitude • Ratio data – Zero point distinguishable in colour sequence Structure • Magnitude of a variable at every spatial position – Use luminance (gray scale) or saturation high spatial frequency low Spatial Frequency luminance-based saturation-based Segmentation • Low frequency – more segmentation steps can be used Highlighting Luminance-based map can be highlighted using hue variations. The highlighted regions have the same luminance value as the rest of the map. PRAVDA • Perceptual Rule-Based Architecture for Visualizing Data Accurately • Part of IBM’s Visualization Data Explorer ( http://www.research.ibm.com/dx/) • Provides choices for colour maps based on spatial frequency, data type, and user-selected goal: isomorphic (structure-preserving), segmentation, highlighting PRAVDA