First Exhibition of All Color Photography
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Digital Holography Using a Laser Pointer and Consumer Digital Camera Report Date: June 22Nd, 2004
Digital Holography using a Laser Pointer and Consumer Digital Camera Report date: June 22nd, 2004 by David Garber, M.E. undergraduate, Johns Hopkins University, [email protected] Available online at http://pegasus.me.jhu.edu/~lefd/shc/LPholo/lphindex.htm Acknowledgements Faculty sponsor: Prof. Joseph Katz, [email protected] Project idea and direction: Dr. Edwin Malkiel, [email protected] Digital holography reconstruction: Jian Sheng, [email protected] Abstract The point of this project was to examine the feasibility of low-cost holography. Can viable holograms be recorded using an ordinary diode laser pointer and a consumer digital camera? How much does it cost? What are the major difficulties? I set up an in-line holographic system and recorded both still images and movies using a $600 Fujifilm Finepix S602Z digital camera and a $10 laser pointer by Lazerpro. Reconstruction of the stills shows clearly that successful holograms can be created with a low-cost optical setup. The movies did not reconstruct, due to compression and very low image resolution. Garber 2 Theoretical Background What is a hologram? The Merriam-Webster dictionary defines a hologram as, “a three-dimensional image reproduced from a pattern of interference produced by a split coherent beam of radiation (as a laser).” Holograms can produce a three-dimensional image, but it is more helpful for our purposes to think of a hologram as a photograph that can be refocused at any depth. So while a photograph taken of two people standing far apart would have one in focus and one blurry, a hologram taken of the same scene could be reconstructed to bring either person into focus. -
Sample Manuscript Showing Specifications and Style
Information capacity: a measure of potential image quality of a digital camera Frédéric Cao 1, Frédéric Guichard, Hervé Hornung DxO Labs, 3 rue Nationale, 92100 Boulogne Billancourt, FRANCE ABSTRACT The aim of the paper is to define an objective measurement for evaluating the performance of a digital camera. The challenge is to mix different flaws involving geometry (as distortion or lateral chromatic aberrations), light (as luminance and color shading), or statistical phenomena (as noise). We introduce the concept of information capacity that accounts for all the main defects than can be observed in digital images, and that can be due either to the optics or to the sensor. The information capacity describes the potential of the camera to produce good images. In particular, digital processing can correct some flaws (like distortion). Our definition of information takes possible correction into account and the fact that processing can neither retrieve lost information nor create some. This paper extends some of our previous work where the information capacity was only defined for RAW sensors. The concept is extended for cameras with optical defects as distortion, lateral and longitudinal chromatic aberration or lens shading. Keywords: digital photography, image quality evaluation, optical aberration, information capacity, camera performance database 1. INTRODUCTION The evaluation of a digital camera is a key factor for customers, whether they are vendors or final customers. It relies on many different factors as the presence or not of some functionalities, ergonomic, price, or image quality. Each separate criterion is itself quite complex to evaluate, and depends on many different factors. The case of image quality is a good illustration of this topic. -
Seeing Like Your Camera ○ My List of Specific Videos I Recommend for Homework I.E
Accessing Lynda.com ● Free to Mason community ● Set your browser to lynda.gmu.edu ○ Log-in using your Mason ID and Password ● Playlists Seeing Like Your Camera ○ My list of specific videos I recommend for homework i.e. pre- and post-session viewing.. PART 2 - FALL 2016 ○ Clicking on the name of the video segment will bring you immediately to Lynda.com (or the login window) Stan Schretter ○ I recommend that you eventually watch the entire video class, since we will only use small segments of each video class [email protected] 1 2 Ways To Take This Course What Creates a Photograph ● Each class will cover on one or two topics in detail ● Light ○ Lynda.com videos cover a lot more material ○ I will email the video playlist and the my charts before each class ● Camera ● My Scale of Value ○ Maximum Benefit: Review Videos Before Class & Attend Lectures ● Composition & Practice after Each Class ○ Less Benefit: Do not look at the Videos; Attend Lectures and ● Camera Setup Practice after Each Class ○ Some Benefit: Look at Videos; Don’t attend Lectures ● Post Processing 3 4 This Course - “The Shot” This Course - “The Shot” ● Camera Setup ○ Exposure ● Light ■ “Proper” Light on the Sensor ■ Depth of Field ■ Stop or Show the Action ● Camera ○ Focus ○ Getting the Color Right ● Composition ■ White Balance ● Composition ● Camera Setup ○ Key Photographic Element(s) ○ Moving The Eye Through The Frame ■ Negative Space ● Post Processing ○ Perspective ○ Story 5 6 Outline of This Class Class Topics PART 1 - Summer 2016 PART 2 - Fall 2016 ● Topic 1 ○ Review of Part 1 ● Increasing Your Vision ● Brief Review of Part 1 ○ Shutter Speed, Aperture, ISO ○ Shutter Speed ● Seeing The Light ○ Composition ○ Aperture ○ Color, dynamic range, ● Topic 2 ○ ISO and White Balance histograms, backlighting, etc. -
Explicit Image Detection Using Ycbcr Space Color Model As Skin Detection
Applications of Mathematics and Computer Engineering Explicit Image Detection using YCbCr Space Color Model as Skin Detection JORGE ALBERTO MARCIAL BASILIO1, GUALBERTO AGUILAR TORRES2, GABRIEL SÁNCHEZ PÉREZ3, L. KARINA TOSCANO MEDINA4, HÉCTOR M. PÉREZ MEANA5 Sección de Estudios de Posgrados e Investigación 1Escuela Superior de Ingeniería Mecánica y Eléctrica Unidad Culhuacan Santa Ana #1000 Col. San Francisco Culhuacan, Del. Coyoacán C.P. 04430 MEXICO CITY [email protected], [email protected], [email protected], [email protected], [email protected] Abstract: - In this paper a novel way to detect explicit content images is proposed using the YCbCr space color, moreover the color pixels percentage that are within the image is calculated which are susceptible to be a tone skin. The main goal to use this method is to apply to forensic analysis or pornographic images detection on storage devices such as hard disk, USB memories, etc. The results obtained using the proposed method are compared with Paraben’s Porn Detection Stick software which is one of the most commercial devices used for detecting pornographic images. The proposed algorithm achieved identify up to 88.8% of the explicit content images, and 5% of false positives, the Paraben’s Porn Detection Stick software achieved 89.7% of effectiveness for the same set of images but with a 6.8% of false positives. In both cases were used a set of 1000 images, 550 natural images, and 450 with highly explicit content. Finally the proposed algorithm effectiveness shows that the methodology applied to explicit image detection was successfully proved vs Paraben’s Porn Detection Stick software. -
Passport Photograph Instructions by the Police
Passport photograph instructions 1 (7) Passport photograph instructions by the police These instructions describe the technical and quality requirements on passport photographs. The same instructions apply to all facial photographs used on the passports, identity cards and permits granted by the police. The instructions are based on EU legislation and international treaties. The photographer usually sends the passport photograph directly to the police electronically, and it is linked to the application with a photograph retrieval code assigned to the photograph. You can also use a paper photograph, if you submit your application at a police service point. Contents • Photograph format • Technical requirements on the photograph • Dimensions and positioning • Posture • Lighting • Expressions, glasses, head-wear and make-up • Background Photograph format • The photograph can be a black-and-white or a colour photograph. • The dimensions of photographs submitted electronically must be exactly 500 x 653 pixels. Deviations as small as one pixel are not accepted. • Electronically submitted photographs must be saved in the JPEG format (not JPEG2000). The file extension can be either .jpg or .jpeg. • The maximum size of an electronically submitted photograph is 250 kilobytes. 1. Correct 2. Correct 2 (7) Technical requirements on the photograph • The photograph must be no more than six months old. • The photograph must not be manipulated in any way that would change even the small- est details in the subject’s appearance or in a way that could raise suspicions about the photograph's authenticity. Use of digital make-up is not allowed. • The photograph must be sharp and in focus over the entire face. -
Photographic Films
PHOTOGRAPHIC FILMS A camera has been called a “magic box.” Why? Because the box captures an image that can be made permanent. Photographic films capture the image formed by light reflecting from the surface being photographed. This instruction sheet describes the nature of photographic films, especially those used in the graphic communications industries. THE STRUCTURE OF FILM Protective Coating Emulsion Base Anti-Halation Backing Photographic films are composed of several layers. These layers include the base, the emulsion, the anti-halation backing and the protective coating. THE BASE The base, the thickest of the layers, supports the other layers. Originally, the base was made of glass. However, today the base can be made from any number of materials ranging from paper to aluminum. Photographers primarily use films with either a plastic (polyester) or paper base. Plastic-based films are commonly called “films” while paper-based films are called “photographic papers.” Polyester is a particularly suitable base for film because it is dimensionally stable. Dimensionally stable materials do not appreciably change size when the temperature or moisture- level of the film change. Films are subjected to heated liquids during processing (developing) and to heat during use in graphic processes. Therefore, dimensional stability is very important for graphic communications photographers because their final images must always match the given size. Conversely, paper is not dimen- sionally stable and is only appropriate as a film base when the “photographic print” is the final product (as contrasted to an intermediate step in a multi-step process). THE EMULSION The emulsion is the true “heart” of film. -
Computational RYB Color Model and Its Applications
IIEEJ Transactions on Image Electronics and Visual Computing Vol.5 No.2 (2017) -- Special Issue on Application-Based Image Processing Technologies -- Computational RYB Color Model and its Applications Junichi SUGITA† (Member), Tokiichiro TAKAHASHI†† (Member) †Tokyo Healthcare University, ††Tokyo Denki University/UEI Research <Summary> The red-yellow-blue (RYB) color model is a subtractive model based on pigment color mixing and is widely used in art education. In the RYB color model, red, yellow, and blue are defined as the primary colors. In this study, we apply this model to computers by formulating a conversion between the red-green-blue (RGB) and RYB color spaces. In addition, we present a class of compositing methods in the RYB color space. Moreover, we prescribe the appropriate uses of these compo- siting methods in different situations. By using RYB color compositing, paint-like compositing can be easily achieved. We also verified the effectiveness of our proposed method by using several experiments and demonstrated its application on the basis of RYB color compositing. Keywords: RYB, RGB, CMY(K), color model, color space, color compositing man perception system and computer displays, most com- 1. Introduction puter applications use the red-green-blue (RGB) color mod- Most people have had the experience of creating an arbi- el3); however, this model is not comprehensible for many trary color by mixing different color pigments on a palette or people who not trained in the RGB color model because of a canvas. The red-yellow-blue (RYB) color model proposed its use of additive color mixing. As shown in Fig. -
Tnemec Colorbook
COLORBOOK WHITES 00WH Tnemec White � 06WH Albatross � 07WH Winter Mist � 08WH Acropolis � LRV 84% LRV 82% LRV 80% LRV 72% 01WH Ash White � 02WH Iceberg � 03WH Daisy � 04WH Silver Pearl � LRV 84% LRV 84% LRV 75% LRV 76% 12WH Milkweed � 13WH French Vanilla � 14WH Veiled � 15WH Aspen � LRV 78% LRV 73% LRV 78% LRV 72% 15BR Pale � 22BR Nova � 57BR Cloud � 79BR Colliseum � LRV 83% LRV 81% LRV 75% LRV 67% NOTE: Colors represented are digital reproductions of actual standards and will vary in appearance due to differences in monitor and video card output. These digital representations should not be used to finalize color selection(s). Please contact your local Tnemec Coatings Consultant for color-accurate samples or for assistance with suitable primer and finish coat selections and color matching. LRV = Light Reflectance Value � Standard color and gloss warranty is available in this color for Fluoronar and HydroFlon products. Other colors may be included. Contact your Tnemec representative for more information. GRAYS 30GR Comet � 24GR Lightpole � 43GR Constellation � 37GR Gradation � LRV 75% LRV 62% LRV 71% LRV 65% 25GR Grey Day � 31GR Slate Gray � 57GR Aluminum � 38GR Dove Gray � LRV 46% LRV 61% LRV 46% LRV 58% 33GR Gray – ANSI No. 61 � 32GR Light Gray – ANSI No. 70 � 46GR Sinker � 39GR Pigeon � LRV 33% LRV 44% LRV 26% LRV 42% 35GR Black � 34GR Deep Space � 48GR Moon Shadow � 41GR Hammerhead � LRV 4% LRV 12% LRV 10% LRV 17% NOTE: Colors represented are digital reproductions of actual standards and will vary in appearance due to differences in monitor and video card output. -
One Is Enough. a Photograph Taken by the “Single-Pixel Camera” Built by Richard Baraniuk and Kevin Kelly of Rice University
One is Enough. A photograph taken by the “single-pixel camera” built by Richard Baraniuk and Kevin Kelly of Rice University. (a) A photograph of a soccer ball, taken by a conventional digital camera at 64 64 resolution. (b) The same soccer ball, photographed by a single-pixel camera. The image is de- rived× mathematically from 1600 separate, randomly selected measurements, using a method called compressed sensing. (Photos courtesy of R. G. Baraniuk, Compressive Sensing [Lecture Notes], Signal Processing Magazine, July 2007. c 2007 IEEE.) 114 What’s Happening in the Mathematical Sciences Compressed Sensing Makes Every Pixel Count rash and computer files have one thing in common: compactisbeautiful.Butifyou’veevershoppedforadigi- Ttal camera, you might have noticed that camera manufac- turers haven’t gotten the message. A few years ago, electronic stores were full of 1- or 2-megapixel cameras. Then along came cameras with 3-megapixel chips, 10 megapixels, and even 60 megapixels. Unfortunately, these multi-megapixel cameras create enor- mous computer files. So the first thing most people do, if they plan to send a photo by e-mail or post it on the Web, is to com- pact it to a more manageable size. Usually it is impossible to discern the difference between the compressed photo and the original with the naked eye (see Figure 1, next page). Thus, a strange dynamic has evolved, in which camera engineers cram more and more data onto a chip, while software engineers de- Emmanuel Candes. (Photo cour- sign cleverer and cleverer ways to get rid of it. tesy of Emmanuel Candes.) In 2004, mathematicians discovered a way to bring this “armsrace”to a halt. -
Guided Tour of Color Space
A Guided Tour of Color Space Charles Poynton This article describes the theory of color repro- dent radiation with different spectral response duction in video, and some of the engineering curves. Because there are exactly three types of compromises necessary to make practical color photoreceptors, three components are cameras and practical coding systems. necessary and sufficient to describe a color, providing that appropriate spectral weighting Video processing is generally concerned with functions are used: Color vision is inherently color represented in three components derived trichromatic. from the scene, usually red, green, and blue or components computed from these. Once red, A fourth type of photoreceptor cell, the rod, is green, and blue components of a scene are also present in the retina. Rods are effective only obtained, color coding systems transform these at extremely low light intensities. Because there components into other forms suitable for is only one type of rod cell, “night vision” cannot processing, recording, and transmission. Accu- perceive color. Image reproduction takes place at rate color reproduction depends on knowledge light levels sufficiently high that the rod recep- of exactly how the physical spectra of the orig- tors play no role. inal scene are transformed into these compo- nents and exactly how the color components are Isaac Newton said, “Indeed rays, properly transformed to physical spectra at the display. expressed, are not colored.” Spectral power distributions (SPDs) exist in the physical world, I use the term luminance and the symbol Y to but color exists only in the eye and the brain. refer to true, CIE luminance, a linear-light quan- tity proportional to physical intensity. -
Kelvin Color Temperature
KELVIN COLOR TEMPERATURE William Thompson Kelvin was a 19th century physicist and mathematician who invented a temperature scale that had absolute zero as its low endpoint. In physics, absolute zero is a very cold temperature, the coldest possible, at which no heat exists and kinetic energy (movement) ceases. On the Celsius scale absolute zero is -273 degrees, and on the Fahrenheit scale it is -459 degrees. The Kelvin temperature scale is often used for scientific measurements. Kelvins, as the degrees are now called, are derived from the actual temperature of a black body radiator, which means a black material heated to that temperature. An incandescent filament is very dark, and approaches being a black body radiator, so the actual temperature of an incandescent filament is somewhat close to its color temperature in Kelvins. The color temperature of a lamp is very important in the television industry where the camera must be calibrated for white balance. This is often done by focusing the camera on a white card in the available lighting and tweaking it so that the card reads as true white. All other colors will automatically adjust so that they read properly. This is especially important to reproduce “normal” looking skin tones. In theatre applications, where it is only important for colors to read properly to the human eye, the exact color temperature of lamps is not so important. Incandescent lamps tend to have a color temperature around 3200 K, but this is true only if they are operating with full voltage. Remember that dimmers work by varying the voltage pressure supplied to the lamp. -
RGB Color Code According to the Commission for the Geological Map of the World (CGMW), Paris, France
RGB Color Code according to the Commission for the Geological Map of the World (CGMW), Paris, France Holocene 254/242/236 Tithonian 217/241/247 Famennian 242/237/197 Ediacaran 254/217/106 254/242/224 Upper Neo- Upper Upper 255/242/211 Kimmeridgian 204/236/244 241/225/157 Frasnian 242/237/173 proterozoic Cryogenian 254/204/92 179/227/238 99 254/179/66 Pleistocene "Ionian" 255/242/199 Oxfordian 191/231/241 Middle Givetian 241/225/133 Tonian 254/191/78 255/242/174 241/200/104 Calabrian 255/242/186 Callovian 191/231/229 203/140/55 Eifelian 241/213/118 Stenian 254/217/154 Quaternary 249/249/127 Meso- Gelasian 255/237/179 Bathonian 179/226/227 247/53/ proterozoic Ectasian Middle Emsian 229/208/117 253/204/138 Lower 253/180/98 Pliocene Piacenzian 255/255/191 52/178/201 128/207/216 Bajocian 166/221/224 Pragian 229/196/104 Calymmian 253/192/122 229/172/77 255/255/153 Zanclean 255/255/179 Aalenian 154/217/221 Lochkovian 229/183/90 Statherian 248/117/167 Devonian Pridoli Messinian 255/255/115 Toarcian 153/206/227 230/245/225 Paleo- Orosirian 247/104/152 103/197/202 230/245/225 247/67/112 proterozoic Tortonian 255/255/102 128/197/221 Rhyacian 247/91/137 Jurassic Pliensbachian Ludfordian 217/240/223 Lower Ludlow Proterozoic 247/67/112 255/230/25 Miocene Serravallian 255/255/89 66/174/208 Sinemurian 103/188/216 191/230/207 Gorstian 204/236/221 Siderian 247/79/124 242/249/29 255/255/0 Langhian 255/255/77 78/179/211 Hettangian Wenlock Homerian 204/235/209 Neoarchean 250/167/200 255/255/65 Burdigalian Rhaetian 227/185/219 179/225/182 179/225/194 Sheinwoodian