A clear visualization of RGB and CMY color model. How they work and what are their color elements.At the end, you also find the equation of calculating and converting them.
At the end of this lesson, you should be able to;
identify color formation and how color visualize.
describe primary and secondary colors.
describe display on CRT and LCD.
comprehend RGB, CMY, CMYK and HSI color models.
color image processing is divided into two major areas:
1. Full Color image Processing
2. Pseudo Color image Processing
It Includes Color Fundamentals,Color Models,Pseudo color image Processing,Full Color image Processing,Color Transformation.
This document discusses color image processing and provides details on color fundamentals, color models, and pseudocolor image processing techniques. It introduces color image processing, full-color versus pseudocolor processing, and several color models including RGB, CMY, and HSI. Pseudocolor processing techniques of intensity slicing and gray level to color transformation are explained, where grayscale values in an image are assigned colors based on intensity ranges or grayscale levels.
This document discusses color image processing and provides information on various color models and color fundamentals. It describes full-color and pseudo-color processing, color fundamentals including the visible light spectrum, color perception by the human eye, and color properties. It also summarizes RGB, CMY/CMYK, and HSI color models, conversions between models, and methods for pseudo-color image processing including intensity slicing and intensity to color transformations.
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
A color model specifies a color space and visible subset of colors within it. There are four main hardware-oriented color models: RGB, CMY, CMYK, and YIQ. However, these are not intuitive for describing color in terms of hue, saturation and brightness. Therefore, models like HSV, HLS, and HVC were developed which relate more directly to human perception of color. The RGB and CMY models represent colors as combinations of red, green, blue and cyan, magenta, yellow primary colors respectively and are used in monitors and printing.
Halftoning is the process of converting a greyscale image to a binary image made up of black and white dots. In newspapers, halftoning simulates greyscale using patterns of black dots of varying sizes on a white background. Traditionally, halftoning was done photographically by projecting an image through a halftone screen with an etched grid onto film. Different screen frequencies control dot size. Digital halftoning techniques include patterning, which replaces each pixel with a pattern from a binary font, and dithering, which thresholds the image against a dither matrix to determine black and white pixels.
The RGB and CMY color models are two primary systems for representing color digitally. The RGB model uses additive color mixing of red, green, and blue light to reproduce a wide gamut of colors on computer screens. It is well-suited for digital imaging. The CMYK model uses subtractive color mixing of cyan, magenta, yellow, and black inks to reproduce colors for print. It is widely used in color printing. Both models can be described using numeric values or percentages of their primary colors to precisely define a specific hue.
At the end of this lesson, you should be able to;
identify color formation and how color visualize.
describe primary and secondary colors.
describe display on CRT and LCD.
comprehend RGB, CMY, CMYK and HSI color models.
color image processing is divided into two major areas:
1. Full Color image Processing
2. Pseudo Color image Processing
It Includes Color Fundamentals,Color Models,Pseudo color image Processing,Full Color image Processing,Color Transformation.
This document discusses color image processing and provides details on color fundamentals, color models, and pseudocolor image processing techniques. It introduces color image processing, full-color versus pseudocolor processing, and several color models including RGB, CMY, and HSI. Pseudocolor processing techniques of intensity slicing and gray level to color transformation are explained, where grayscale values in an image are assigned colors based on intensity ranges or grayscale levels.
This document discusses color image processing and provides information on various color models and color fundamentals. It describes full-color and pseudo-color processing, color fundamentals including the visible light spectrum, color perception by the human eye, and color properties. It also summarizes RGB, CMY/CMYK, and HSI color models, conversions between models, and methods for pseudo-color image processing including intensity slicing and intensity to color transformations.
This document discusses color image processing and different color models. It begins with an introduction and then covers color fundamentals such as brightness, hue, and saturation. It describes common color models like RGB, CMY, HSI, and YIQ. Pseudo color processing and full color image processing are explained. Color transformations between color models are also discussed. Implementation tips for interpolation methods in color processing are provided. The document concludes with thanks to the head of the computer science department.
A color model specifies a color space and visible subset of colors within it. There are four main hardware-oriented color models: RGB, CMY, CMYK, and YIQ. However, these are not intuitive for describing color in terms of hue, saturation and brightness. Therefore, models like HSV, HLS, and HVC were developed which relate more directly to human perception of color. The RGB and CMY models represent colors as combinations of red, green, blue and cyan, magenta, yellow primary colors respectively and are used in monitors and printing.
Halftoning is the process of converting a greyscale image to a binary image made up of black and white dots. In newspapers, halftoning simulates greyscale using patterns of black dots of varying sizes on a white background. Traditionally, halftoning was done photographically by projecting an image through a halftone screen with an etched grid onto film. Different screen frequencies control dot size. Digital halftoning techniques include patterning, which replaces each pixel with a pattern from a binary font, and dithering, which thresholds the image against a dither matrix to determine black and white pixels.
The RGB and CMY color models are two primary systems for representing color digitally. The RGB model uses additive color mixing of red, green, and blue light to reproduce a wide gamut of colors on computer screens. It is well-suited for digital imaging. The CMYK model uses subtractive color mixing of cyan, magenta, yellow, and black inks to reproduce colors for print. It is widely used in color printing. Both models can be described using numeric values or percentages of their primary colors to precisely define a specific hue.
Image restoration and degradation modelAnupriyaDurai
This document discusses image restoration and degradation. It provides an overview of image restoration techniques which attempt to reverse degradation processes and restore lost image information. Several types of image degradation are described, including motion blur, noise, and misfocus. Common noise models are explained, such as Gaussian, salt and pepper, Erlang, exponential, and uniform noise. Methods for estimating degradation models from observed images are also summarized, including using image observations, experimental replication of degradation, and mathematical modeling.
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
Image enhancement techniques can be divided into spatial and frequency domain methods. Spatial domain methods operate directly on pixel values using techniques like basic gray level transformations, contrast stretching and thresholding. These manipulations are used to accentuate image features, improve display quality or aid machine analysis by modifying pixel intensities within an image.
This document discusses different types of electromagnetic radiation and their uses in digital image processing. It covers gamma rays, X-rays, ultraviolet rays, visible and infrared rays, microwaves, and radio bands. Applications described include medical imaging techniques like MRI, industrial inspection, astronomy, remote sensing, and law enforcement applications like license plate and fingerprint recognition. Radar imaging is also discussed as a key application using microwaves.
The document discusses pseudo color images and techniques for converting grayscale images to color. It defines pseudo color images as grayscale images mapped to color according to a lookup table or function. It describes various color schemes for this mapping, including grayscale schemes that use shades of gray and oscillating schemes that emphasize certain grayscale ranges in color. The document also discusses using piecewise linear functions and smooth non-linear functions to transform grayscale levels to color for purposes such as enhancing contrast or reducing noise in images.
This document discusses different color models used in computer graphics and printing. It explains that color models are systems for creating a range of colors from a small set of primary colors. The two main types are additive models which use light, like RGB, and subtractive models which use inks, like CMYK. RGB uses red, green and blue light and is for computer displays. CMYK uses cyan, magenta, yellow and black inks and is the standard for color printing. It provides details on how each model mixes colors and describes other models like HSV which represents color in terms of hue, saturation and value.
Digital image processing involves techniques to restore degraded images. Image restoration aims to recover the original undistorted image from a degraded observation. The degradation is typically modeled as the original image being operated on by a degradation function and additive noise. Common restoration techniques include spatial domain filters like mean, median and order-statistic filters to remove noise, and frequency domain filtering to reduce periodic noise. The choice of restoration method depends on the type and characteristics of degradation in the image.
Color fundamentals and color models - Digital Image ProcessingAmna
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
This document discusses color image processing and color models. It covers:
1) The basics of color perception and how humans see color through cone cells in the eye sensitive to different wavelengths.
2) Common color models like RGB, HSV, and CMYK and how they represent color.
3) Converting between color models and adjusting color properties like hue, saturation, and intensity.
4) Applications of color processing like pseudocoloring grayscale images and correcting color imbalances.
5) Approaches for adapting color images to be more visible for those with color vision deficiencies.
This document contains information about a lecture on digital image processing given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It provides the lecture schedule and contact information for Dr. Myint, as well as an outline of topics to be covered in Chapter 6, including color fundamentals, color models, color transformations, smoothing and sharpening of color images, and color image compression. The document discusses concepts such as the RGB, CMYK, and HSI color models and how they represent color, as well as methods for processing and manipulating colors in digital images.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
Edge detection algorithms identify points in a digital image where the image brightness changes sharply or has discontinuities. Common edge detection methods include gradient operators like Prewitt and Sobel, the Laplacian of Gaussian (LoG) used in Marr-Hildreth edge detection, and the Canny edge detector. The Canny edge detector applies smoothing, finds the image gradient, performs non-maximum suppression and double thresholding to detect edges with good localization and a single response to each edge.
RGB color stands for RED,GREEN and BLUE. This color model is used in computer monitors, television sets,
and theater. It's an additive color model.
CMYK refers to the four inks used in some color printing: cyan, magenta, yellow and key (black).
A color model is a specification for representing colors as combinations of primary colors. There are several common color models including RGB, CMY, YIQ, CIE, HSV, and HLS. The RGB model uses red, green, and blue primaries and is used in computer and television displays. The CMY model uses cyan, magenta, and yellow primaries and is used in color printing. The CIE model is based on human color perception and covers the full range of perceivable colors.
A color model is a system that creates a full range of colors from a small set of primary colors. There are two main types of color models: additive and subtractive. Additive color models like RGB use light to display colors on screens by combining red, green, and blue light, starting with black and ending with white. Subtractive color models like CMYK use pigments to print colors, starting with white and ending with black by combining cyan, magenta, yellow, and black inks. RGB is used for digital displays while CMYK is used for print because it reflects how inks absorb and reflect light.
This document introduces color models and discusses RGB and CMY color models. It provides an overview of the primary colors and color mixing properties of each model. RGB is an additive model that uses red, green, and blue light to create colors and is used for displays. CMY is a subtractive model that uses cyan, magenta, and yellow inks to create colors by absorbing light and is used for printing. Each model has different applications and advantages for displays versus printed materials.
Image restoration and degradation modelAnupriyaDurai
This document discusses image restoration and degradation. It provides an overview of image restoration techniques which attempt to reverse degradation processes and restore lost image information. Several types of image degradation are described, including motion blur, noise, and misfocus. Common noise models are explained, such as Gaussian, salt and pepper, Erlang, exponential, and uniform noise. Methods for estimating degradation models from observed images are also summarized, including using image observations, experimental replication of degradation, and mathematical modeling.
This document discusses digital image compression. It notes that compression is needed due to the huge amounts of digital data. The goals of compression are to reduce data size by removing redundant data and transforming the data prior to storage and transmission. Compression can be lossy or lossless. There are three main types of redundancy in digital images - coding, interpixel, and psychovisual - that compression aims to reduce. Channel encoding can also be used to add controlled redundancy to protect the source encoded data when transmitted over noisy channels. Common compression methods exploit these different types of redundancies.
Image enhancement techniques can be divided into spatial and frequency domain methods. Spatial domain methods operate directly on pixel values using techniques like basic gray level transformations, contrast stretching and thresholding. These manipulations are used to accentuate image features, improve display quality or aid machine analysis by modifying pixel intensities within an image.
This document discusses different types of electromagnetic radiation and their uses in digital image processing. It covers gamma rays, X-rays, ultraviolet rays, visible and infrared rays, microwaves, and radio bands. Applications described include medical imaging techniques like MRI, industrial inspection, astronomy, remote sensing, and law enforcement applications like license plate and fingerprint recognition. Radar imaging is also discussed as a key application using microwaves.
The document discusses pseudo color images and techniques for converting grayscale images to color. It defines pseudo color images as grayscale images mapped to color according to a lookup table or function. It describes various color schemes for this mapping, including grayscale schemes that use shades of gray and oscillating schemes that emphasize certain grayscale ranges in color. The document also discusses using piecewise linear functions and smooth non-linear functions to transform grayscale levels to color for purposes such as enhancing contrast or reducing noise in images.
This document discusses different color models used in computer graphics and printing. It explains that color models are systems for creating a range of colors from a small set of primary colors. The two main types are additive models which use light, like RGB, and subtractive models which use inks, like CMYK. RGB uses red, green and blue light and is for computer displays. CMYK uses cyan, magenta, yellow and black inks and is the standard for color printing. It provides details on how each model mixes colors and describes other models like HSV which represents color in terms of hue, saturation and value.
Digital image processing involves techniques to restore degraded images. Image restoration aims to recover the original undistorted image from a degraded observation. The degradation is typically modeled as the original image being operated on by a degradation function and additive noise. Common restoration techniques include spatial domain filters like mean, median and order-statistic filters to remove noise, and frequency domain filtering to reduce periodic noise. The choice of restoration method depends on the type and characteristics of degradation in the image.
Color fundamentals and color models - Digital Image ProcessingAmna
This presentation is based on Color fundamentals and Color models.
~ Introduction to Colors
~ Color in Image Processing
~ Color Fundamentals
~ Color Models
~ RGB Model
~ CMY Model
~ CMYK Model
~ HSI Model
~ HSI and RGB
~ RGB To HSI
~ HSI To RGB
This document discusses various techniques for image segmentation. It describes two main approaches to segmentation: discontinuity-based methods that detect edges or boundaries, and region-based methods that partition an image into uniform regions. Specific techniques discussed include thresholding, gradient operators, edge detection, the Hough transform, region growing, region splitting and merging, and morphological watershed transforms. Motion can also be used for segmentation by analyzing differences between frames in a video.
Here in the ppt a detailed description of Image Enhancement Techniques is given which includes topics like Basic Gray level Transformations,Histogram Processing.
Enhancement using Arithmetic/Logic Operations.
image averaging and image averaging methods.
Piecewise-Linear Transformation Functions
This document discusses color image processing and color models. It covers:
1) The basics of color perception and how humans see color through cone cells in the eye sensitive to different wavelengths.
2) Common color models like RGB, HSV, and CMYK and how they represent color.
3) Converting between color models and adjusting color properties like hue, saturation, and intensity.
4) Applications of color processing like pseudocoloring grayscale images and correcting color imbalances.
5) Approaches for adapting color images to be more visible for those with color vision deficiencies.
This document contains information about a lecture on digital image processing given by Dr. Moe Moe Myint at Technological University in Kyaukse, Myanmar. It provides the lecture schedule and contact information for Dr. Myint, as well as an outline of topics to be covered in Chapter 6, including color fundamentals, color models, color transformations, smoothing and sharpening of color images, and color image compression. The document discusses concepts such as the RGB, CMYK, and HSI color models and how they represent color, as well as methods for processing and manipulating colors in digital images.
Abstract Image Segmentation plays a vital role in image processing. The research in this area is still relevant due to its wide applications. Image segmentation is a process of assigning a label to every pixel in an image such that pixels with same label share certain visual characteristics. Sometimes it becomes necessary to calculate the total number of colors from the given RGB image to quantize the image, to detect cancer and brain tumour. The goal of this paper is to provide the best algorithm for image segmentation. Keywords: Image segmentation, RGB
This is about Image segmenting.We will be using fuzzy logic & wavelet transformation for segmenting it.Fuzzy logic shall be used because of the inconsistencies that may occur during segementing or
Edge detection algorithms identify points in a digital image where the image brightness changes sharply or has discontinuities. Common edge detection methods include gradient operators like Prewitt and Sobel, the Laplacian of Gaussian (LoG) used in Marr-Hildreth edge detection, and the Canny edge detector. The Canny edge detector applies smoothing, finds the image gradient, performs non-maximum suppression and double thresholding to detect edges with good localization and a single response to each edge.
RGB color stands for RED,GREEN and BLUE. This color model is used in computer monitors, television sets,
and theater. It's an additive color model.
CMYK refers to the four inks used in some color printing: cyan, magenta, yellow and key (black).
A color model is a specification for representing colors as combinations of primary colors. There are several common color models including RGB, CMY, YIQ, CIE, HSV, and HLS. The RGB model uses red, green, and blue primaries and is used in computer and television displays. The CMY model uses cyan, magenta, and yellow primaries and is used in color printing. The CIE model is based on human color perception and covers the full range of perceivable colors.
A color model is a system that creates a full range of colors from a small set of primary colors. There are two main types of color models: additive and subtractive. Additive color models like RGB use light to display colors on screens by combining red, green, and blue light, starting with black and ending with white. Subtractive color models like CMYK use pigments to print colors, starting with white and ending with black by combining cyan, magenta, yellow, and black inks. RGB is used for digital displays while CMYK is used for print because it reflects how inks absorb and reflect light.
This document introduces color models and discusses RGB and CMY color models. It provides an overview of the primary colors and color mixing properties of each model. RGB is an additive model that uses red, green, and blue light to create colors and is used for displays. CMY is a subtractive model that uses cyan, magenta, and yellow inks to create colors by absorbing light and is used for printing. Each model has different applications and advantages for displays versus printed materials.
This document discusses converting between RGB (used in additive color spaces like screens) and CMY (used in subtractive color spaces like printing) color models. It explains that each color in one model has a corresponding "opponent" color in the other model, with conversions between the two represented as a linear transformation. The document provides examples of converting white (0,0,0 in CMY) and green (1,0,1 in CMY) from RGB to CMY. It also works through converting a specific color, Blueprint (0.835, 0.655, 0.376 in CMY), between the two models.
Color image processing involves working with images that contain color information. There are two main types: full-color processing of images from color cameras or scanners, and pseudocolor processing which assigns a color to grayscale values. Color is described using properties like hue, saturation and brightness. Common color models for image processing include RGB, CMY, and HSI. RGB represents colors as combinations of red, green and blue primary colors. CMY uses cyan, magenta and yellow pigment primaries for printing. HSI separates intensity from hue and saturation, making it useful for color image algorithms.
This document provides an overview of the RGB and CMY color models. It discusses that RGB uses red, green, and blue primary colors in an additive model, and is used for computer and television screens. CMY uses cyan, magenta, and yellow in a subtractive model, where inks subtract brightness from white. It is used for color printing. Examples of each color model are also presented.
The document discusses color models and monitor resolution. It describes the RGB and CMYK color models, which are the two main types of color models. The RGB color model uses combinations of red, green, and blue light to make colors and is used for computer and television screens. The CMYK color model uses cyan, magenta, yellow, and black inks to make colors and is used for color printing. It also discusses monitor resolution, which is measured by the number of pixels horizontally and vertically, and recommends different resolutions depending on screen size.
"Color model" Slide for Computer Graphics PresentationAshek Shanto
This document provides an overview of different color models, including RGB, CMYK, and HSV models. It explains that RGB is an additive color model used for computer displays where colors result from transmitted light. CMYK is a subtractive color model used in printing, where colors are the result of reflected light. It describes the primary colors that make up each model - red, green, blue in RGB and cyan, magenta, yellow, and black in CMYK. The document also introduces the HSV color model which describes colors in terms of hue, saturation, and value.
The document discusses different color models including RGB, CMY, and HSV. The RGB model is an additive color model used for displays with primary colors of red, green, and blue. The CMY model is a subtractive color model used in printing with primary colors of cyan, magenta, and yellow. RGB uses light to create colors and is used for digital devices while CMY uses ink on paper. Both models have their advantages and are used in different applications depending on whether light or reflected light is used to produce colors.
This document discusses various color models used in computer graphics including RGB, HSV, HSL, CMY, and CMYK. It explains the key components of each model such as hue, saturation, value, and how colors are represented. Common applications of different color models are also summarized such as RGB for computer displays and CMYK for printing. In addition, the concepts of dithering and half-toning techniques used to reproduce colors on devices are introduced.
The document discusses various color models and color spaces including RGB, CMY, HSV, YUV, and grayscale. It provides details on:
- How RGB, CMY, and other color models represent and define color using combinations of primary/secondary colors.
- The differences between color models and how they are used for things like printing (CMY) vs displays (RGB).
- How HSV represents color in terms of hue, saturation, and value to better match human perception compared to RGB.
- Methods for converting between color models and spaces, as well as converting color images to grayscale. This includes weighted vs average methods and maintaining brightness information.
1. The document discusses various color models including RGB, CMY(K), HSV, HSL, and YIQ color models.
2. It describes the key components and properties of each color model such as hue, saturation, brightness. For example, RGB is an additive color model where primary colors are combined with light, while CMY(K) is a subtractive model used in printing.
3. Different color models have different applications based on their properties. For example, RGB is used for computer graphics and image processing while CMY(K) is used for printing and YIQ is used for television broadcasting.
The document discusses color models used in computer displays and printing. It describes the RGB and CMYK color models. The RGB color model uses additive color mixing of red, green, and blue light to produce colors and is used for computer displays. The CMYK color model uses subtractive color mixing of cyan, magenta, yellow, and black inks to produce colors and is used in color printing. Both models are described in terms of their primary colors and how combinations of those primary colors are used to produce other colors.
RGB is the color model used for light-based devices like computer monitors while CMYK is used for printed materials. RGB uses additive color by combining red, green, and blue light, while CMYK uses subtractive color by combining cyan, magenta, yellow, and black pigments. Hexadecimal codes are used to represent the intensity of each RGB color value from 00 to FF. Common color spaces include RGB, CMYK, and Pantone which uses proprietary spot colors. Understanding color theory involves concepts like hue, saturation and color psychology. Accessible color use ensures sufficient contrast between colors.
The document discusses the major color modes used in graphic design, including RGB, CMYK, LAB, greyscale, and bitmap. RGB uses red, green, and blue channels and is used for digital images. CMYK uses cyan, magenta, yellow, and black inks and is used for printed materials. LAB separates color into lightness, green-red, and blue-yellow channels. Greyscale varies colors from black to white. Bitmap uses only two colors, black and white. The document provides brief descriptions and uses of each color mode.
This document discusses color image processing and various color models. It begins with an overview of color fundamentals, including the visible light spectrum and primary/secondary colors. It then describes several color models - RGB, CMY, and HSI. Conversion between these color spaces is also covered. The document also discusses pseudocolor image processing techniques like intensity slicing and gray level to color transformations. Finally, it covers full-color image processing, including treating each color component separately, color complements, and color image smoothing and segmentation in RGB space.
RGB, CMYK, HSV, and YIQ are common color models. RGB represents colors as combinations of red, green, and blue. It is additive and used in computer graphics. CMYK is subtractive and represents colors as combinations of cyan, magenta, yellow, and black, and is used in printing. HSV represents colors in a hexacone using hue, saturation, and value. YIQ was used in broadcasting and separates luminance and chrominance information.
The document discusses color models used in image processing. It describes the RGB, CMYK, and HSB color models. RGB represents colors with red, green and blue values and is used in monitors and cameras. CMYK represents colors with cyan, magenta, yellow and black values and is used in printing. A color model mathematically describes how colors can be represented by numbers in a coordinate system.
This document provides an overview of color image processing. It discusses pseudo color image processing which involves assigning different colors to intensity values in a grayscale image. Full color image processing techniques are also described, including color transformation, intensity modification, color complements, color slicing, tone corrections, and color compression. Color models like RGB, CMY, and HSI are introduced. Various color image processing operations and their applications are explained such as color conversion, intensity modification, tone corrections, sharpening, and smoothing.
This presentation covers some of the basics of color gamut, including the differences between sRGB and Adobe RGB and different steps photographers and designers should take to get the most out of their photos and imagery.
The document discusses different color modes including RGB, CMYK, grayscale, bitmap, duotone, and indexed color. It provides details on:
- How each color mode represents and creates colors numerically
- When each color mode should be used based on the intended media (web, print, etc.)
- How to convert between color modes such as converting a color image to grayscale or a grayscale image to a duotone
- Tips for working in different color modes and avoiding unnecessary conversions
The document is an informative guide to understanding and working with different color modes in Photoshop.
This slide is a small demo of projectile motion application in a game and real life. There also a little visualization of angry birds game geometry and projectile motion implementations.
The document describes a vehicle polishing service station simulation model created in Arena. The model simulates vehicles arriving exponentially with a mean of 10 hours, then undergoing a quality control test with a 35% failure rate before being polished for 2 hours if they pass. The simulation will be built in Arena using Create, Assign, Process, Decide, Record, and Dispose modules to represent the arrival, testing, polishing, and exiting of vehicles. A diagram and simulation results will be presented, and potential upgrades to the model are discussed.
This document discusses the application of the Runge-Kutta (RK4) numerical integration method in game physics simulations. RK4 is a 4th order method that calculates estimations in 4 steps to provide extremely accurate solutions with less risk compared to previous methods. It is commonly used to model rocket trajectories and sniper shots in games. While more complex to implement than other methods, RK4's increased accuracy rewards it with more realistic in-game physics simulations and fewer bugs.
This document provides an overview of fingerprints, including their history, patterns, identifying features, and technology. It discusses how fingerprints were first used for identification in 1882 by Dr. Henry Fault. The three main fingerprint patterns are arches, loops, and whorls, which make up 5%, 65%, and 30% of patterns respectively. Fingerprints can be identified by features like crossovers, cores, bifurcations, ridge endings, islands, deltas, and pores. The document also briefly outlines fingerprint sensor technologies and processing.
Calculus is a branch of mathematics focused on limits, functions, derivatives, integrals, and infinite series. It was independently discovered in the mid-17th century by Isaac Newton and Gottfried Leibniz. Calculus has many applications across diverse fields including engineering, biology, physics, economics, and other areas. It is used to model varying rates of change and optimize solutions.
Rethinking Kållered │ From Big Box to a Reuse Hub: A Transformation Journey ...SirmaDuztepeliler
"Rethinking Kållered │ From Big Box to a Reuse Hub: A Transformation Journey Toward Sustainability"
The booklet of my master’s thesis at the Department of Architecture and Civil Engineering at Chalmers University of Technology. (Gothenburg, Sweden)
This thesis explores the transformation of the vacated (2023) IKEA store in Kållered, Sweden, into a "Reuse Hub" addressing various user types. The project aims to create a model for circular and sustainable economic practices that promote resource efficiency, waste reduction, and a shift in societal overconsumption patterns.
Reuse, though crucial in the circular economy, is one of the least studied areas. Most materials with reuse potential, especially in the construction sector, are recycled (downcycled), causing a greater loss of resources and energy. My project addresses barriers to reuse, such as difficult access to materials, storage, and logistics issues.
Aims:
• Enhancing Access to Reclaimed Materials: Creating a hub for reclaimed construction materials for both institutional and individual needs.
• Promoting Circular Economy: Showcasing the potential and variety of reusable materials and how they can drive a circular economy.
• Fostering Community Engagement: Developing spaces for social interaction around reuse-focused stores and workshops.
• Raising Awareness: Transforming a former consumerist symbol into a center for circular practices.
Highlights:
• The project emphasizes cross-sector collaboration with producers and wholesalers to repurpose surplus materials before they enter the recycling phase.
• This project can serve as a prototype for reusing many idle commercial buildings in different scales and sizes.
• The findings indicate that transforming large vacant properties can support sustainable practices and present an economically attractive business model with high social returns at the same time.
• It highlights the potential of how sustainable practices in the construction sector can drive societal change.
Value based approach to heritae conservation -.docxJIT KUMAR GUPTA
Text defines the role, importance and relevance of value based approach in identification, preservation and conservation of heritage to make it more productive and community centric.
2. GROUP MEMBERS
• Nazmin Nahar Nipa (133-15-2969)
• Sheikh Maruf Hossain(133-15-3051)
• Irteza Rahman (133-15-3057)
3. INDEX
• RGB and CMY color model
• Basic Examples
• Differences
• RGB to CMY and CMY to RGB conversion
4. RGB COLOR MODEL
• RGB color stands for RED,GREEN and BLUE. This color
model is used in computer monitors, television sets,
and theater. It's an additive color model.
• Additive color model means is that where we can
combine these three basic color in various ways to get
a new color.
5. RGB COLOR MODEL CONT.
When we will combine
RED+GREEN+BLUE=WHITE
[colors result from transmitted light]
We will see example later..
R G B (Red, Green ,Blue) is of 8-bit each.
Thus maximum possible range by individual is
0-255 (as 2^8 = 256)
6. CMY COLOR MODEL
A subtractive color model.
CMYK refers to the four inks used in some color
printing: cyan, magenta, yellow and key (black).
Subtractive because, The ink reduces the light
that would otherwise be reflected. Inks
subtract brightness from white.
Represent by value only 0 or 1
0 = no color
1= full color
7. EXAMPLES RGB
WHITE(max)
While we combined
R(255)+G(255)+B(255) =W
“+” means additive
We get white while intersecting
C (cyan) = G + B
M (magenta) = R + B
Y (yellow) = R + G
8. EXAMPLES CMY
“-” means subtractive
C (cyan) = W - R
M (magenta) = W - G
Y (yellow) = W – B
Key color black(max)
10. DIFFERENCES BETWEEN RGB AND
CMY CONT.
CMYK is a four-color mode that utilizes the colors cyan,
magenta, yellow and black in various amounts to create
all of the necessary colors
RGB is an additive type of color mode that combines the
primary colors, red, green and blue, in varying levels, to
create a variety of different colors.
RGB is vibrant
Whereas CMYK is not vibrant but good for reading
11. DIFFERENCES BETWEEN RGB AND
CMY CONT.
In RGB, when all three colors are combined and displayed
to their full extent, the result is a pure white.
In CMYK, when the first three colors are added together,
the result is not pure black, but rather a very dark brown.
CMYK is the primary color model used by color printers.
RGB is the typical color model used on electronic devices
such as computers, theaters etc.
12. CONVERSION POLICY
(RGB TO CMYK]
There is a core formula if we want to convert.
The R,G,B values are divided by 255 to change the range from 0..255 to 0..1:
• R' = R/255
• G' = G/255
• B' = B/255
13. CONVERSION POLICY
(RGB TO CMYK] CONT.
• The black key (K) color is calculated from the red (R'), green (G') and blue (B') colors:
• K = 1-max(R', G', B')
• The cyan color (C) is calculated from the red (R') and black (K) colors:
• C = (1-R'-K) / (1-K)
• The magenta color (M) is calculated from the green (G') and black (K) colors:
• M = (1-G'-K) / (1-K)
• The yellow color (Y) is calculated from the blue (B') and black (K) colors:
• Y = (1-B'-K) / (1-K)
14. EXAMPLE
• Lets think There is RGB value for violate(100,50,150)
• We want to convert it into CMYK
According to formula….
R’ =100/255 =0.392
G’ =50/255 =0.196
B’ =150/255 =0.588
Lets find the key for CMY
K =(1-0.588)=0.412 [Because this the largest value among given result]
(1-K) =0.588 [Will require later]
15. EXAMPLE
C=(1-R’-K)/(1-K) =0.333 [calculate from Red and black]
M=(1-G’-K)/(1-K) =0.667 [calculate from Green and black]
Y=(1-B’-K)/(1-K) =0. [calculate from Blue and black]
So,CMYK value for violate is (.333 , .667 , 0)
17. CONVERSION POLICY
(CMYK TO RGB]
• CMYK to RGB conversion formula
• The R,G,B values are given in the range of 0..255.
• The red (R) color is calculated from the cyan (C) and black (K) colors:
• R = 255 × (1-C) × (1-K)
• The green color (G) is calculated from the magenta (M) and black (K) colors:
• G = 255 × (1-M) × (1-K)
• The blue color (B) is calculated from the yellow (Y) and black (K) colors:
• B = 255 × (1-Y) × (1-K)
18. EXAMPLE
Given CMYK value for violate is (.333 , .667 , 0)
and key value is 0.412
(1-K) =.588
We need to bring out RGB value from that
According to formula….
R =255 x(1-0.333) x .588 = 100
G =255 x (1-0.667) x .588 = 50
B =255 x (1-0) x .588 =150
So,RGB value for Violate is (100,50,150)
If you want to calculate these problem online
Go to http://www.rapidtables.com/convert/color/rgb-to-cmyk.htm