Here I presented my presentation slide about color image processing.
In color image processing, an abstract mathematical model known as color space is used to characterize the colors in terms of intensity values. This color space uses a three-dimensional coordinate system. For different types of applications, a number of different color spaces exists. The saturation is determined by the excitation purity, and depends on the amount of white light mixed with the hue. A pure hue is fully saturated, i.e. no white light mixed in. Hue and saturation together determine the chromaticity for a given colour. Finally, the intensity is determined by the actual amount of light, with more light corresponding to more intense colours[1].
Achromatic light has no colour - its only attribute is quantity or intensity. Greylevel is a measure of intensity. The intensity is determined by the energy, and is therefore a physical quantity. On the other hand, brightness or luminance is determined by the perception of the colour, and is therefore psychological. Given equally intense blue and green, the blue is perceived as much darker than the green. Note also that our perception of intensity is nonlinear, with changes of normalised intensity from 0.1 to 0.11 and from 0.5 to 0.55 being perceived as equal changes in brightness[2].
Colour depends primarily on the reflectance properties of an object. We see those rays that are reflected, while others are absorbed. However, we also must consider the colour of the light source, and the nature of human visual system. For example, an object that reflects both red and green will appear green when there is green but no red light illuminating it, and conversely it will appear red in the absence of green light. In pure white light, it will appear yellow (= red + green).
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.
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.
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.
full color,pseudo color,color fundamentals,Hue saturation Brightness,color model,RGB color model,CMY and CMYK color model,HSI color model,Coverting RGB to HSI, HSI examples
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.
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.
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.
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.
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.
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.
full color,pseudo color,color fundamentals,Hue saturation Brightness,color model,RGB color model,CMY and CMYK color model,HSI color model,Coverting RGB to HSI, HSI examples
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.
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.
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.
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 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.
Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extract...IOSR Journals
This document describes a technique for colorizing grayscale images by matching texture features between the grayscale image and windows in a color reference image. The technique works by first converting the images to the YCbCr color space, which has decorrelated color channels that allow color to be transferred without artifacts. Texture features like energy, entropy, homogeneity, contrast and correlation are then extracted from windows in the color image and compared to the grayscale image to find the best matching window. The mean and standard deviation of color values in the matching window are then imposed on pixels in the grayscale image to transfer color, while retaining the original luminance values. This process is repeated on small windows across the image to colorize the entire grayscale input.
Study of Color Image Processing For Capturing and Comparing Imagespaperpublications3
The document discusses color image processing techniques for capturing and comparing images. It describes several color models used in digital image processing like RGB and HSI. Pixel-by-pixel image comparison is performed by comparing the color of pixels at the same coordinates. Factors that affect comparison include pixel tolerance, color tolerance, and inclusion of mouse pointer. OpenCV is analyzed for image capturing and color detection capabilities. The conclusion discusses testing the proposed method on different images and future work in comparing algorithms and image histograms.
The document discusses color image processing and color models. It covers color fundamentals including the visible light spectrum and human color vision. It describes two common color models - RGB and HSI. RGB represents colors as combinations of red, green, and blue primary colors. HSI represents colors in terms of hue, saturation and intensity. The document explains how to convert between the RGB and HSI color models and provides examples of manipulating images by first converting to HSI, applying changes, and converting back to RGB. Pseudocolor processing is also introduced as a technique to assign colors to grayscale values.
Color to Gray and back’ using normalization of color components with Cosine, ...IOSR Journals
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
Gray-level slicing is a technique used to highlight a specific range of gray levels in an image. There are two main approaches: 1) display the range of interest as white and other levels as black, and 2) brighten the range of interest while preserving other levels. Bit-plane slicing works similarly but highlights the contribution of each bit that makes up pixel values. Histograms provide a graphical representation of pixel intensity distributions in an image and are useful for image enhancement, statistics, and other processing tasks like compression and segmentation. Histogram equalization increases contrast by spreading out the most frequent intensity values.
Retooling of Color Imaging in ihe Quaternion Algebramathsjournal
A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work, we consider a full model for representation and processing color images in the quaternion algebra. Color images are presented in the threefold complex plane where each color component is described by a complex image. Our preliminary experimental results show significant performance improvements of the proposed approach over other well-known color image processing techniques. Moreover, we have shown how a particular image enhancement of the framework leads to excellent color enhancement (better than other algorithms tested). In the framework of the proposed model, many other color processing algorithms, including filtration and restoration, can be expressed.
RETOOLING OF COLOR IMAGING IN THE QUATERNION ALGEBRAmathsjournal
A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work,
we consider a full model for representation and processing color images in the quaternion algebra. Color
images are presented in the threefold complex plane where each color component is described by a
complex image. Our preliminary experimental results show significant performance improvements of the
proposed approach over other well-known color image processing techniques. Moreover, we have shown
how a particular image enhancement of the framework leads to excellent color enhancement (better than
other algorithms tested). In the framework of the proposed model, many other color processing algorithms,
including filtration and restoration, can be expressed.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
The document discusses three color models:
1) RGB color model represents colors as combinations of red, green, and blue primary colors. It can be represented as a color cube with these primary colors at the vertices.
2) CMY color model uses cyan, magenta, and yellow primaries and subtracts from white to produce colors.
3) HSV color model describes colors in terms of hue, saturation, and value. Hue is represented as an angle around a hexagonal boundary in a three-dimensional hexcone shape. Saturation corresponds to purity and value corresponds to intensity.
Edge detection is one of the most powerful image analysis tools for enhancing and detecting edges. Indeed, identifying and localizing edges are a low level task in a variety of applications such as 3-D reconstruction, shape recognition, image compression, enhancement, and restoration. This paper introduces a new algorithm for detecting edges based on color space models. In this RGB image is taken as an input image and transforming the RGB image to color models such as YUV, YCbCr and XYZ. The edges have been detected for each component in color models separately and compared with the original image of that particular model. In order to measure the quality assessment between images, SSIM (Structural Similarity Index Method) and VIF (Visual Information Fidelity) has been calculated. The results have shown that XYZ color model is having high SSIM value and VIF value. In the previous papers, edge detection based on RGB color model has low SSIM and VIF values. So by converting the images into different color models shows a significant improvement in detection of edges. Keywords: Edge detection, Color models, SSIM, VIF.
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.
The document discusses color representation in digital images. It describes how the human eye perceives color using rod and cone cells in the retina. There are three types of color receptors (red, green, blue) corresponding to the tristimulus theory of color vision. Color depth refers to the number of bits used to represent each color component, with 24-bit color allowing over 16 million colors. Indexed color reduces file size by storing a palette of colors and referencing indexes rather than full color values. Common color models like RGB, HSV, and CMYK are described along with their applications in computer monitors, imaging, and printing respectively.
This document provides an overview of key concepts in digital image fundamentals. It discusses the human visual system and image formation in the eye. It also covers image acquisition, sampling, quantization, and representation. Additionally, it defines concepts like spatial and intensity resolution and describes basic image processing operations and transforms. The goal is to introduce fundamental digital image processing concepts.
The document discusses color science and human color perception. It explains that color depends on the wavelength of light and how the eye perceives different wavelengths. The eye contains three types of cones that are most sensitive to red, green, and blue light. Combinations of these primary colors can reproduce any color visible to humans. Common color models used in devices include RGB used in computer monitors, CMYK used in printing, and YUV/YCbCr used in video and television.
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 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.
Colorization of Gray Scale Images in YCbCr Color Space Using Texture Extract...IOSR Journals
This document describes a technique for colorizing grayscale images by matching texture features between the grayscale image and windows in a color reference image. The technique works by first converting the images to the YCbCr color space, which has decorrelated color channels that allow color to be transferred without artifacts. Texture features like energy, entropy, homogeneity, contrast and correlation are then extracted from windows in the color image and compared to the grayscale image to find the best matching window. The mean and standard deviation of color values in the matching window are then imposed on pixels in the grayscale image to transfer color, while retaining the original luminance values. This process is repeated on small windows across the image to colorize the entire grayscale input.
Study of Color Image Processing For Capturing and Comparing Imagespaperpublications3
The document discusses color image processing techniques for capturing and comparing images. It describes several color models used in digital image processing like RGB and HSI. Pixel-by-pixel image comparison is performed by comparing the color of pixels at the same coordinates. Factors that affect comparison include pixel tolerance, color tolerance, and inclusion of mouse pointer. OpenCV is analyzed for image capturing and color detection capabilities. The conclusion discusses testing the proposed method on different images and future work in comparing algorithms and image histograms.
The document discusses color image processing and color models. It covers color fundamentals including the visible light spectrum and human color vision. It describes two common color models - RGB and HSI. RGB represents colors as combinations of red, green, and blue primary colors. HSI represents colors in terms of hue, saturation and intensity. The document explains how to convert between the RGB and HSI color models and provides examples of manipulating images by first converting to HSI, applying changes, and converting back to RGB. Pseudocolor processing is also introduced as a technique to assign colors to grayscale values.
Color to Gray and back’ using normalization of color components with Cosine, ...IOSR Journals
This document proposes three methods for converting a color image to grayscale while embedding color information, and then recovering the original color image from the grayscale version. The first method embeds normalized color components in the LH and HL subbands of the wavelet transform. The second method embeds them in the HL and HH subbands. The third method embeds in the LH and HH subbands. Experimental results show that the second method performs better than the first and third methods for color to grayscale conversion and recovery across different wavelet transforms. The goal is to reduce image size by a factor of three while retaining the ability to recover the original color image when needed.
Gray-level slicing is a technique used to highlight a specific range of gray levels in an image. There are two main approaches: 1) display the range of interest as white and other levels as black, and 2) brighten the range of interest while preserving other levels. Bit-plane slicing works similarly but highlights the contribution of each bit that makes up pixel values. Histograms provide a graphical representation of pixel intensity distributions in an image and are useful for image enhancement, statistics, and other processing tasks like compression and segmentation. Histogram equalization increases contrast by spreading out the most frequent intensity values.
Retooling of Color Imaging in ihe Quaternion Algebramathsjournal
A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work, we consider a full model for representation and processing color images in the quaternion algebra. Color images are presented in the threefold complex plane where each color component is described by a complex image. Our preliminary experimental results show significant performance improvements of the proposed approach over other well-known color image processing techniques. Moreover, we have shown how a particular image enhancement of the framework leads to excellent color enhancement (better than other algorithms tested). In the framework of the proposed model, many other color processing algorithms, including filtration and restoration, can be expressed.
RETOOLING OF COLOR IMAGING IN THE QUATERNION ALGEBRAmathsjournal
A novel quaternion color representation tool is proposed to the images and videos efficiently. In this work,
we consider a full model for representation and processing color images in the quaternion algebra. Color
images are presented in the threefold complex plane where each color component is described by a
complex image. Our preliminary experimental results show significant performance improvements of the
proposed approach over other well-known color image processing techniques. Moreover, we have shown
how a particular image enhancement of the framework leads to excellent color enhancement (better than
other algorithms tested). In the framework of the proposed model, many other color processing algorithms,
including filtration and restoration, can be expressed.
This document discusses various methods for contrast enhancement of images, including:
- Local color correction, which enhances contrast locally rather than globally.
- Simplest color balance, which clips a percentage of dark and light pixels before normalization.
- Screened Poisson equation, which acts as a high-pass filter using a single contrast parameter. Implementations of these methods in various color spaces like RGB, HSI, HSV, and HSL are provided. Local color correction is shown to perform better than global gamma correction by handling both dark and bright areas simultaneously.
The document discusses three color models:
1) RGB color model represents colors as combinations of red, green, and blue primary colors. It can be represented as a color cube with these primary colors at the vertices.
2) CMY color model uses cyan, magenta, and yellow primaries and subtracts from white to produce colors.
3) HSV color model describes colors in terms of hue, saturation, and value. Hue is represented as an angle around a hexagonal boundary in a three-dimensional hexcone shape. Saturation corresponds to purity and value corresponds to intensity.
Edge detection is one of the most powerful image analysis tools for enhancing and detecting edges. Indeed, identifying and localizing edges are a low level task in a variety of applications such as 3-D reconstruction, shape recognition, image compression, enhancement, and restoration. This paper introduces a new algorithm for detecting edges based on color space models. In this RGB image is taken as an input image and transforming the RGB image to color models such as YUV, YCbCr and XYZ. The edges have been detected for each component in color models separately and compared with the original image of that particular model. In order to measure the quality assessment between images, SSIM (Structural Similarity Index Method) and VIF (Visual Information Fidelity) has been calculated. The results have shown that XYZ color model is having high SSIM value and VIF value. In the previous papers, edge detection based on RGB color model has low SSIM and VIF values. So by converting the images into different color models shows a significant improvement in detection of edges. Keywords: Edge detection, Color models, SSIM, VIF.
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.
The document discusses color representation in digital images. It describes how the human eye perceives color using rod and cone cells in the retina. There are three types of color receptors (red, green, blue) corresponding to the tristimulus theory of color vision. Color depth refers to the number of bits used to represent each color component, with 24-bit color allowing over 16 million colors. Indexed color reduces file size by storing a palette of colors and referencing indexes rather than full color values. Common color models like RGB, HSV, and CMYK are described along with their applications in computer monitors, imaging, and printing respectively.
This document provides an overview of key concepts in digital image fundamentals. It discusses the human visual system and image formation in the eye. It also covers image acquisition, sampling, quantization, and representation. Additionally, it defines concepts like spatial and intensity resolution and describes basic image processing operations and transforms. The goal is to introduce fundamental digital image processing concepts.
The document discusses color science and human color perception. It explains that color depends on the wavelength of light and how the eye perceives different wavelengths. The eye contains three types of cones that are most sensitive to red, green, and blue light. Combinations of these primary colors can reproduce any color visible to humans. Common color models used in devices include RGB used in computer monitors, CMYK used in printing, and YUV/YCbCr used in video and television.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
2. Contents
The CMY and CMYK Colors Models
The HSI Color Model
Converting colors from RGB to HSI
Converting colors from HSI to RGB
1.
Color Image sharpening
2.
a.
b.
Basics of Full Color Image Processing
Color Transformations
Histogram Processing
Color Image smoothing
Formulations
3.
4.
a.
b.
5.
6.
3. The CMY and CMYK Colors Models
The CMY color model is a subtractive color model, mainly used in color printing.
In the CMY model we begin with white and take away the appropriate primary components to achieve the
desired color. For example, if we subtract red from white, we get green and blue, which is cyan.
Figure 1. shows a coordinate system using the three primaries' complementary colors: C (cyan), M (magenta), and
Y (yellow). The corner of the CMY color cube that is at (0,0,0) corresponds to white, whereas the corner of the cube
that is at (1,1,1) represents black. This conversion is performed using the simple operation:
Figure 1. The CMY color space.
4. According to Figure 1, equal amounts of the pigment primaries, cyan, magenta, and
yellow should produce black.
In practice, combining these colors for printing produces a muddy-looking black. So, in
order to produce true black, a fourth color, black, is added, giving rise to the CMYK color
model.
Why CMYK?
5. The HSI Color Model
Creating colors in the RGB and CMY models and changing from one model to the other is a straightforward
process.
The RGB, CMY, and other similar color models are not well suited for describing colors in terms that are
practical for human interpretation.
WHY HSI:
For example, one does not refer to the color of an automobile by giving the percentage of each of the
primaries composing its color.
These color systems are ideally suited for hardware implementations.
6. The HSI (hue, saturation, intensity) color model, decouples the intensity component from the color
carrying information (hue and saturation) in a color image.
the HSI model is an ideal tool for developing image processing algorithms based on color descriptions
that are natural and intuitive to humans.
Basics of HSI:
The HSI model uses three measures to describe colors:
Saturation: Saturation gives a measure of how much a pure color is diluted with white light.
Hue: Hue is a color Component that describes a pure color (pure yellow, orange, or red).
Intensity: Intensity is the chromatic notation of brightness of black & white range.
7. An RGB color image can be viewed as three monochrome intensity images (representing red, green, and
blue), so we could extract intensity from an RGB image.
In the arrangement shown in Fig. 2(a), the line (intensity axis) joining the black and white vertices is
vertical.
Conceptual relationships between the RGB and HSI color models:
To determine the intensity component of any color point in Fig. 2, we would simply pass a plane
perpendicular to the intensity axis and containing the color point.
The intersection of the plane with the intensity axis would give us a point with intensity value in the range
[0, 1].
Figure 2(a)(b):
Conceptual relationships between
the RGB and HSI color models
8. Hue can be determined also from a given RGB point, consider Fig. 2(b), which shows a plane defined by
three points (black, white, and cyan).
All points contained in the plane must have the same hue (cyan in this case), because the black and white
components cannot change the hue.
By rotating the shaded plane about the vertical intensity axis, we would obtain different hues.
9. In Fig. 3(a), this plane we see that the primary colors are separated by 120°. The secondary colors are
60° from the primaries.
Fig. 3(b) shows the same hexagonal shape and an arbitrary color point (shown as a dot).
Hue and saturation in the HSI color model:
The saturation is the length of the vector from the origin to the point.
The hue of the point is determined by an angle from some reference point (red).
Figure 3(a)(b)(c)(d):
Hue and saturation in the
HSI color model.
It makes no difference which shape is picked because a geometric transformation may transform any of these
shapes into one of the other two Fig. 3(c)(d).
10. Example: HSI Model of a Color Triangle and Circle:
Figure 4. The HSI color
model based on (a)
triangular and (b) circular
color planes. The
triangles and circles are
perpendicular to the vertical
intensity axis.
11. Converting colors from RGB to HSI
Step 1. Read a RGB image.
Step 2. Represent the RGB image in the range [0,1].
Step 3. Find HSI components:
Hue as H:
The angle:
Saturation as S:
Intensity as I:
12. Converting colors from HSI TO RGB
Step 1. If the values of HIS given in the interval [0,1], we need to find corresponding
RGB values in the same range.
Step 2. R, G, B values calculated as follows:
13.
14. Basics of Full Color Image Processing
Full color image processing fall into 2 categories: 1. per-color-component. 2.vector-based
processing to be equivalent
In 1st category we process each component image individually and then form a composite
processed color image from the individually processed component.
In 2nd category we work with color pixels directly. Because full color images have at least
three components, color pixels are really vectors.
Let c represent an arbitrary vector in RGB color space:
15. Color components are the function of co-ordinates(x, y) so we can write it as:
For an image of size MxN there are MN such vectors, c(x,y), for x=0,1,2,…,M-1; y=0,1,2,…,N-1.
It is important to keep in mind that this equation depicts a vector whose components are spatial
variables in x and y.
16. As an illustration, Fig. 5 shows neighborhood spatial processing of gray-scale and full-color images.
Figure 5. Spatial masks for gray-scale and RGB color images.
In order for per-color-component and vector-based processing to be equivalent, two conditions have to be
satisfied:
First, the process has to be applicable to both vectors and scalars.
Second, the operation on each component of a vector must be independent of the other components.
Neighborhood average:
Can’t satisfy the two conditions,
results of two full color processing
approaches are different.
17. Color Transformation
Color Transformations deal with processing the components of color image within the context of a single color model.
The image below shows a high resolution color image of a bowl of strawberries
and a cup of coffee.
Follows component of the initial CMYK scan.
Black represent 0 and white represent 1.
Formulation
As with the gray-level transformation, we model
color transformations using the expression
g(x,y) = T[f(x,y)]
Where f(x,y) is the color image, g(x,y) is the
transformed or processed color output image,
and T is an operator on f over a spatial
neighborhood of (x,y)
The pixel value are triplets or quartets from the
color space chosen to represent the images.
We will restrict our attention to color
transformations of the form
Si = Ti(r1,r2,…,n), where i=1,2,…,n
Si and ri are variables denoting the color
components of f(x,y) and g(x,y) at any point
(x,y).
n is the number of color components.
(T1,T2,…,Tn) is a set of transformation or color
mapping functions that operate on ri to produce
Si.
Example: Color Image and its Various Components
Full color image Cyan Magenta Yellow
Note the strawberries are composed of a large amount of magenta and
yellow because the image corresponding to these two CMYK components
are the brightest.
Black is used sparingly and is generally confined to the coffee and shadows
within the bowl of strawberries.
Black
18. Converting the CMYK to RGB component
image.
The strawberries contains a large amount
of red and very little green and blue.
The stem of the strawberries shows some
green.
Red Blue
Green
Computing the HSI component, the strawberries are relatively pure in
color; they poses the highest saturation or least dilution by white light
of any of the hues in the image.
Hue Intensity
Saturation
We note some difficulties in interpreting the hue component
There is a discontinuity in the HSI model where 0o and 360o
HUE is undefined for a saturation of 0.
The discontinuity of the model is most apparent around the
strawberries, which are depict in gray level values near the both black
(0) and white (1).
The result is an unexpected mixture of highly contrasting gray level to
represent a single color – red.
19. Example: Modify the intensity of the image on Figure using g(x,y)=k*f(x,y), where k=0.7.
In HSI color space, this can be
done with Hue: s1=r1, Saturation:
s2=r2, Intensity: s3=k*r3 (Only
intensity component must be
transformed)
In RGB space, this can be done
with three components,
si=k*ri (i=1,2,3.)
In CMY space, it requires a set of
linear transformations
si=k*ri + (1-k) (i=1,2,3.)
Original picture
Result of decreasing its
intensity by 30% (k=0.7)
20. Histogram Processing
Histogram equalization automatically determines a
transformation that seeks to produce an image with a uniform
histogram of intensity values.
Since color images are composed of multiple components,
however, consideration must be given to adapting the gray
scale technique to more than one component and/or
histogram.
It is generally unwise to histogram equalize the components
of a color image independently.
A more logical approach is to spread the color intensities
uniformly, leaving the hues (colors) unchanged.
The following example shows a color image of a caster
stand containing cruets and shakers whose intensity
component spans the entire (normalized) ranged of possible
values [0,1].
The image intensity before processing contains a large
number of dark colors that reduces the median intensity to
0.36.
Example: Histogram Equalization in the HSI Color Space
Original image Image HSI components
Histogram equalize the
intensity component without
altering the hue and
saturation. The image is
significantly brighter and
several grain of the wooden
table are now visible
Correcting by partially
increasing the image’s
saturation. Note the color
of oil and vinegar before
and after the saturation
change.
21. Color Image smoothing
can be carried out on a per-color-plane basis
Smooth only the intensity component of the HSI
representation and convert the processed result to
an RGB image for display
Let Sxy denote the set of coordinates defining a
neighborhood centered at (x,y) in an RGB color
image.
The average of the RGB component vectors in this
neighborhood is:
It’s follows from previous equations and the
properties of vector addition that,
Example: Color Image Smoothing by neighborhood averaging
The following example shows a color image with its RGB component
and its HSI component.
Applying a 5 X 5 smoothing filters to the RGB component independently
In the HSI components, we only smooth the intensity component
leaving the hue and saturation components unchanged.
RGB image Red Green
Hue Saturation Intensity
RGB corrected HSI corrected
Difference
between both
corrections
Blue
22. Color Image sharpening
We consider sharpening an image using Laplacian.
Laplacian of a vector is defined as a vector whose components
are equal to the Laplacian of the individual scalar components of
the input vector.
In an RGB color system, the Laplacian of vector c is
The following example takes an RGB color image and apply the
Laplacian to its RGB components.
It shows a sharpened image based on its HSI components by
combining the Laplacian of the intensity component with the
unchanged hue and saturation components.
Example: Sharpening with the Laplacian
RGB
image
Red
components
Blue
components
Green
components
Hue Saturation Intensity
RGB sharpened HSI sharpened
Difference
between both
corrections