This document discusses various color models and rendering techniques. It describes additive and subtractive color models, including the RGB, CMY, and HSV color models. It also discusses illumination models, including ambient light, diffuse reflection, and specular reflection. Common rendering techniques like Gouraud shading and Phong shading are summarized, which interpolate lighting across triangle surfaces. Ray tracing is also briefly explained as a technique for simulating light paths.
In imaging science, image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. Images are also processed as three-dimensional signals where the third-dimension being time or the z-axis.
Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.
Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance.
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.
Any colour that can be specified using a model will correspond to a single point within the subspace it defines. Each colour model is oriented towards either specific hardware (RGB,CMY,YIQ), or image processing applications (HSI).
In imaging science, image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, or a video, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it. Images are also processed as three-dimensional signals where the third-dimension being time or the z-axis.
Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.
Closely related to image processing are computer graphics and computer vision. In computer graphics, images are manually made from physical models of objects, environments, and lighting, instead of being acquired (via imaging devices such as cameras) from natural scenes, as in most animated movies. Computer vision, on the other hand, is often considered high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images (e.g., videos or 3D full-body magnetic resonance scans).
In modern sciences and technologies, images also gain much broader scopes due to the ever growing importance of scientific visualization (of often large-scale complex scientific/experimental data). Examples include microarray data in genetic research, or real-time multi-asset portfolio trading in finance.
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.
Any colour that can be specified using a model will correspond to a single point within the subspace it defines. Each colour model is oriented towards either specific hardware (RGB,CMY,YIQ), or image processing applications (HSI).
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
Do Not just learn computer graphics an close your computer tab and go away..
APPLY them in real business,
Visit Daroko blog for real IT skills applications,androind, Computer graphics,Networking,Programming,IT jobs Types, IT news and applications,blogging,Builing a website, IT companies and how you can form yours, Technology news and very many More IT related subject.
-simply google:Daroko blog(professionalbloggertricks.com)
• Daroko blog (www.professionalbloggertricks.com)
• Presentation by Daroko blog, to see More tutorials more than this one here, Daroko blog has all tutorials related with IT course, simply visit the site by simply Entering the phrase Daroko blog (www.professionalbloggertricks.com) to search engines such as Google or yahoo!, learn some Blogging, affiliate marketing ,and ways of making Money with the computer graphic Applications(it is useless to learn all these tutorials when you can apply them as a student you know),also learn where you can apply all IT skills in a real Business Environment after learning Graphics another computer realate courses.ly
• Be practically real, not just academic reader
An illumination model, also called a lighting model and sometimes referred to as a shading model, is used to calculate the intensity of light that we should see at a given point on the surface of an object.
Surface rendering means a procedure for applying a lighting model to obtain pixel intensities for all the projected surface positions in a scene.
A surface-rendering algorithm uses the intensity calculations from an illumination model to determine the light intensity for all projected pixel positions for the various surfaces in a scene.
Surface rendering can be performed by applying the illumination model to every visible surface point.
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
The Importance of Terminology and sRGB Uncertainty - Notes - 0.4Thomas Mansencal
The latest version of this presentation is available at this URL: https://www.slideshare.net/thomasmansencal/the-importance-of-terminology-and-srgb-uncertainty-notes-05
The organised and formatted embodiment of the Colour Science notes I have taken along the years.
It is aimed at the VFX industry, and is the work-in-progress subset of a broader and generic Colour Science presentation.
By analysing the explanation of the classical heapsort algorithm via the method of levels of abstraction mainly due to Floridi, we give a concrete and precise example of how to deal with algorithmic knowledge. To do so, we introduce a concept already implicit in the method, the ‘gradient of explanations’. Analogously to the gradient of abstractions, a gradient of explanations is a sequence of discrete levels of explanation each one refining the previous, varying formalisation, and thus providing progressive evidence for hidden information. Because of this sequential and coherent uncovering of the information that explains a level of abstraction—the heapsort algorithm in our guiding example—the notion of gradient of explanations allows to precisely classify purposes in writing software according to the informal criterion of ‘depth’, and to give a precise meaning to the notion of ‘concreteness’.
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
Do Not just learn computer graphics an close your computer tab and go away..
APPLY them in real business,
Visit Daroko blog for real IT skills applications,androind, Computer graphics,Networking,Programming,IT jobs Types, IT news and applications,blogging,Builing a website, IT companies and how you can form yours, Technology news and very many More IT related subject.
-simply google:Daroko blog(professionalbloggertricks.com)
• Daroko blog (www.professionalbloggertricks.com)
• Presentation by Daroko blog, to see More tutorials more than this one here, Daroko blog has all tutorials related with IT course, simply visit the site by simply Entering the phrase Daroko blog (www.professionalbloggertricks.com) to search engines such as Google or yahoo!, learn some Blogging, affiliate marketing ,and ways of making Money with the computer graphic Applications(it is useless to learn all these tutorials when you can apply them as a student you know),also learn where you can apply all IT skills in a real Business Environment after learning Graphics another computer realate courses.ly
• Be practically real, not just academic reader
An illumination model, also called a lighting model and sometimes referred to as a shading model, is used to calculate the intensity of light that we should see at a given point on the surface of an object.
Surface rendering means a procedure for applying a lighting model to obtain pixel intensities for all the projected surface positions in a scene.
A surface-rendering algorithm uses the intensity calculations from an illumination model to determine the light intensity for all projected pixel positions for the various surfaces in a scene.
Surface rendering can be performed by applying the illumination model to every visible surface point.
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
The Importance of Terminology and sRGB Uncertainty - Notes - 0.4Thomas Mansencal
The latest version of this presentation is available at this URL: https://www.slideshare.net/thomasmansencal/the-importance-of-terminology-and-srgb-uncertainty-notes-05
The organised and formatted embodiment of the Colour Science notes I have taken along the years.
It is aimed at the VFX industry, and is the work-in-progress subset of a broader and generic Colour Science presentation.
By analysing the explanation of the classical heapsort algorithm via the method of levels of abstraction mainly due to Floridi, we give a concrete and precise example of how to deal with algorithmic knowledge. To do so, we introduce a concept already implicit in the method, the ‘gradient of explanations’. Analogously to the gradient of abstractions, a gradient of explanations is a sequence of discrete levels of explanation each one refining the previous, varying formalisation, and thus providing progressive evidence for hidden information. Because of this sequential and coherent uncovering of the information that explains a level of abstraction—the heapsort algorithm in our guiding example—the notion of gradient of explanations allows to precisely classify purposes in writing software according to the informal criterion of ‘depth’, and to give a precise meaning to the notion of ‘concreteness’.
Program to implement Cohen-Suderland Line Clipping Algorithm. Make provision to specify the input line, window for clipping and view port for displaying the clipped image.
Region filling and object removal by exemplar based image inpaintingWoonghee Lee
To get rid of (an) object(s) at a picture or to restore a picture from scratches or holes, Criminisi at el. suggested an algorithm which is combied "texture synthesis" and "inpainting". I made the slide to present at a class to introduce this algorithm. I refered a slide http://bit.ly/1Ng7DNt. I wish this slide may help you to understand the algorithm. Thank you.
An illumination model, also called a lighting model and sometimes referred to as a shading model, is used to calculate the intensity of light that we should see at a given point on the surface of an object.
The Importance of Terminology and sRGB Uncertainty - Notes - 0.5Thomas Mansencal
The organised and formatted embodiment of the Colour Science notes I have taken along the years.
It is aimed at the VFX industry, and is the work-in-progress subset of a broader and generic Colour Science presentation.
The human visual system can distinguish hundreds of thousands of different color shades and intensities, but only around 100 shades of grey. Therefore, a great deal of extra information in an image may be contained in color. This additional information can simplify image analysis such as object identification and extraction based on color.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2. What is the color?
• Color is a sensation produced by the human eye and nervous
system.
• Color is the aspect of things that is caused by differing
qualities of light being reflected or emitted by them.
- It is related to light, but an understanding of the properties of
light is not sufficient to understand color, and is especially not
sufficient to understand the art of color reproduction.
3. Color Model
• A color model is an orderly system for creating a whole
range of colors from a small set of primary colors.
• The range of colors that can be described by
combinations of other colors is called a Color Gamut.
• Color model is of 2 types:-
* Additive
* Subtractive
5. Color model for Raster Graphics
• Hardware Oriented models
RGB color model
CMY color model
YIQ color model
• User Oriented model
HSV color model
6. RGB Color Model:
* Additive color model.
* For computer displays.
* Uses light to display color.
* Colors result from transmitted
light.
* Red + Green + Blue = White.
7.
8. * Subtractive color model.
* Used in ink-jet plotters that
deposit pigment on paper.
* Uses ink to display color.
* Colors result from reflected
light.
* K for Black
* Cyan + Magenta + Yellow = Black.
8
CMY(K) Color Model:
9. • Cyan, magenta, and yellow are complements of red, green, and
blue.
• We can convert RGB into CMY representation by C =
1 - R
M = 1 - G
Y= 1 - B
• Conversion from CMY into RGB
R = 1 - C
G = 1 - M
B = 1 - Y
10. YIQ color model
• Y => Luminous or Brightness information.
• I => Hue (The degree to which a stimulus can be
described as similar to or different from stimuli ).
For eg. Rust = root color or hue is orange
Navy = root color or hue is blue
• Q => Purity (Intensity of hue)
11. • Y parameter
The combination of Red, Green & Blue intensities are chosen
for Y parameter to give standard luminously curve. Since Y
contains luminous information, black and white television
monitors use only Y signal. It occupies about 4 MHz bandwidth.
12. • I parameter
It contains Orange, Cyan hue information that provides the
Flesh tone shading (use color range to pull darker flesh from the
shadows of images)
It occupies the a bandwidth of approximately 1.5 MHz.
• Q parameter
It contains Green, Magenta hue information.
It occupies about 0.6 MHz bandwidth.
13. • RGB signal can be converted to television signal using NTSC
encoder which converts RGB values to YIQ values, then
modulate and superimpose the I and Q information on the Y
signal.
• Conversion form RGB values to YIQ using following
transformation:-
Y 0.299 0.587 0.144 R
I = 0.596 - 0.275 -0.321 . G
Q 0.212 - 0.528 0.311 B
14. • NTSC video signal can be converted to RGB signal using NTSC
decoder, which separates the video signal into YIQ components,
then converts to RGB using inverse matrix transformation :-
R 1.000 0.956 0.620 Y
G = 1.000 -0.272 -0.647 . I
B 1.000 -1.108 1.705 Q
15. HSV color model
• The hue (H) of a color refers to which pure color it resembles.
All tints, tones and shades of red have the same hue.
• Hues are described by a number that specifies the position of
the corresponding pure color on the color wheel, as a fraction
between 0 and 1. Value 0 refers to red; 1/6 is yellow; 1/3 is
green; and so forth around the color wheel.
• The saturation (S) of a color describes how white the color is.
A pure red is fully saturated, with a saturation of 1; tints of red
have saturations less than 1; and white has a saturation of 0.
• The value (V) of a color, also called its lightness, describes
how dark the color is. A value of 0 is black, with increasing
lightness moving away from black.
17. • The outer edge of the top of the cone is the color wheel, with
all the pure colors. The H parameter describes the angle around
the wheel.
• The S (saturation) is zero for any color on the axis of the cone;
the center of the top circle is white. An increase in the value of
S corresponds to a movement away from the axis.
• The V (value or lightness) is zero for black. An increase in the
value of V corresponds to a movement away from black and
toward the top of the cone.
19. • How do you make something look 3D?
• Shading that is appropriate for the lighting and the primary cue for
3D appearance.
20. Light
• Light energy falling on a surface can be:
Absorbed Reflected Transmitted
They make an object visible
• The amount of energy absorbed, transmitted or deflected
depends on the wavelength or intensity of the light.
21. • The characterstics of the light reflected or transmitted depends
on:
* The surface orientation
* The surface properties of the object
* Composition of the light source
* Direction of the light source
* Geometry of the light source
22. Illumination model
• Illumination model is used to calculate the intensity of the light
that we should see at a given point on the surface of the object.
• It is also known as Lighting model or Shading model. There are 2
types of illumination model:-
(1) Local illumination model:- Direct illumination of surface
by light sources.
(2) Global illumination model:- All light or surface
interactions for entire environment.
23. • A common simple illumination model is built from 3
components:-
* Ambient light
* Diffuse reflections
* Specular reflection
24. Ambient light
• Uniform from all directions (an object is illuminated uniformly).
• Not dependent on light, view or object direction, nor distance to
anything else.
• La = Ka * Ia
• Where,
La is a reflected ambient light .
Ka is an ambient coefficient.
Ia is an ambient light.
25.
26. Diffuse Reflection
• Diffuse reflections consider Point lights (Radiates equal intensity in
all direction ) which comes from one direction to generate shading
properties a change in color intensity across the surface of an object
in relation to light sources.
• Depends on direction of light (L) and surface normal (N)
Id = Ip(L.N)
where, Ip is intensity of point light
•
27.
28. Specular reflection
• Component of reflection due to mirror-like
reflection off shiny surface.
• Depends on perfect reflection direction, viewer
direction, and surface normal
Is = Ip(R.V)^ n
where n is Specular exponent, determining falloff
rate.
• Shininess
29.
30. Gouraud shading
• Gouraud shading, developed by Henri Gouraud in 1971, was
one of the first shading techniques developed in computer
graphics.
• Gouraud shading is most often used to achieve continuous
lighting on triangle surfaces by computing the lighting at the
corners of each triangle and linearly interpolating the resulting
colors for each pixel covered by the triangle.
• Gouraud shading - Wikipedia, the free encyclopedia.htm
31. Phong Shading
• It describes the way a surface reflects light as a
combination of the Diffuse reflection of rough
surfaces with the Specular reflection of shiny
surfaces .
32.
33. Ray tracing
• It is a technique for generating an image by tracing the path of
light through pixels in an image plane and simulating the effects
of its encounters with virtual objects.