Introduction to compositing. What is compositing?  The combination of two images to produce a single image  Many ways we can do this, especially in.

Slides:



Advertisements
Similar presentations
1 ECE 495 – Integrated System Design I Introduction to Image Processing ECE 495, Spring 2013.
Advertisements

Start with your equation Move the # term to the other side, and leave a space Determine what HALF of the coefficient of X is Factor the left side Write.
Photoshop Lab colorspace A quick and easy 26 step process for enhancing your photos.
1 3 Computing System Fundamentals 3.5 Data Representation.
APPLICATIONS OF METEOSAT SECOND GENERATION (MSG) RGB COMPOSITES WITH CHANNEL 12 AND THEIR INTERPRETATION.
3D Compositing & Special Effects Implementation Pertemuan 07 Matakuliah : U0584 / DIGITAL COMPOSITING II Tahun : 2009.
Original Finished Anaglyph What are we trying to do?
Numbers in Images GCNU 1025 Numbers Save the Day.
Advanced Computer Graphics CSE 190 [Spring 2015], Lecture 5 Ravi Ramamoorthi
Green Screen. Objectives: 2. Understand what the difference is between a Luma key and a Chroma key. By the end of todays lesson students will: 3. Understand.
Advanced Computer Graphics (Spring 2005) COMS 4162, Lecture 6: Image Compositing, Morphing Ravi Ramamoorthi
RGB color model Skills: none IT concepts: combining red, green and blue light to generate colors This work is licensed under a Creative Commons Attribution-Noncommercial-
Steganography Rayan Ghamri.
Color (1) Turtle class contains a method to change the pen color Note: before using Color class, you should add following line in the top of the source.
Whiteboardmaths.com © 2007 All rights reserved
02/14/02(c) University of Wisconsin 2002, CS 559 Last Time Filtering Image size reduction –Take the pixel you need in the output –Map it to the input –Place.
Contains 16,777,216 Colors. My Car is red My Car is red How do I add colors to my web page? Notepad Browser Works with the “Standard” colors: Red, Green,
Case Study: Losing the Rainforest, One Tree at a Time Synthetic Aperture Radar (SAR) satellite images are used to study change in the Amazon rainforest.
Digital Colour Theory. What is colour theory? It is the theory behind colour mixing and colour combination.
Choose Text Styles Sparingly for Emphasis with or without COLOR Bold Italic Underline Combination.
Digital Images The digital representation of visual information.
Prerequisite Skills VOCABULARY CHECK Copy and complete using a review word from the list; whole number, sum, difference, product, quotient. 1. When you.
© 1999 Rochester Institute of Technology Color. Imaging Science Workshop for Teachers ©Chester F. Carlson Center for Imaging Science at RIT Color Images.
Image Processing & Perception Sec 9-11 Web Design.
1.05 On The Importance of Color. Color… color never changes Knowing your way around color is one of the two things that any designer must, indispensably,
Digital Terminology. Bitmap A representation consisting of rows and columns of dots of a graphic image stored in computer memory. To display a bitmap.
Filtering and Color To filter a color image, simply filter each of R,G and B separately Re-scaling and truncating are more difficult to implement: –Adjusting.
Dividing by 10, 100 or 1000 ÷ 100 = 13.4 ÷ 100 = 38.3 ÷ 100 = 24.8 ÷ 100 = 85.5 ÷ 100 = 79.7 ÷ 100 = C. ÷x 0 + On ² - Ans = √ (-) Whole.
Color and Resolution Introduction to Digital Imaging.
CSC Computing with Images
infinity-project.org Engineering education for today’s classroom 2 Outline How Can We Use Digital Images? A Digital Image is a Matrix Manipulating Images.
Do Now: Do Not Log In. Take out your notebook and a pen. Good morning! Do Now: Do Not Log In. Take out your notebook and a pen. Good morning! Aim: Review.
Prerequisite Skills Vocabulary Check 1. When you add two numbers, the result is called the. ? ANSWER sum 2. When you multiply two numbers, the result is.
Digital Image Compositing. Compositing for volume rendering c1 c2 c3 The initial pixel color = Black opaque Back-to-Front compositing: use ‘under’ operator.
Special functions. Piece-wise functions A function is piece-wise, if it is defined over a union of domains which have different rules for each set of.
Satellite Band Combinations.. Bands 3, 2, 1 in red, green, blue. This is considered the natural colour composition. It is usually used primarily for display.
CISC 110 Day 3 Introduction to Computer Graphics.
Pseudo / Color Image Processing Fasih ur Rehman. Color Image Processing Two major areas of Color Image Processing –Pseudo Color Image Processing Assigning.
BINARY Toby Wilson. LEARNING OBJECTIVES  Be able to convert binary to denary  Be able to convert denary into binary  Be able to explain how computers.
Implementing the By: Matthew Marsh Supervisors: Prof Shaun Bangay Mrs Adele Lobb segmentation technique as a plugin for the GIMP.
It's a palindrome! Announcements March USACO o 2nd to last round o last day to take it  if busy, can start late too because of CAHSEE tomorrow.
More Digital Representation Discrete information is represented in binary (PandA), and “continuous” information is made discrete.
© Hamilton Trust Keeping Up Term 2 Week 4 Day 3 Objective: Multiply 3-digit numbers by single-digit numbers using the grid method.
Elimination Method - Systems. Elimination Method  With the elimination method, you create like terms that add to zero.
Intro to Graphics from Chapter 2 of Java Software Solutions
♣Multiplying Decimals♣
Images Data Representation.
Introduction to Skin and Face Detection
When the denominators are the same,
Advanced Computer Graphics
The Colour of Light: Additive colour theory.
Image Processing & Perception
Computer Graphics Different Images File.
DIGITAL SIGNAL PROCESSING
COMS 161 Introduction to Computing
Images Presentation Name Course Name Unit # – Lesson #.# – Lesson Name
Game Controller Introduction.
Combinations of Functions; Composite Functions
© Partitioning Numbers ©
Images Presentation Name Course Name Unit # – Lesson #.# – Lesson Name
Colors Computers build colors from Red, Green, and Blue; not Red, Blue, and Yellow. RGB = Red Green Blue Creating Colors Red + Blue = Purple No Red, No.
Which One Doesn’t Belong?
Objective: To know the equations of simple straight lines.
Non-numeric Data Representation
The Image The pixels in the image The mask The resulting image 255 X
Adding integers with tiles and number lines
Translate 5 squares left and 4 squares up.
HW#4: Image Compositing
Objective: To know the equations of simple straight lines.
Presentation transcript:

Introduction to compositing

What is compositing?  The combination of two images to produce a single image  Many ways we can do this, especially in the digital domain  Perform operations on pixel values of each image according to a given equation  We treat an image as having three channels: red, green and blue.  These combine to produce the whole image

Simple example: add  Simply add the values for each channel (R, G and B) of the (N x M) images P and Q:  For i = 0 to N-1 For j = 0 to M-1 RGB_O ij = RGB_P ij + RGB_Q ij  Generally this won’t be very useful!

+ =

Other ways of combining images  Usually when we are combining images one can be considered a background image and one a foreground image  Often there may be more than two images, but they can usually be split into background and foreground pairs  What we need is a way to decide which pixels from each image we use at each location in the image

Simple example  We have a background image from which we want to use the left half combined with the right half of the foreground image + =

Simple example  For i = 0 to N-1 For j = 0 to M-1 If i<(N/2) RGB_O ij = RGB_BG ij Else RGB_O ij = RGB_FG ij

Compositing  We need a flexible way of deciding which pixels from which image we use at each position  To do this we can create an N x M array of values that define which pixels we use from which image.  As a simple example, use a value of 0 if we want to use the background pixel and 1 if we want to use the foreground pixel  This N x M array is itself an image, consider 0 as black and 1 as white  For the previous example:

Mattes  Such an image is a called a matte  The black and white pixels can occur anywhere and the image can be created using any image production technique (e.g. manually in Photoshop)  In general, we can use shades of grey to define different amounts of each image rather than a simple ‘on-off’ switch  Consider a more realistic example…

The process  We’ll now look at how this works in principle  We use the equation: O = (FG x M) + (BG x (1-M)) where BG is the background image, FG is the foreground image and M is the matte  This means that we multiply the foreground by the matte and the background by the inverse of the matte before adding the results

x x= = +=

Generating mattes  Mattes can be created in many ways, ranging from fully automatically to fully manually  If the images we’re using are generated by a computer (e.g. as part of a computer animation) then the alpha information may be generated at the same time as the images  If the images are from live action video or film, we need to create the matte either manually or automatically  For anything but simple images, this is complex

Creating mattes from images  If we have to create a matte for an existing image, there are ways that we can ease the process  If the pixels that we want replaced in the image have some common feature, we can use this to create our matte  This is what the ‘blue screen’ (or ‘green screen’) technique is based on

Colour keying  If all the pixels in our foreground image that we want replaced with our background image are the same colour it is easy to create a matte  With video/film, we can achieve this by filming the action against a constant colour backdrop  Usually a bright blue or green is used because these colours do not occur in natural scenes very often  When we post-process the images, we choose the backdrop colour to define our matte  This process is called colour keying

Example Blue screen originalGenerated matte Background imageComposited image

Colour keying  In practice, no matter how evenly we light the backdrop it will not be exactly one shade of colour  We therefore usually use colours within a certain threshold of the chosen one  We can also feather or soften the edges to help blend the two images and make the composite look more natural

Creating mattes  If we cannot shoot the original footage against a backdrop we need other ways to create a matte  One way is to import the images into an image manipulation package and create a matte manually

Manually creating mattes  This is obviously a labour intensive process, especially as in general we will be using sequences of moving images at between 24 and 30 frames per second!  There are ways of helping the process semi-automatically using image processing techniques

Image Processing  We can enhance the image to help define a matte by processing the image  One approach is to enhance the edges in the image by high pass filtering  Edges represent high frequencies in the image and so can be brought out by filtering the image

High pass filtering  We can high pass filter an image by looking at the difference between a pixel and its surrounding pixels  If they are very different, then there is a high frequency present and probably an edge  We move across the image pixel by pixel

Feature tracking  If the element of the image that we are trying to composite is similar in each frame (e.g. it is a an object that moves across the frame), then we can create a matte and track the object with it  This relies on automatically locating the object in each frame and moving the matte accordingly  To do this we can specify certain features to assist tracking

Example

Alpha channels  The matte information can be considered as another channel in addition to the RGB information  Hence, if we are using a 24 bit system for colour information (8 bits for each colour) we store an additional 8 bits of alpha  We then have an RGBA, 32 bit image

In Premier