CS 414 – Multimedia Systems Design
Lecture 4 – Digital Image
Representation
Klara Nahrstedt
Spring 2009
CS 414 - Spring 2009
Administrative
Group Directories will be established
hopefully today (or latest by Friday)
MP1 will be out on 1/28 (today)
Start by reading the MP1 and organizing
yourself as a group this week, start to read
documentation, search for audio and video
files.
CS 414 - Spring 2009
Images – Capturing and
Processing
CS 414 - Spring 2009
Capturing Real-World Images
Picture – two dimensional image captured
from a real-world scene that represents a
momentary event from the 3D spatial
world W2 W1
r
W3
F r= function of (W1/W3);
s s=function of (W2/W3)
CS 414 - Spring 2009
Image Concepts
An image is a function of intensity values
over a 2D plane I(r,s)
Sample function at discrete intervals to
represent an image in digital form
matrix of intensity values for each color plane
intensity typically represented with 8 bits
Sample points are called pixels
CS 414 - Spring 2009
Digital Images
Samples = pixels
Quantization = number of bits per pixel
Example: if we would sample and quantize
standard TV picture (525 lines) by using
VGA (Video Graphics Array), video
controller creates matrix 640x480pixels,
and each pixel is represented by 8 bit
integer (256 discrete gray levels)
CS 414 - Spring 2009
Image Representations
Black and white image
single color plane with
2 bits
Grey scale image
single color plane with
8 bits
Color image
threecolor planes
each with 8 bits
RGB, CMY, YIQ, etc.
Indexed color image
singleplane that 4 gray levels 2gray levels
indexes a color table
Compressed images
TIFF, JPEG, BMP, etc.
Digital Image Representation
(3 Bit Quantization)
CS 414 - Spring 2009
Color Quantization
Example of 24 bit RGB Image
24-bit Color Monitor
CS 414 - Spring 2009
Image Representation Example
24 bit RGB Representation (uncompressed)
128 135 166 138 190 132
129 255 105 189 167 190
229 213 134 111 138 187
128 138 135 190 166 132
129 189 255 167 105 190
229 111 213 138 134 187
Color Planes
Graphical Representation
CS 414 - Spring 2009
Image Properties (Color)
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Color Histogram
CS 414 - Spring 2009
Image Properties (Texture)
Texture – small surface structure, either
natural or artificial, regular or irregular
Texture Examples: wood barks, knitting
patterns
Statistical texture analysis describes
texture as a whole based on specific
attributes: regularity, coarseness,
orientation, contrast, …
CS 414 - Spring 2009
Texture Examples
CS 414 - Spring 2009
Spatial and Frequency Domains
Spatial domain
refers to planar region of
intensity values at time t
Frequency domain
think of each color plane
as a sinusoidal function of
changing intensity values
refers to organizing pixels
according to their
changing intensity
(frequency)
CS 414 - Spring 2009
Image Processing Function: 1. Filtering
Filter an image by replacing each pixel in the
source with a weighted sum of its neighbors
Define the filter using a convolution mask, also
referred to as a kernel
non-zero values in small neighborhood, typically
centered around a central pixel
generally have odd number of rows/columns
CS 414 - Spring 2009
Convolution Filter
100 100 100 100 100
100 100 50 50 100 0 1 0
100 100 100 100 100 0 0 0
100 100 100 100 100 X 0 0 0 =
100 100 100 100 100
100 100 100 100 100
100 100 50 50 100
100 100 50 100 100
100 100 100 100 100
100 100 100 100 100
CS 414 - Spring 2009
Mean Filter
20 12 14 23
45 15 19 33 1 1 1
1 1 1 1
55 34 81 22 9
8 64 49 95 1 1 1
Subset of image Convolution filter
CS 414 - Spring 2009
Mean Filter
20 12 14 23
45 15 19 33 1 1 1
1 1 1 1
55 34 81 22 9
8 64 49 95 1 1 1
Subset of image Convolution filter
CS 414 - Spring 2009
Common 3x3 Filters
1 1 1 1 1 1
Low/High pass filter 1 1 1 1 1 9 1
9
1 1 1
1 1 1
1 2 1
Blur operator
1 2 1 2
13
1 2 1
1 2 1 1 0 1
0 0 0 2 2
H/V Edge detector 0
1 2 1 1 0 1
Example
CS 414 - Spring 2009
Image Function: 2. Edge Detection
Identify areas of strong
intensity contrast
filter
useless data; preserve
important properties
Fundamental technique
e.g.,use gestures as input
identify shapes, match to
templates, invoke commands
Edge Detection
CS 414 - Spring 2009
Simple Edge Detection
Example: Let assume single line of pixels
5 7 6 4 152 148 149
Calculate 1st derivative (gradient) of the
intensity of the original data
Using gradient, we can find peak pixels in image
I(x) represents intensity of pixel x and
I’(x) represents gradient (in 1D),
Then the gradient can be calculated by convolving the
original data with a mask (-1/2 0 +1/2)
I’(x) = -1/2 *I(x-1) + 0*I(x) + ½*I(x+1)
CS 414 - Spring 2008
Basic Method of Edge Detection
Step 1: filter noise using mean filter
Step 2: compute spatial gradient
Step 3: mark points > threshold as edges
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Mark Edge Points
Given gradient at each
pixel and threshold
mark pixels where
gradient > threshold as
edges
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Compute Edge Direction
Calculation of Rate of Change in
Intensity Gradient
Use 2nd derivative
Example: (5 7 6 4 152 148 149)
Use convolution mask (+1 -2 +1)
I’’(x) = 1*I(x-1) -2*I(x) + 1*I(x+1)
Peak detection in 2nd derivate
is a method for line detection.
CS 414 - Spring 2009
Summary
Other Important Image Processing Functions
Image segmentation
Image recognition
Formatting
Conditioning
Marking
Grouping
Extraction
Matching
Image synthesis
CS 414 - Spring 2009
