Teaching Notes on Image Processing For senior students
Lectures 2: Elementary Image Operations Dr. Mohammed Abdel-Megeed Salem Scientific Computing Department, Faculty of Computer and Information Sciences, Ain Shams University in Cairo
Outline • Course Information & Objectives • Introduction – Definitions • Images, Image Processing, Image Analysis, Computer Vision, Computer Graphics
– Image Processing: History, Objectives and Applications
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Course Info - Contents 1
Introduction – Definitions • Images, Image Processing, Image Analysis, Computer Vision, Computer Graphics
– Image Processing: History, Objectives and Applications
2
Elementary Image Information and Operations
3
Fundamentals of Signal and Image Processing
4
Acquisition and Digitizing Images
5
Image Processing Operations
6
Applications
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Quick Check [Lecture 2] • Draw a new Venn diagram for the term “Image” similar to the diagram of Castleman but based on your own understanding. • Repeat the last image denoising example on one of your own images. Write down your observations/ comments.
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Definitions – Images [Lecture 2]
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Simple Example [Lecture 2] • The following code should run on Matlab: RGB = imread('saturn.png'); I = rgb2gray(RGB); J =imnoise(I,'gaussian',0,0.025); imshow(J) K = wiener2(J,[5 5]); figure, imshow(K)
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Definitions – Images [Lecture 2] • The image is treated as a grid of discrete elements, ordered from top to bottom and left to right. • The first component r (the row) increases downward, while the second component c (the col.) increases to the right. • Pixel coordinates are integer values and range between 1 and the length of the row or column.
The Pixel Coordinate System
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Definitions – Images [Lecture 2] • A grey level image:
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Definitions – Images [Lecture 2] • RGB-coloured image:
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Course Info - Contents 1
Introduction
2
Elementary Image Information and Operations – Image Statistics – Histogram
– Point Operations
3
Fundamentals of Signal and Image Processing
4
Acquisition and Digitizing Images
5
Image Processing Operations
6
Applications
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Objective • The objective of this lecture is to gain a touchable feeling about the basic information of images and basic operation may be operated on images. This understanding will help us in the coming lecture to study the fundamentals of Signal and Image Processing. • For you to achieve this objective you should try to implement these operations on images created by your own or, when this is not available, on standard set of images. Ex: http://imagedatabase.cs.washington.edu/groundtruth/ Salem, Teaching Notes on Image Processing, 2015
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Image Statistics • • • •
Image Size: total number of pixels. Minimum gray level, Maximum gray level, Range, the difference between the maximum and the minimum. • Entropy: a measure of randomness – used to characterize the texture of the image.
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Image Statistics Image Size: 768x1024 = 786432 Minimum gray level: 0 Maxmum gray level: 255 Range: 255 Mean gray level: 142,9 Mode gray level: 160 Median gray level: 151 Entropy: 7.7554
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Image Histogram • Provides information on grey scale distribution • Represents the Discrete Probability Function for the colour distribution
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Image Histogram • Most used: normalized histogram with relative frequencies hg of grey values • For M pixel with G grey values (g = 0, 1,… G-1) and ag as number of pixels with grey value g holds:
and
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Image Histogram Interpretation of typical histograms • Homogeneous image: one bar of length 1 • Two level image: two bars • Dark image with low contrast: there are more values of hg for lower g • Bright image with low contrast: there are more values of hg for larger g • High contrast image: all hg equal (e.g. = 1 / 256)
• Typical foreground / background image: bimodal histogram
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Image Histogram
Marsa Matrouh - Dark
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Marsa Matrouh
Marsa Matrouh - Light
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Image Histogram
Marsa Matrouh – High Contrast
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Marsa Matrouh
Marsa Matrouh – Low Contrast
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Report 2-1 • Apply the operations of increasing and decreasing the brightness and contrast on one of your own images. Show the histogram of the output images in comparison to the input image and comment the results.
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Image Histogram Nearly bimodal histogram
Meffert and Salem
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Image Histogram Properties of histograms
• Spatial information is lost • Every image has a special histogram • BUT: every histogram is not assigned to a single image • Example: if objects in an image were rearranged then the histogram is not changed • The structure of an image can not derived from the histogram
• Histogram tells the user if an image is scaled well
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Image Histogram Two different images and their (identical) histogram
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Image Histogram N-dimensional histogram
• Useful for a lot of joint processes • In image processing (e.g.): multi-channel images • Definition with
number of corresponding pixel pairs
• Example: number of pixels having the value gμ in the matrix for red and gν in the matrix for blue Salem, Teaching Notes on Image Processing, 2015
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Image Statistics - Histogram red
Two-dimensional histogram
number of pixels with the two defined grey values blue Meffert and Salem
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Image Statistics - Histogram
The baboons colour image BUNT
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Image Statistics - Histogram
G
B
G
B
R
Color
BUNT
R
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Image Statistics - Histogram
BUNT
R
G
red
green
5
5 hRlabe l
hGlabe l
512
512
0
100
200 label
300
0
100
200
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label H1
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Image Statistics - Histogram
BUNT
R
B
blue
red
5
5
hRlabe l
hBlabe l
512
512
0
100
200 label
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label
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Image Statistics - Histogram
BUNT
G
B
blue
green
5
5
hGlabe l
hBlabe l
512
512
0
100
200 label
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label
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Wikipedia: Mandrillus is the genus of the mandrill and its close relative the drill. These two species are closely related to the baboons, and until recently were lumped together as a single species of baboon. Both Mandrillus species have long furrows on either side of their elongated snouts. The adult male mandrill's furrows are blue, while the furrows of the drill are black. Both species are terrestrial, living on the ground of the rainforest and occasionally grasslands of Central Africa.
Foto: Haremsmännchen
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Image Statistics - Histogram • Cumulative Histogram
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Basic Image Processing Operations • Image-Processing operations may be divided into 3 classes based on information required to perform the transformation. • 1) Point Operations • process according to the pixel’s value alone (single pixel).
• 2) Neighborhood processing • process the pixel in a small neighborhood of pixels around the given pixel. • 3)Transforms • process entire image as one large block
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Point Operations • The gray value of each pixel in the output image g(x, y) depends on the gray value of only the corresponding pixel of the input image f (x,y). Every pixel of f(x, y) with the same gray level maps to a single (usually different) gray value in the output image.
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Image Arithmetic • Image arithmetic is the implementation of standard arithmetic operations. – –
– – –
Addition Subtraction Multiplication Division Complement
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Image Arithmetic • Let x is the old gray value, y is the new gray value, c is a positive constant. • • • • •
Addition: y = x + c Subtraction: y = x - c Multiplication: y = cx Division: y = x/c Complement: y= 255 - x
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Image Arithmetic • Image arithmetic has many uses in image processing for example, image subtraction. Image Arithmetic Saturation Rules: • The results of integer arithmetic can easily overflow the data type for storage. • Ex: unit8 [0-255] Result Class Truncated Value 300 uint8 255 -45 uint8 0 10.5 uint8 11 Salem, Teaching Notes on Image Processing, 2015
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Report 2-2 • Discuss different techniques to rescale the range of the output images after applying the arithmetic operations into the range of [0,255].
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Image Arithmetic - Addition • Lighten the image • Some details may be lost
+ 128 = Salem, Teaching Notes on Image Processing, 2015
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Image Arithmetic - Subtraction • Darken the image • Some details may be lost
- 128 Salem, Teaching Notes on Image Processing, 2015
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Image Arithmetic - Multiplication Lighten the image Some details may be lost but less than addition
* 2 Salem, Teaching Notes on Image Processing, 2015
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Image Arithmetic - Division Darken the image Some details may be lost but less than subtraction
/ 2 Salem, Teaching Notes on Image Processing, 2015
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Image Arithmetic – Addition vs Multiplication
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Image Arithmetic – Subtraction vs Division
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Image Arithmetic – Complement • Create the negative image
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Image Arithmetic • Image arithmetic functions can be used in combination to perform a series of operations.
I = imread('rice.png'); I2 = imread('cameraman.tif'); K = imdivide(imadd(I,I2), 2); % not recommended Salem, Teaching Notes on Image Processing, 2015
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Image Arithmetic • imlincomb performs all the arithmetic operations in the linear combination in double precision and only rounds and saturates the final result. • K = imlincomb(.5,I,.5,I2); % recommended
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Report 2-3 • Apply the above mentioned operations on one of your own images or on MarsaMatrouhGrey image. Show the histograms and comment the results.
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Image Arithmetic – Denoising/Sharping
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Image Arithmetic – Fault Detection Where is the defect?
Image g(x,y) (no defect) Salem, Teaching Notes on Image Processing, 2015
Image f (x,y) (with defect) Lecture 2
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Report 2-4 • Consider the previous images of circuits without and with defects. Apply manual alignment to align both images to each other and then apply the subtraction to find the defect.
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Image Arithmetic – Moving Object Detection
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Image Arithmetic – Moving Object Detection • Identify the active cells?
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Thresholding • Segmentation can be achieved simply by thresholding the image at a particular intensity level. • The technique that‘s most frequently employed for determining thresholds involves the histogram of the image. • Problems: – ignores spatial context, – selection of the threshold parameter
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Thresholding
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Thresholding
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Thresholding
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Double Thresholding • A larger threshold T2 yields “seeds” of the segmentation, which are accepted in all cases. • They grow in all directions where grey values can be found that exceed some smaller threshold T1. • method does take into account spatial context
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Report 2-5 • Program a small image processing code for fault detection for textile or glass industry. For example your program should be able to detect broken/ splitted/ cracked glass.
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Contacts Image Processing for senior students, 2015 Dr. Mohammed Abdel-Megeed M. Salem Faculty of Computer and Information Sciences, Ain Shams University Abbassia, Cairo, Egypt Tel.: +2 011 1727 1050 Email:
[email protected] Website: https://sites.google.com/a/fcis.asu.edu.eg/salem/home
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