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Image Processing
1. Introduction
Computer Engineering, Sejong University
Dongil Han
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What is Image Processing?
Science of manipulating a picture
• Enhance or distort an image
• Highlight certain features of an image
• Create a new image from portions of other images
• Restore an image that has been degraded
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Median Filtering
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Subtraction operation
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Original Image
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Negative Image
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Left Image
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Right Image
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Depth Map
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Wiener Filter
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Image Processing vs. Computer Graphics
They are companion technologies
Computer graphics
• The generation of synthetic images
• Works with 2-D, 3-D objects
Image processing
• Manipulation of images that have already been captured or generated
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Graphic images
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Image based rendering
Original image User marked edge Generated image
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Four basic classifications
Point processing• Modifies a pixel’s value based on that pixel’s original value
or position
Area processing• Modifies a pixel’s value based on its original value and the
values of neighboring pixels
Geometric processing• Change the position or arrangement of the pixels
Frame processing• Generate pixel values based on operations on two or more
images
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Four basic classifications
Point processing example Area processing example
Geometric processing example Frame processing example
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Image Processing Applications
Science and Space• NASA research projects provided millions of images
Movies• Image composition• Morphing• Image Warping
The paperless office• Document image processing
Medical industry• X-rays, ultrasound, CT(computed tomography),
MRI(magnetic resonance imaging), etc.
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Warping example
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Warping example
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Warping example
20/74Gamma-ray imaging
Image Processing Applications
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X-ray imaging
Image Processing Applications
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Image Processing Applications
Wilhelm Rontgen(German, 1845~1923)
ElectromagneticSpectrum
First X-ray imagetaken 1895/12/22
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MRI images
Image Processing Applications
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Image Processing Applications
Machine vision• Uses image processing and image analysis• Automated inspection• Extensively used in semiconductor manufacturing
Law enforcement• Fingerprint inspection• Iris recognition• Face recognition
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Image Processing Applications
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Image Processing Applications
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Image Processing Applications
http://www.ri.cmu.edu/projects/project_320.html
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Image Processing Applications
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Visual PerceptionStructure of the human eye
Cornea : 각막
Pupil : 동공
Iris : 홍채
Lens : 수정체
Retina : 망막
Fovea : 중심와
Blind Spot : 맹점
Optic Nerve : 시신경
BlindSpot
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light
Five distinct element
• Photoreceptors
(rods and cones)
• Horizontal cells
• Bipolar cells
• Amacrine cells
• Ganglion cell
The Human Retina
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• Rods- respond to dim light for BW vision- respond to form and movement- do not contribute color vision
• Cones- provide daylight color vision- one of 3 spectral types: S(blue), M(green), L(red)- concentrated in the center of retina
Rods and Cones
Visual Perception
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The left eye
Visual Perception
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Visual Perception
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Visual Perception
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Blind Spot Test
Visual Perception
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Spectral response of cones- see Figure 1.7
Visual Perception
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Visual Perception
S ML
Three types of Cones• S type : responds Short-wavelength of visible light• M type : responds Middle-wavelength of visible light• L type : responds Long-wavelength of visible light
Slightly different from conventional R, G, B color model
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Simultaneous Contrast
Visual Perception
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Perceived Brightness
Visual Perception
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Visual Perception
Brightness adaptation
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Optical Illusions
Visual Perception
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Color representation
Color model(color space)• Way of representing colors and their relationship to each
other• Different image processing systems use different color
models TV camera, Display monitor : RGB color space Publishing industry : CMYK color space Broadcasting : YIQ color space HSI Color space : Hue(색상), Saturation(채도),
Intensity(명도) XYZ, LAB, YUV, YCbCr, etc.
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Color representationRGB color space
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Color representation
CMYK color space• Primary color : cyan, magenta, yellow
CMY color spaceC = 1.0 – RM = 1.0 - GY = 1.0 - B
• CMYK color space Black(K) is added Black is more pure black than the combination of other
three colors Black ink is cheaper than colored ink K = min(C, M, Y)
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• Mixture of light (빛의 합성)– Additive primaries(가법혼색)– Red, Green, Blue– Color monitor
• Mixture of pigments (염료의 합성)
– Subtractive primaries(감법혼색)– Cyan, Magenta, Yellow– Color printer
Primary Color
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HSI color space
More intuitive and perceptually relevant than RGB space
Hue(색상) : represents color spectrum– Color information such as green, orange, blue, etc.
Saturation (채도) : represents the purity of color– greater the saturation, further the color is from
gray/white/black– Magnitude of Saturation : Pink < Red
Intensity (명도) : represents the brightness of color– 0: black, 1 : white
Suitable for representing the surface color(표면색) of subjects
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RGB to HSI
GBif
GBifH
360
Conversion formulas from RGB to HIS :
Red : = 0o
Normalized RGB values between [0,1] is used.
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1
)])(()[(
)]()[(5.0cos
BGBRGR
BRGR
)(3
1BGRI
)],,[min()(
31 BGR
BGRS
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RGB, HSI Color Model
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HSI Color Model
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HSI Color Example
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Color image and its components
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Color representation
YCbCr color space• Y : encodes luminance information• Cb : encodes blueness information• Cr : encodes redness information
Y = 0.299R + 0.587G + 0.114BCb = -0.16874R – 0.33216G + 0.5BCr = 0.5R – 0.41869G – 0.08131B
• Separates the luminance from the color information• Widely used in JPEG, MPEG compression• There are several ways to convert to/from YCbCr
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Image capture
Image capturing device• Photo diode : 0-D sensor, Output voltage waveform is
proportional to light• Scanner : 1-D image sensor• Camera : 2-D image sensor
CCD(Charge Coupled Device) CMOS(Complementary Metal-Oxide Semiconductor) Widely used in digital cameras and light sensing
instruments
• sampling and quantization step is required to use in the computer
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Image capture
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Image capture
Two functions of CCD• converting photons to an electrical charge• moving this charge at the proper time
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Image acquisition process
Creating a Digital ImageFor computer processing, an image intensity function f(x,y) must be digitized both spatially and in amplitude. And f(x,y) must be non-zero and finite.
0 < f(x,y) <
The function f(x,y) can be characterized by illumination and reflectance components.
f(x,y) = i(x,y) r(x,y)
Where
0 < i(x,y) < and 0 < r(x,y) < 1.
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Some typical ranges of i(x,y)• Clear day : 90,000 lm/m2
• Cloudy day : 10,000 lm/m2
• Full moon : 0.1 lm/m2
• Typical commercial office : 1000 lm/m2
Some typical ranges of r(x,y)• Black velvet : 0.01• Stainless steel : 0.65• Flat white wall paint : 0.80• Silver plated medal : 0.90• Snow : 0.93
Image acquisition process
lm(루멘) : 1 cd의 균일한 광도의 광원으로부터 단위입체각의 부분에방출되는 광속. 전 구면의 중심에 대한 입체각은 4p 이므로 1 cd의점광원에서 방출되는 전광속은 4 lm이다.
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Sampling and Quantization
sampled image :digitizing the
coordinate values
quantized image :digitizing the
amplitude values
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Picture elements = pels = pixels
Digital Image
Digital image• sampled and quantized image
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– spatial resolution: number of rows and columns (e.g. 256256)– gray-level resolution: 0 f(x,y) L, L = 2k-1(L = 1, 63, 255,
1023, etc.)– (x,y) : spatial coordinate, t : temporal coordinate
Digital Image Representation
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Digital Image Representation
Image representation• f(x,y) : 2-D still image• f(x,y,z) : 3-D still image• f(x,y,t) : 2-D moving image, video sequence• f(x,y,) : 2-D color image
The meaning of f(.)• Brightness of the subjects
- TV camera, scanner, etc.• Transmission factor of the subjects, especially bodies
- X-ray imaging, Ultrasonic imaging , etc.• Distance between the subjects and detector
- sonar imaging, radar, etc.• Temperature of the subjects
- infrared camera , etc.
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Sampling effect
Digital Image Representation
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Digital Image Representation
Sampling theorem(표본화 정리)• If a function x(t) contains no frequencies higher than B hertz,
it is completely determined by giving its ordinates at a series of points spaced 1/(2B) seconds apart.
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Spatial Aliasing
0
255
The spatial alising effect caused by undersampling
Original sampledUndersampled, Alising
Correctly sampledimage line-detailfrequency is captured
Original image line with brightnessdetails
Undersampled imageline-detail frequencyis aliased
Pixels are sampled at arate 2 times the detailfrequency
Pixels are sampled at arate less than half thedetail frequency
• It appears when the details in the image are sampled at a rate less than twice their spatial frequency
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Original image : Barbara
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Filtered image : Barbara
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Size reduction only Filtering and size reduction
Size reduction comparison
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Digital Image Representation
Quantizationeffect
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Quantizationeffect
false contourDigital Image Representation
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Digital Image Storage
- The digitization process requires decisions about number of rows M, columns N and for the number, L, of discrete gray levels.L typically is an integer power of 2
L = 2k
- The number, b, of bits required to store a digitized image is
b = M x N x k
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Software
Simplest image format : PNM(Portable Anymap Format) file format• uncompress image format : easy to handle• PBM(portable bitmaps): binary image file format• PGM(portable graymap): gray-level image file format• PPM(portable pixelmap): color image file format• File format is defined in the File header
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PNM file format
PNM header + image data PNM header
Magic Number What type of file and how data is storedImage Width Width of image in pixelsImage Height Height of image in pixelsMax Maximum gray scale/color channel value
• Header is written in ASCII form• Each field is separated by white space (blanks, tabs, line
feeds, or carriage returns)• Image data: ASCII or raw binary (called RAWBITS)• Max field is not used in PBM files• #: comment line, can’t be located in first line of file
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PNM file format
Magic number
Format ASCII RAWBITSPBM P1 P4PGM P2 P5PPM P3 P6
Image data format
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PNM file format
PPM file exampleP3720 256255210 0 0214 0 0204 0 0209 0 0199 0 0204 0 0194 0 0199 0 0189 0 0194 0 0184 3 0189 8 0179 15 0184 19 0174 26 0179 31 0169 38 0174 43 0164 49 0169 54 0159 61 0164 66 0154 73 0158 77 5149 84 26153 89 31144 96 52148 101 57
720x256 imageImage
data in file
Magic number
Image size
Channel level
Image data
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