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1-1
Chapter 1: Introduction1.1. Images
An image is worth thousands of words
1-2
• Human Eyeball
• Camera
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1.2. Digital Images
Digital image: content of image array
Pixel: picture element
Gray level: pixel value (0 – 255)
Camera
Sensor array
Image array
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• Imaging Model
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Scene: a 3-D function, g(x,y,z)
Image: a 2-D function, f(x,y)
Origin ○
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• Spatial Resolution
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• Grayscale resolution (Quantization)
False contours
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○ Two major applications of image processing
(A) Human perception
(B) Machine interpretation
(A) Human perception
• Image sharpening
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• Noise removal
• Deblurring
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(B) Machine interpretation
Image segmentationEdge detection
Line drawing
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○ Three levels of image processing
Low-level processing – e.g., Noise removal (smoothing) Contrast enhancement Mid-level processing – e.g., Edge detection Image segmentationHigh-level processing – e.g., Image understanding Scene interpretation
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• Intensity (grayscale) image
○ Types of images
• Binary image
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• Color image
Indexed (or palette) color image
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• X-ray image
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• X-ray transmission computerized tomography (CT) image
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• Gamma-ray images
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• Ultrasound images
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• Ultraviolet images
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• Radio images
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• Multispectral images
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• Range images
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• Moire images
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• Structure light images
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Simultaneous contrast
Optical illusion
○ Image Perception
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Overshoot and Undershoot
e.g., Mack band pattern