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Chapter 1: Introduction1.1. Images

An image is worth thousands of words

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• 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