1-1 Chapter 1: Introduction 1.1. Images An image is worth thousands of words

1-2 Human Eyeball Camera

Digital Images Digital image: content of image array Pixel: picture element Gray level: pixel value (0 – 255) Camera Sensor array Image array

1-4 Imaging Model

1-5 Scene: a 3-D function, g(x,y,z) Image: a 2-D function, f(x,y) Origin ○

1-6 Spatial Resolution

1-7 Grayscale resolution (Quantization) False contours

1-8 ○ Two major applications of image processing (A) Human perception (B) Machine interpretation (A) Human perception Image sharpening

1-9 Noise removal Deblurring

1-10 (B) Machine interpretation Image segmentation Edge detection Line drawing

1-11 ○ Three levels of image processing Low-level processing – e.g., Noise removal (smoothing) Contrast enhancement Mid-level processing – e.g., Edge detection Image segmentation High-level processing – e.g., Image understanding Scene interpretation

1-12 Intensity (grayscale) image ○ Types of images Binary image

Color image Indexed (or palette) color image

1-14 X-ray image

1-15 X-ray transmission computerized tomography (CT) image

1-16 Gamma-ray images

1-17 Ultrasound images

1-18 Ultraviolet images

1-19 Radio images

1-20 Multispectral images

1-21 Range images

1-22 Moire images

1-23 Structure light images

1-24

1-25 Simultaneous contrast Optical illusion ○ Image Perception

1-26 Overshoot and Undershoot e.g., Mack band pattern