240 373 Image Processing Montri Karnjanadecha montricoe psu

240 -373 Image Processing Montri Karnjanadecha montri@coe. psu. ac. th http: //fivedots. coe. psu. ac. th/~montri 240 -373: Chapter 1: Introduction 1

Chapter 1 Introduction 240 -373: Chapter 1: Introduction 2

Definition of Image Processing • Processing of an image, typically by a computer, to produce another image • The computer processing of pictures ( the manipulation of images by computer) 240 -373: Chapter 1: Introduction 3

Applications of Image Processing • • • Pictorial databases Graphic design Medical imaging Object recognition Picture enhancement Microscopy 240 -373: Chapter 1: Introduction 4

Digital Image Processing System Console Image digitizer Input image storage Computer Output image storage Image recorder Program library 240 -373: Chapter 1: Introduction 5

Image Shape • An image is usually defined as rectangular grids of pixels • Image(1. . M, 1. . N), Image (10, 5( 240 -373: Chapter 1: Introduction 6

Color Standards • A pixel color is represented as a point in 3 -D space. Axis may be labeled as independent colors such as R, G, B or may use other independent indicators such as Hue, lightness, saturation. • RBG, HSV, HSL are the most popular. 240 -373: Chapter 1: Introduction 7

RGB • RGB (Red green blue) – For CRT display (TV, computer monitor) – Additive combination of r g and b 240 -373: Chapter 1: Introduction 8

HSV • HSV (Hue Saturation Value) Hue is effectively a measure of the wavelength of the main color. It has a value between 0 -255 (0 -360 o, Red = 0 o, Green=120 o and Blue=240 o. ) Hue can be calculated from RGB values as follows: Rh = R - min (R, G, B) Gh = G - min (R, G, B) Bh = B - min (R, G, B) At least on of these values is 0. Hue value is in between. 240 -373: Chapter 1: Introduction 9

HSV For example if Rh = 0 Hue angle = (240 x. Bh + 120 x. Gh ) / (Bh + Gh) If two are zero, then hue is the angle corresponding to the third non-zero color. If three are zero, then there is no color hue. The monitor will display a gray level (between black and white). 240 -373: Chapter 1: Introduction 10

Saturation • Saturation is the amount of pure hue in the final color If Saturation = 0, final color is without hue ( white light only) If Saturation = 255, no white light in final color Saturation = (max(R, G, B) - min (R, G, B)) / max(R, G, B) 240 -373: Chapter 1: Introduction 11

Value • Value (brightness) is a measure of the intensity of the brightest component and given by Value = max(R, G, B) 240 -373: Chapter 1: Introduction 12

The HSV Model 240 -373: Chapter 1: Introduction 13

HSV Example Given R=100, G=200, B= 40, convert this RGB color model to HSV. Rh = 100 - min(100, 200, 40) = 60 Gh = 200 - min (100, 200, 40) = 160 Bh = 40 - min (100, 200, 40) = 0 H = (60*0 + 160*(120*256/360))/(60+160( 240 -373: Chapter 1: Introduction 14

HSV Example (Continued( Saturation = (max(R, G, B) - min (R, G, B)) / max(R, G, B) = (max(100, 200, 40)-min(100, 200, 40/(( max(100, 200, 40( 200 -40)/200) = %80 = 0. 8 = Saturation = 80*256/100 = 204 Value = max(100, 200, 40)200 = %78 = 100/256*200 = 240 -373: Chapter 1: Introduction 15

240 -373: Chapter 1: Introduction 16

HLS (Hue Lightness Saturation) • Similar to HSV except that the hue angle start at Blue = 0 o, and the model is double cone with a lightness axis going from L=0 (black) to L=1 (white) • For HLS, hue is calculated the same way as for HSV model except Blue = 0 and lightness and saturation are given by: • Lightness = (max(R, G, B) - min(R, G, B))/2 (max+min)/(max-min) if L <= 0. 5 (max-min)/(2 -max-min) otherwise • Saturation = 240 -373: Chapter 1: Introduction 17

HLS 240 -373: Chapter 1: Introduction 18

The Human Vision • Better than any camera yet developed • Eye has 2 classes of discrete light receptors; Cones and rods • 6 -7 million cones, sensitive to bright light • >= 75 Million rods, sensitive to light intensity but not color • eye color perceptions 240 -373: Chapter 1: Introduction 21

Color Additive • Problem with CRT 240 -373: Chapter 1: Introduction 22