Digital Images. Kyungim Baek. Department of Information and Computer Sciences. ICS 101 (November 1, 2016) Digital Images 1

Transcription

1 Digital Images Kyungim Baek Department of Information and Computer Sciences ICS 101 (November 1, 2016) Digital Images 1

2 iclicker Question I know a lot about how digital images are represented, stored, and manipulated in computing devices. A. Strongly agree B. Agree C. Don t agree or disagree D. Disagree E. Strongly disagree ICS 101 (November 1, 2016) Digital Images 2

3 Outline What is a digital image? Early digital images Digital image representation (Spatial) Resolution Grayscale image representation Color image representation Computer Vision vs. Computer Graphics CV Applications ICS 101 (November 1, 2016) Digital Images 3

4 What is a digital image? A digital image is a representation of a twodimensional images as a finite set of digital values Figure from B. Mac Namee ICS 101 (November 1, 2016) Digital Images 4

5 Early Digital Images One of the first applications of digital images was in the newspaper industry in early 1920s The Bartlane cable picture transmission system Pictures were sent by submarine cable between London and New York More than a week less than three hours Pictures were coded for cable transfer and then reproduced at the receiving end on a telegraph printer A digital picture produced in 1921 (McFarlane, 1972) ICS 101 (November 1, 2016) Digital Images 5

6 Early Digital Images Improvements to the Bartlane system resulted in higher quality images A digital picture produced in 1922 showing improvement both in tonal quality and in resolution A digital picture transmitted in 1929 from London to New York with 15 tones Images from McFarlane, 1972 ICS 101 (November 1, 2016) Digital Images 6

7 Digital Images The Problem: How to represent a two-dimensional image in digital form ICS 101 (November 1, 2016) Digital Images 7

8 How is this figure stored? Lines and Arcs Dots ICS 101 (November 1, 2016) Digital Images 8

9 How is this figure stored? Lines and Arcs Dots ICS 101 (November 1, 2016) Digital Images 9

10 A Digital View ICS 101 (November 1, 2016) Digital Images 10

11 Image vs. Data ICS 101 (November 1, 2016) Digital Images 11

12 Digital Image Representation Pixel: Each cell or dot (picture element) A digital image is a matrix of numbers called pixel values an approximation of a real scene (digitization) 5 1 pixel Figures from B. Mac Namee ICS 101 (November 1, 2016) Digital Images 12

13 Spatial Resolution The number of pixels in width and height is the image s spatial resolution E.g = 2,073,600 pixels ( 2.1 Megapixels) Can you claim that an image with a particular spatial resolution has a particular size in the real world? ICS 101 (November 1, 2016) Digital Images 13

14 Pixel Density Pixel density: Number of pixels per inch (ppi) Connect size (in inches) to spatial resolution Sometimes called dpi (dots per inch) Given an image with a particular size, high pixel density implies high spatial resolution Example: Consider a 12 (width) by 9 (height) picture image: 0.8 Megapixels (spatial resolution) 1024 pixels/12 = 85.3 ppi (width) 768pixels/9 = 85.3 ppi (height) image: 2.1 Megapixels (spatial resolution) 1920 pixels/12 = 160 ppi (width) 1080 pixels/9 = 120 ppi (height) ICS 101 (November 1, 2016) Digital Images 14

15 Digital Image Quality The impact of spatial resolution (or pixel density) on the image quality Smaller pixel size (given an image with a particular size), i.e. higher spatial resolution and higher pixel density Sharper lines & more detail High resolution more data to store ICS 101 (November 1, 2016) Digital Images 15

16 Comparing Pixel Density ICS 101 (November 1, 2016) Digital Images 16

17 Important Things to Learn The resulting image will never be a complete or exact reproduction of the original The quality of the image depends upon the dot density As the pixel density increases we must store more data ICS 101 (November 1, 2016) Digital Images 17

18 iclicker Question Consider an analog image with 5 (w) by 4 (h) in actual size and a digitized version of the image with 250 (w) 200 (h) pixels in spatial resolution. What happens when the resolution changes to 500 (w) 400 (h) pixels? A. The image is stretched horizontally B. The image is stretched vertically C. The image becomes blurry D. The image becomes sharper E. No change in the appearance and the quality of the image ICS 101 (November 1, 2016) Digital Images 18

19 Workspace 1 1. Describe how a digital image is represented. What does spatial resolution of an image mean? 2. Describe the relationship between the pixel density and the quality of digital images. ICS 101 (November 1, 2016) Digital Images 19

20 Building a Digital Image The world is not black & white but many shades of gray! How many? 20 ICS 101 (November 1, 2016) Digital Images

21 How do we digitize whiteness? What kind of measurement shall we use? How accurate is it? Should we use a scale of 0 to 100? How many different shades is reasonable? ICS 101 (November 1, 2016) Digital Images 21

22 How much white is there? light meter ICS 101 (November 1, 2016) Digital Images 22

23 How do we store the result? We think in base 10 We would accept a concept of 0% to 100% white (black to white) But computers don t store numbers in base 10 Computers use only binary (on-off) ICS 101 (November 1, 2016) Digital Images 23

24 How computers store numbers The most elementary on-off unit is a bit (stands for binary digit) We use 1 for on, 0 for off Computers organize single bits into groups of 8 bits called a byte. ICS 101 (November 1, 2016) Digital Images 24

25 Bits & Bytes bit byte ICS 101 (November 1, 2016) Digital Images 25

26 The Capacity of a Byte How many different values can be represented in 1 byte? 2 8 = 256 values ICS 101 (November 1, 2016) Digital Images 26

27 The Capacity of a Byte 2 8 = 256 Range: 0 to = = 154 ICS 101 (November 1, 2016) Digital Images 27

28 How much white is there? light meter ICS 101 (November 1, 2016) Digital Images 28

29 Bit Depth The bit depth (or radiometric resolution) of an image is the number of bits used to represent the pixel value Determine the number of colors/grayscales each pixel can possibl take on Small bit depth can cause quantization artifacts 4 bits per pixel = 2 4 = 16 unique values 8 bits per pixel = 2 8 = 256 unique values Images from C. Perry ICS 101 (November 1, 2016) Digital Images 29

30 Bit Depth bits range Binary image Typical grayscale image High quality grayscale image Very high quality grayscale image Floating point format /color channel 24 (= ) bit True Color ICS 101 (November 1, 2016) Digital Images 30

31 Wasteful Storage? Need 1 byte but only 1 bit of it! ICS 101 (November 1, 2016) Digital Images 31

32 The File Contents Not very compact ICS 101 (November 1, 2016) Digital Images 32

33 File Compression Run-length encoded e.g ICS 101 (November 1, 2016) Digital Images 33

34 iclicker Question Which one represents the run-length encoding of the following bit string? A B C D E ICS 101 (November 1, 2016) Digital Images 34

35 Digitizing Color ICS 101 (November 1, 2016) Digital Images 35

36 Trichromatic Theory Trichromatic ( tri = 3; chroma = color) The colors of the visible spectrum can be made by combining varying amounts of the three primary colors Primary colors of light: Red, Green, and Blue Monitors use the additive color System Printing devices use the subtractive color system ICS 101 (November 1, 2016) Digital Images 36

37 RGB Color Space Primary colors of light are monochromatic energies with 645.2nm (Red), 526.3nm (Green), 444.4nm (Blue) Used mainly in color monitor and video camera Grayscale is represented by the diagonal joining black to white Additive color system: Colors are created by adding components to black (0, 0, 0) Gimp2/Grokking-the-GIMP-v1.0/ node50.html RGB 24-bits color cube. Courtesy of R. C. Gonzalez & R. E. Woods ICS 101 (November 1, 2016) Digital Images 37

38 CMY Color Space Each color is represented by the three colors of light: Cyan (C), Magenta (M), and Yellow (Y) (C, M, Y secondary colors of light or the primary colors of pigments) Subtractive color system: It models printing on white paper and subtracts from white rather than adds to black as the RGB system does Mainly used in color printing devices that deposit color pigments It is related to the RGB color space by: ICS 101 (November 1, 2016) Digital Images 38 C M Y = R G B

39 iclicker Question Which one of the following represents pure Blue color in CMY color space? (Assume that each color component is represented by 1 byte.) A. (255, 255, 0) B. (255, 0, 255) C. (0, 255, 255) D. (0, 0, 255) E. (255, 0, 0) ICS 101 (November 1, 2016) Digital Images 39

40 Workspace 2 1. How many different values can be represented with 5 bits? 2. Describe the additive and subtractive color models. How do they work? What colors do they use? Primary colors of light Primary colors of pigments What output devices are they used for? ICS 101 (November 1, 2016) Digital Images 40

41 Digitizing Color How red is a rose? How many shades of red? ICS 101 (November 1, 2016) Digital Images 41

42 How much Red is there? red meter ICS 101 (November 1, 2016) Digital Images 42

43 How much Red is there? red meter ICS 101 (November 1, 2016) Digital Images 43

44 How much Red is there? red meter ICS 101 (November 1, 2016) Digital Images 44

45 Mixing the Three Components red meter blue meter green meter ICS 101 (November 1, 2016) Digital Images 45

46 Back to the Byte! Each byte has 256 possible combinations 3 bytes used per color, so = 16 million colors!!! This is called true color or 24 bit ICS 101 (November 1, 2016) Digital Images 46

47 8-Bit Color 8-bit = indexed color 256 color palette Each pixel has a 1 byte address to the palette ICS 101 (November 1, 2016) Digital Images 47

48 Workspace 3 1. Describe how a color image is digitized. 2. What is true color? How many bits (or bytes) are used to represent colors in true color? ICS 101 (November 1, 2016) Digital Images 48

49 iclicker Question I gained better understanding of representation and quality of digital images. A. Strongly agree B. Agree C. Don t agree or disagree D. Disagree E. Strongly disagree ICS 101 (November 1, 2016) Digital Images 49

50 Digital Image Processing The continuum from image processing to computer vision can be broken up into low-, mid- and highlevel processes Low Level Process Input: Image Output: Image Examples: Noise removal, image sharpening Mid Level Process Input: Image Output: Attributes Examples: Object recognition, segmentation High Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation Source: B. Mac Namee ICS 101 (November 1, 2016) Digital Images 50

51 What is (Computer) Vision? When we see something, what does it involve? Take a picture with a camera, it is just a bunch of colored dots (pixels) Want to make computers understand images Inferring the properties of the world from one or more images Looks easy, but not really Image (or video) Sensing device Interpreting device Interpretations Corn / mature corn in a cornfield Plant/blue sky in the background Etc. Slide adapted from F-F, Li ICS 101 (November 1, 2016) Digital Images 51

52 TED Talk by Fei-Fei Li: How we re teaching computers to understand pictures ICS 101 (November 1, 2016) Digital Images 52

53 CG versus CV Computer vision is the inverse of computer graphics (sometimes called inverse graphics ) 3D models of objects, locations Lighting information Camera parameters Computer Graphics Computer Vision Images The forward process is unique, the inverse process is not! ICS 101 (November 1, 2016) Digital Images 53

54 CV Applications: Security Biometrics: verify identity from images of fingerprints, retina, faces, etc. Visual surveillance and activity recognition ICS 101 (November 1, 2016) Digital Images 54

55 CV Applications: Face Detection Most digital cameras detect faces (and more) Source: D. Hoiem ICS 101 (November 1, 2016) Digital Images 55

56 CV Applications: Object Recognition Nokia Point & Find (Object recognition in mobile phones) Google goggles ICS 101 (November 1, 2016) Digital Images 56

57 CV Applications: Sports Improve viewer experience and/or broadcasting FoxTrax ICS 101 (November 1, 2016) Digital Images 57

58 CV Applications: Augmented Reality Google glasses (Augmented Reality) ICS 101 (November 1, 2016) Digital Images 58

59 CV Applications: Smart Cars Mobileye: vision systems currently in many cars Pedestrian collision warning, Lane departure, Forward collision warning, Headway monitoring, Intelligent high-beam, Speed limit indication ICS 101 (November 1, 2016) Digital Images 59

60 CV Applications: Autonomous Navigation Autonomous vehicle navigation Estimate motion and position of vehicle Detect and model obstacles Find safe path through environment Mars Rover Curiosity ( Google s self-driving car ICS 101 (November 1, 2016) Digital Images 60

61 CV Applications: Medical Operation Surgical robot da Vinci surgical robot (Intuitive Surgical) Image from Tele-operations Control remote by gesture input TV control by hand motion Photo credit: Ben-Gurion University of the Negev, Israel ICS 101 (November 1, 2016) Digital Images 61

62 CV Applications: Interactive Games Xbox Kinect ICS 101 (November 1, 2016) Digital Images 62

63 CV Applications: Automated Image Retrieval CBIR ICS 101 (November 1, 2016) Digital Images 63

64 More CV Applications Medicine Automated blood vessel counting Computer aided diagnosis Optical character recognition Processing text from scanned image, mobile note taker, etc. Detect and identify characters Industrial quality control Visual inspection of assembled products Etc ICS 101 (November 1, 2016) Digital Images 64