Introduction

Transcription

1 Introduction Lecturer: Dr. Hossam Hassan hossameldin.hassan@eng.asu.edu.eg Computers and Systems Engineering

2 Essential Books 1. Digital Image Processing Rafael Gonzalez and Richard Woods, Third Edition, Prenhall, Digital Image Processing using MATLAB Rafael Gonzalez, Richard Woods and Steven Eddins, Prenhall, Image processing, analysis and machine vision Milan Sonka, Vaclav Hlavac and Roger Boyle, Third edition, Thomson Learning, London, 2008

3 Course Contents Introduction Digital Image Fundamentals Image Enhancement in the Spatial Domain Image Enhancement in the Frequency Domain Edge Detection Image Segmentation Representation and Description Introduction to Object Recognition 3

4 Grading System Final examination 70% Midterm examination + Assignment/Quiz/Report 30% Warnings: A quiz may be given without being informed before. Copying assignment is prohibited. Delay of submission influences on marks 4

5 Overview Early days of computing, data was numerical. Later, textual data became more common. Today, many other forms of data: voice, music, speech, images, computer graphics, etc. Each of these types of data are signals. Loosely defined, a signal is a function that conveys information.

6 Relationship of Signal Processing to other fields As long as people have tried to send or receive through electronic media : telegraphs, telephones, television, radar, etc. there has been the realization that these signals may be affected by the system used to acquire, transmit, or process them. Sometimes, these systems are imperfect and introduce noise, distortion, or other artifacts.

7 Understanding the effects these systems have and finding ways to correct them is the fundamental of signal processing. Sometimes, these signals are specific messages that we create and send to someone else (e.g., telegraph, telephone, television, digital networking, etc.). That is, we specifically introduce the information content into the signal and hope to extract it out later.

8 Sender Acquiring Natural Image Enhance Picture Compress for Transmission Encode and Transmit over Digital network Recipient Transmitted Codes of Image Decode Decompress Display

9 Concerned fields: Digital Communication Compression Speech Synthesis and Recognition Computer Graphics Image Processing Computer Vision

10 What is Image Processing? Image processing is a subclass of signal processing concerned specifically with pictures. Improve image quality for human perception and/or computer interpretation.

11 Several fields deal with images Computer Graphics : the creation of images. Image Processing : the enhancement or other manipulation of the image the result of which is usually another images. Computer Vision: the analysis of image content.

12 Several fields deal with images

13 2 Principal application areas 1. Improvement of pictorial information for human interpretation. 2. Processing of image data for storage, transmission, and representation for autonomous machine perception. Pictorial: of or expressed in pictures; illustrated

14 Ex. of fields that use DIP Categorized by image sources Radiation from the Electromagnetic spectrum Acoustic Ultrasonic Electronic (in the form of electron beams used in electron microscopy) Computer (synthetic images used for modeling and visualization)

15 Gamma-Ray Imaging Nuclear Image (a) Bone scan (b) PET (Positron emission tomography) image Astronomical Observations. (c) Cygnus Loop Nuclear Reaction (d) Gamma radiation from a reactor valve

16 X-ray Imaging Medical diagnostics (a) chest X-ray (familiar) (b) aortic angiogram (c) head CT Industrial imaging (d) Circuit board Astronomy (e) Cygnus Loop

17 Imaging in Visible and Infrared Bands Astronomy Light microscopy Pharmaceuticals (a) taxol (anticancer agent) (b) Cholesterol Micro-inspection to materials characterization (c) Microprocessor (d) Nickel oxide thin film (e) Surface of audio CD (f) Organic superconductor

18 Remote sensing

19 Remote Sensing: Weather Observations

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23 Imaging in Radio Band

24 Ultrasound Imaging

25 Generated images by computer

26 3 types of computerized process Image Analysis Examples: reading bar coded tags or as sophisticated as identifying a person from his/her face.

27 Fundamental steps

28 Image Acquisition:

29 Camera

30 Frame Grabber

31 Image Enhancement

32 Image Restoration

33 Color Image Processing

34 Wavelets

35 Compression

36 Morphological processing

37 Image Segmentation

38 Representation & Description

39 Representation & Description

40 Recognition & Interpretation

41 Knowledge base

42 Human and Computer Vision

43 Simple questions 43

44 What is Vision? Recognize objects people we know things we own Locate objects in space to pick them up Track objects in motion catching a baseball avoiding collisions with cars on the road Recognize actions walking, running, pushing

45 Vision is Deceivingly easy Deceptive Computationally demanding Critical to many applications

46 Vision is Deceivingly Easy We see effortlessly seeing seems simpler than thinking we can all see but only select gifted people can solve hard problems like chess we use nearly 70% of our brains for visual perception! All creatures see frogs see birds see snakes see but they do not see alike

47 Vision is Deceptive Vision is an exceptionally strong sensation vision is immediate we perceive the visual world as external to ourselves, but it is a reconstruction within our brains we regard how we see as reflecting the world as it is; but human vision is subject to illusions quantitatively imprecise limited to a narrow range of frequencies of radiation passive

48 Some Illusion

49 Some Illusion

50 Some Illusion

51 Some Illusion

52 Human Vision is Passive It relies on external energy sources (sunlight, light bulbs, fires) providing light that reflects off of objects to our eyes Vision systems can be active - carry their own energy sources Radars Bat acoustic imaging systems

53 Spectral Limitation of Human Vision We see only a small part of the energy spectrum of sunlight we don t see ultraviolet or lower frequencies of light we don t see infrared or higher frequencies of light we see less than.1% of the energy that reaches our eyes But objects in the world reflect and emit energy in these and other parts of the spectrum

54 Structure of the Human Eye

55 Structure of the Human Eye

56 Lens & Retina

57 Receptors

58 Cones

59 Rods

60 Contrast sensitivity

61 Weber ratio

62 Simultaneous contrast Which small square is the darkest one?

63 Signals

64 Time-Varying Signals

65 Spatially-Varying Signals

66 Spatiotemporal Signals Video Signal!

67 Types of Signals

68 Analog & Digital

69 Sampling

70 Quantization

71 Digital Image Representation

72 Digital Image Representation

73 Digital Image Representation

74 Example of Digital Image

75 Light-intensity function

76 Illumination and Reflectance

77 Illumination and Reflectance

78 Gray level

79 Color Perception Color is an important part of our visual experience. We distinguish only 100 levels of grays but hundreds of thousands of colors. Color detection is important to computer vision; Object recognition is easier Underutilized because more processing is required, hard to publish

80 Color Perception of Reflection

81 Color Models Color Models are useful for driving hardware that generates or captures images Monitors, TVs, video cameras Color printers Since color sensation can be reproduced by combination of pure colors, it is simpler to use phosphors and CCD (charge-couple device) elements that have sharp and narrow spectra rather than combine overlapping spectra. Color models describe in what proportion to combine these spectra to produce different color impressions.

82 Additive Color Models In monitors, 3 electron beams illuminate phosphors of 3 colors that act as additive light sources. The powers of these beams are controlled by the components of colors described by the R,G,B model

83 Color Models (RGB Cube)

84 Number of bits

85 Resolution

86 Checkerboard effect

87 False contouring

88 Nonuniform sampling

89 Example

90 Example

91 Nonuniform quantization

92 Image Formation

93 Lens-less Imaging Systems - Pinhole Optics Projects images without lens with infinite depth of field Smaller the pinhole better the focus less the light energy from any single point Good for tracking solar eclipses

94 Pinhole Camera (Cont ) Distant Objects are Smaller

95 Pinhole Camera (Cont ) Bigger Hole-More Blurred Images

96 Lenses Collect More Lights With a lens, diverging rays from a scene point are converged back to an image point

97 Lens Equation n: Lens Refractive Index h I : Image Height h o : Object Height The negative values for image height indicate that the image is an inverted image

98 Thin Lens relates the distance between the scene point being viewed and the lens to the distance between the lens and the point s image (where the rays from that point are brought into focus by the lens) Let M be a point being viewed, p is the distance of M from the lens along the optical axis. The thin lens focuses all the rays from M onto the same point, the image point m at distance q from the lens.

99 Thin Lens Equation m can be determined by intersecting two known rays MQ is parallel to the optical axis, so it must be refracted to pass through F. MO passes through the lens center, so it is not bent. Note two pairs of similar triangles MSO and Osm (yellow) OQF and Fsm (green)

100 Thin Lens Equation As p gets large, q approaches f As q approaches f, p approaches infinity

101 Field of View As f gets smaller, image becomes more wide angle (more world points project onto the finite image plane). As f gets larger, image becomes more telescopic (smaller part of the world projects onto the finite image plane)

102 According to that MODEL?

103 Vanishing Point?

104 Vanishing Point TWO Points Perspective v y v x

105 Optical Power and Accommodation Optical power of a lens - how strongly the lens bends the incoming rays Short focal length lens bends rays significantly It images a point source at infinity (large p) at distance f behind the lens. The smaller f, the more the rays must be bent to bring them into focus sooner. Optical power is 1/f, with f measured in meters. The unit is called the diopter Human vision: when viewing faraway objects the distance from the lens to the retina is 0.017m. So the optical power of the eye is 58.8 diopters

106 Accommodation How does the human eye bring nearby points into focus on the retina? by increasing the power of the lens muscles attached to the lens change its shape to change the lens power accommodation: adjusting the focal length of the lens bringing points that are nearby into focus causes faraway points to go out of focus depth-of-field: range of distances in focus

107 Accommodation Physical cameras: mechanically change the distance between the lens and the image plane