# Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester Lecture 6: Image Acquisition and Digitization Dr. Mohammed Abdel-Megeed Salem Media Engineering Technology, German University in Cairo

2 Course Info - Contents 1. Introduction 2. Elementary Image Information and Operations 3. Fundamentals of Signal and Image Processing 1. Definition, 2. Important Signals 3. Signal & Image Processing 4. Sampling and Quantization 4. Image Acqusition and Digitization 5. Sensing and Perception (HVS) and the Color Image Processing 6. Image Operations 1. Point Image Operations 2. Local Image Operations and Filters 3. Global Image Operation and Transforms Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 2

3 Course Info - Contents 1. Introduction 2. Elementary Image Information and Operations 3. Fundamentals of Signal and Image Processing 4. Image Acqusition and Digitization Analog vs Digital Images Technical Aspects Principles of Digitization Systems for Image Digitizing Display Devices 2D Sampling Theorem Mathematical Description Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 3

4 Analog vs Digital Images Analog Data in nature are analog Analog data must be converted to digital form before it can be manipulated by computers. Digital Bits are units of data that can have one of two values. Bytes are unites of eight bits Numbers, characters, colors,... are group of bits. Digitization comprises two operations: Sampling and quantization. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 4

5 Analog vs Digital Images Analog Digital Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 5

6 Analog vs Digital Images Analog Images Original continuous image Special procedures for characterization and manipulation: Film and paper is treated in a series of chemical baths Analog results Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 6

7 Analog vs Digital Images Digital Images Original analog image Simple Procedure of characterization and manipulation Analog results sometimes are desirable (for photo album) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 7

8 Analog vs Digital Images Optics Spatial Range Signal converter Sampler Quantizer (Lenses) (Sensors) (Frame grabber) [Lehmann P. 142] Image acquisition system: LTI system Objective: Transmit a continuous signal f(x,y) in a matrix element g(m,n) H 2 : limitation in space H 3 : CCD camera (continuous in values, discrete in space) or CMOS H 4, H 5 : sampler and quantizer united in frame grabber Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 8

9 Outline 0. Course Information and Objectives 1. Introduction 2. Fundamentals of Signal and Image Processing 3. Image Acqusition and Digitization Analog vs Digital Images Technical Aspects Principles of Digitization Systems for Image Digitizing Display Devices 2D Sampling Theorem Mathematical Description Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 9

10 Principles of Digitization Vector Graphics vs Raster Images Images are displayed (rendered) as array of pixels using internal model. Images may be modelled using vector graphics or bitmap images (raster graphics). Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 10

11 Principles of Digitization Vector Graphics The images are stored as a mathematical description of a collection of individual lines, curves, and shapes. Computation is required for rendering Bitmap Array Is an array of pixels (storing color values). Can be mapped directly to the physical pixels on the display (e.g., Monitor). Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 11

12 Principles of Digitization Vector Graphics Bitmap Array Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 12

13 Principles of Digitization Vector Graphics Bitmap Array Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 13

14 Principles of Digitization Vector Graphics Bitmap Array Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 14

15 Principles of Digitization Vector Graphics Easy to transform (scaling, shifting, warping) with no distortion low memory requirements Properties of geometric elements will remain or can be changed any time Modern displays and printers are raster devices. Vector formats have to be converted to raster format. Bitmap Array Transformation is only possible by means of the whole image or after segmentation. Although all pixels of a certain object have common features but they are independent. Used directly by displays and printers. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 15

16 Principles of Digitization Vector Graphics To store: start to target coordinates, colour value, attributes (circle: centre, radius, colour, thickness, ) conversion to a raster image simple, opposite way difficult Bitmap Array To store: A set of pixels will have common visual features, such as color. Easy to be sensed. Difficult to be converted into vector graphics Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 16

17 Principles of Digitization Raster Images: Generation of raster images in two steps: scanning and digitization Scanning: Every picture element (=pixel) gets its definite coordinates. Sampling and Quantization: Assignment of a numeric value to an area segment. (e.g. Black = 0, white= 255) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 17

18 Principles of Digitization Analog Signal Sampled Signal (Discrete) Quantized Signal (Digital) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 18

19 Principles of Digitization Real Coordinate System Image Coordinate System Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 19

20 Principles of Digitization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 20

21 Principles of Digitization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 21

22 Principles of Digitization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 22

23 Principles of Digitization Gray level image of Alexander Von Humboldt Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 23

24 Principles of Digitization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 24

25 Principles of Digitization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 25

26 Principles of Digitization Remarks: Scanning addressing a location in the image Sample measuring the grey value of a pixel location Quantization converting the continuous grey value in a discrete value Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 26

27 Outline 0. Course Information and Objectives 1. Introduction 2. Fundamentals of Signal and Image Processing 3. Image Acqusition and Digitization Analog vs Digital Images Technical Aspects Principles of Digitization Systems for Image Digitizing Display Devices 2D Sampling Theorem Mathematical Description Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 27

28 Digitization System Components Aperture: fine aperture of the optics to localize the image section. (the aperture of an optical system is the opening that determines the cone angle of a bundle of rays that come to a focus in the image plane.) Light sensor: detecting the pixel brightness and converting this amount to an electrical value Quantizer: converting the sensor output in numerical values Memory: storage of the grey value for further processing Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 28

29 Digitization System Components Zoom Lens Aperture IR Filter Memory Light Sensor A/D Display Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 29

30 Digitization System Components Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 30

31 Digitization System Components Requirements for digitizing devices low noise and low distortion pixel size and space adaptable to the application given linearity or adjustable nonlinearity number of pixels per row/column and levels of gray adjustable Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 31

32 Light Sensor Single sensor, array sensor, and 2D sensor array Rolling sensor array Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 32

33 Light Sensor Charge-Coupled Device (CCD) A CCD image sensor is an analog device. When light strikes the chip it is held as a small electrical charge in each photo sensor. The charges are converted to voltage one pixel at a time as they are read from the chip. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 33

34 Light Sensor Charge-Coupled Device (CCD) matrix of photodiodes on silicon converting photons into electrical charges charge proportional to the light collection of charges in a pool (packages) transport of the charge packets of shift registers sensor elements 11x13 μm Discrete output only! Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 34

35 Light Sensor CCD: Different read-out principles Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 35

36 Light Sensor CCD: Color sensor distributed based on Bayer pattern. 50% green sensors, 25% for red and 25% for blue. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 36

37 Light Sensor CCD Advantages: Compact, robust camera: Used in professional, medical, applications free of geometric distortions Good resolution Produce high quality images quality depending on the price Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 37

38 Light Sensor CCD Disadvantages: Gaps between the individual elements ( dead pixels ) Smearing: faulty signal, which is located vertically in the image (caused if shifting is not fast enough) Blooming: in case of too much light, the electrons that are collected in the bins will overflow overdrive in over- exposed cells Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 38

39 Light Sensor Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 39

40 Light Sensor Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 40

41 Light Sensor CMOS: Complementary metal oxide semiconductor Is an active-pixel sensor (APS): consisting of an integrated circuit containing an array of pixel sensors, each pixel containing a photodetector and an active amplifier. Suited to applications in which packaging, power management, and on-chip processing are important. Widely used, from high-end digital photography down to mobile-phone cameras. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 41

42 Light Sensor CCD vs CMOS Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 42

43 Light Sensor CMOS Advantages: Single power supply Low power consumption X, Y addressing and subsampling Smallest system size Easy integration of circuitry Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 43

44 Light Sensor CMOS Disadvantages: Due to the fact that CMOS sensor captures a row at time within approximately 50 Hz or 60 Hz it may result in a "rolling shutter" effect, where the image is skewed (tilted to the left or right, depending on the direction of camera or subject movement). A frame-transfer CCD sensor does not have this problem, instead capturing the entire image at once into a frame store. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 44

45 Types of sensors used?! CCD: CMOS: 1. High quality low noise times more power consumed than a CMOS sensor. 3. More expensive than the cmos sensors. 1. More noise. 2. Low power consumption. 3. CMOS chips can be fabricated on just about any standard silicon production line, so they tend to be extremely inexpensive compared to CCD sensors. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 45

46 Outline 0. Course Content and Objectives 1. Introduction 2. Fundamentals of Signal and Image Processing 3. Image Acqusition and Digitization Analog vs Digital Images Technical Aspects Principles of Digitization Systems for Image Digitizing Display Devices 2D Sampling Theorem Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 46

47 Display Devices Permanent systems: printer Non-permanent systems: Monitor (cathode ray tubes, LCD, TFT,... ) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 47

48 Outline 1. Introduction 2. Fundamentals of Signal and Image Processing 3. Image Acqusition and Digitization Analog vs Digital Images Technical Aspects Principles of Digitization Systems for Image Digitizing Display Devices Sampling and Quantization 2D Sampling Theorem Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 48

49 Sampling and Quantaization The balance of Amount of data - information loss Sampling is the conversion of a continuous signal to a discrete signal. It is to produce samples equivalent to the instantaneous value of the continuous signal at the desired points. Sampling is performed by measuring the value of the continuous signal every a constant period of time, which is called the sampling interval. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 49

50 Sampling and Quantaization Sampling Divide the horizontal axis (time) into discrete pieces The continuous signal reduced to a sequence of equally spaced values. -> Discrete Signal Sampling rate: The number of samples in a fixed amount of time or space. Undersampling leads to aliasing Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 50

51 Sampling 768x1024 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 51

52 Sampling 192x256 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 52

53 Sampling 24x32 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 53

54 Sampling 3x4 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 54

55 Sampling 3x4 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 55

56 5 x 4 pixel Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 56

57 11 x 8 pixel Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 57

58 22 x 16 pixel Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 58

59 45 x 32 pixel Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 59

60 about 500 x 128 pixel Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 60

61 Digitization Quantization: is restricting the sample values to a set of quantization levels. Hence, in order to have every sample on one of the allowed levels: some of the values may be chopped off, some of the values may be rounded up. The quantization levels are the set of values to which a signal is quantized. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 61

62 Digitization Quantization: Divide the vertical axis (signal strength - voltage) into pieces. For example, 8- bit quantization divides the vertical axis into 256 levels. 16 bit gives you levels. Lower the quantization, lower the quality of the sound Linear vs. Non-Linear quantization: If the scale used for the vertical axis is linear we say its linear quantization; If its logarithmic then we call it non-linear ( -law or A-law in Europe). The non-linear scale is used because small amplitude signals are more likely to occur than large amplitude signals, and they are less likely to mask any noise.

63 Digitization Reconstruction The information is lost between samples. In order to reconstruct the signal, we need to fill in the gaps between the samples. One way to fill in the gaps between samples is to sample and hold: The value of a sample is used for the entire extent between it and the following sample. This produces a signal with abrupt transitions. This is not very accurate but suitable for many situations. Digitized signal Reconstructed signal Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 63

64 Quantaization Range of quantization: s in (s min, s max ) Each sample of the signal is mapped to a quantum level s min <= c i <=s max, 1<=i<=L, L>=2. Using minimum-distance criterion: Error: g(x,y) = argmin (1<=i<=L) {d(s(x,y), c i )} g = argmin (1<=i<=L) {d(s, c i )} (Note: s, g are ind. on x,y) Summation (k) (s k -g k ) 2 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 64

65 Quantization Original colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 65

66 Quantization 256 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 66

67 Quantization 64 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 67

68 Quantization 16 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 68

69 Quantization 8 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 69

70 Quantization 4 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 70

71 Quantization 2 colours Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 71

72 202 grey values Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 72

73 16 grey values Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 73

74 2 grey values Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 74

75 Principles of Digitization Analog Signal Sampled Signal (Discrete) Quantized Signal (Digital) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 75

76 Sampling Theorem Sampling Theorem is the theoretical basis for an optimum grid size. Optimisation criterion: choose grid size so that no information gets lost. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 76

77 Sampling Theorem f(t) 1D Problem:? T A?? t Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 77

78 Nyquist Theorem Consider a sine wave Sampling once a cycle Appears as a constant signal For Lossless digitization, the sampling rate should be at least twice the maximum frequency responses Sampling 1.5 times each cycle Appears as a low frequency Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 78 sine signal

79 Nyquist Theorem The Sampling Theorem states that, if the highestfrequency component of a signal is at f h, the signal can be properly reconstructed if it has been sampled at a frequency greater than 2 f h. This limiting value is known as the Nyquist rate. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 79

80 Nyquist Theorem Sampling rate vs frequency How a pure sine waves at different frequencies combine to produce more complex waveforms. Starting with a pure sine wave of frequency f, we successively add components to it with frequencies of 3f, 5f, 7f, and so on, whose amplitudes are one third, one fifth, one seventh, of the amplitude of the original signal. As you can see, as we add more harmonics, the signal begins to look more and more like a square wave; the more frequency components we add, the better the approximation. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 80

81 Nyquist Theorem Application: Nyquist theorem is used to calculate the optimum sampling rate in order to obtain good audio quality. For example, if the CD standard sampling rate of Hz means that the waveform is sampled times per sec. Digitally sampled audio has a bandwidth of (20 Hz - 20 KHz). By sampling at twice the maximum frequency (40 KHz) we could have achieved good audio quality. CD audio slightly exceeds this, resulting in an ability to represent a bandwidth of around Hz. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 81

82 Nyquist Theorem Nyquist Rate Some authors, especially in the field of audio, use the term Nyquist rate to denote the highest-frequency component that can be accurately reproduced. That is, if a signal is sampled at fs, their Nyquist rate is fs/2. The fact that the term is used with both meanings is unfortunate, but any ambiguity is usually easily resolved by context. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 82

83 Sampling Theorem The discrete spectrum of the time stationary signals has all information. If you have a time-limited signal (period T 0 ) it is enough to have spectral values in a distance of (f A < 1/T 0 ). -T 0 /2 +T 0 /2 t f A f Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 83

84 Sampling Theorem The discrete spectrum of the time stationary signals has all information If you limit the frequency content to f g then it is enough to have samples in a distance of Δt < 1/(2 f g ). T A -f g +f g f Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 84

85 Sampling Theorem The discrete spectrum of the time stationary signals has all information Sampling rate must be at least as double as the highest frequency contained in the signal 1/ Δt > (2 f g ). -T 0 /2 +T 0 /2 t T A Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 85

86 Sampling Theorem Continuous signal Continuous spectrum Periodic signal discrete spectrum Time limited signal is continued periodically discrete spectrum Discrete spectrum Fourier synthesis periodic signal cut one period Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 87

87 Sampling Theorem Continous Signal Sampled Signal (Discrete) Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 88

88 2D Sampling Theorem Image are 2D Signals Problem: given: a spatial continuous image f(x,y) wanted: a spatial discrete image Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 89

89 2D Sampling Theorem Image are 2D Signals Question: How to choose Δx and Δy if we don t want loss of information? Solution process: define a sequence of 2D Delta impulses multiply the image by this sequence result is a discrete image, existing only at the discrete coordinates Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 90

90 2D Sampling Theorem Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 91

91 2D Sampling Theorem 2D Dirac field: 2D discrete image: Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 92

92 Sampling Theorem Spectral overlap if the sampling frequency is too small a) signal to be sampled and b) corresponding spectral function c) sampled signal; Δt =2/3 s, d) corresponding spectral function, ω A = 2π/T A Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 93

93 Sampling Theorem e) sampled signal, Δt = 2 s, f) corresponding spectral function g) from h) reconstructed signal, h) one period from f) with ±ω N =±π/2 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 94

94 Sampling Theorem Undersampling happens when samples are too far apart. In this case, any reconstruction will be inadequate. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 95

95 Sampling Theorem Undersampling Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 96

96 Sampling Theorem Undersampling This phenomenon is known as aliasing, and is perceived in different ways in different media. With sound, it is heard as distortion; in images, it is usually seen in the form of jagged edges, or, where the image contains fine repeating details, Moiré patterns. Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 97

97 Effect of under sampling on high detailed image.

98

99

100

101 Rio-Andirrio-Brücke Straßenbrücke über den Golf von Korinth

102

103 Comments Sampling Determines the resolution of the image Pixel dimensions (Width x Height). Reducing the resolution called down-sampling, increasing it called up-sampling. Too low Sampling rate cause loss of information and reduces the image dimensions. Pixelization Quantization Determines the number of bits used to store a colour value - the colour depth. Determines how many colours can be represented. Too low quantization level leads to loss of image details but reduces file size. Posterization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 104

104 Comments 198 x x 1240 Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 105

105 Comments Pixelization Posterization Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 106

106 Comments Pixelization Posterization 24bit 8bit Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 107

107 Readings Rafael G. Gonzalaz and Richard E. Woods, Digital Image Processing, 3 rd Edition, Pearson Edu., [Section 2.2: Image Sensing and Acquisition] Chapman and Chapman, Digital Multimedia, 3rd Edition, [Section 3.1: Victor Graphics and Bitmap Graphics] Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 108

108 Contacts Image Processing for Mechatronics Engineering, for senior students, Winter Semester 2017 Dr. Mohammed Abdel-Megeed M. Salem Media Engineering Technology, German University in Cairo Office: C7.311 Ext Tel.: Salem, Image Processing for Mechatronics Engineering, Winter Semester 2017 Lecture 6 109

### Image and Video Processing

Image and Video Processing () Image Representation Dr. Miles Hansard miles.hansard@qmul.ac.uk Segmentation 2 Today s agenda Digital image representation Sampling Quantization Sub-sampling Pixel interpolation

### Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester

Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester Lecture 4: Fundamentals of Signal and Image Processing 30.09.2017 Dr. Mohammed Abdel-Megeed

### Image acquisition. In both cases, the digital sensing element is one of the following: Line array Area array. Single sensor

Image acquisition Digital images are acquired by direct digital acquisition (digital still/video cameras), or scanning material acquired as analog signals (slides, photographs, etc.). In both cases, the

### IMAGE SENSOR SOLUTIONS. KAC-96-1/5" Lens Kit. KODAK KAC-96-1/5" Lens Kit. for use with the KODAK CMOS Image Sensors. November 2004 Revision 2

KODAK for use with the KODAK CMOS Image Sensors November 2004 Revision 2 1.1 Introduction Choosing the right lens is a critical aspect of designing an imaging system. Typically the trade off between image

### Visual perception basics. Image aquisition system. IE PŁ P. Strumiłło

Visual perception basics Image aquisition system Light perception by humans Humans perceive approx. 90% of information about the environment by means of visual system. Efficiency of the human visual system

### Lecture 2: Digital Image Fundamentals -- Sampling & Quantization

I2200: Digital Image processing Lecture 2: Digital Image Fundamentals -- Sampling & Quantization Prof. YingLi Tian Sept. 6, 2017 Department of Electrical Engineering The City College of New York The City

### A 1.3 Megapixel CMOS Imager Designed for Digital Still Cameras

A 1.3 Megapixel CMOS Imager Designed for Digital Still Cameras Paul Gallagher, Andy Brewster VLSI Vision Ltd. San Jose, CA/USA Abstract VLSI Vision Ltd. has developed the VV6801 color sensor to address

### Evaluating Commercial Scanners for Astronomical Images. The underlying technology of the scanners: Pixel sizes:

Evaluating Commercial Scanners for Astronomical Images Robert J. Simcoe Associate Harvard College Observatory rjsimcoe@cfa.harvard.edu Introduction: Many organizations have expressed interest in using

### Image Acquisition and Representation. Camera. CCD Camera. Image Acquisition Hardware

Image Acquisition and Representation Camera Slide 1 how digital images are produced how digital images are represented Slide 3 First photograph was due to Niepce of France in 1827. Basic abstraction is

### 2013 LMIC Imaging Workshop. Sidney L. Shaw Technical Director. - Light and the Image - Detectors - Signal and Noise

2013 LMIC Imaging Workshop Sidney L. Shaw Technical Director - Light and the Image - Detectors - Signal and Noise The Anatomy of a Digital Image Representative Intensities Specimen: (molecular distribution)

### digital film technology Resolution Matters what's in a pattern white paper standing the test of time

digital film technology Resolution Matters what's in a pattern white paper standing the test of time standing the test of time An introduction >>> Film archives are of great historical importance as they

### Antialiasing and Related Issues

Antialiasing and Related Issues OUTLINE: Antialiasing Prefiltering, Supersampling, Stochastic Sampling Rastering and Reconstruction Gamma Correction Antialiasing Methods To reduce aliasing, either: 1.

### Determining MTF with a Slant Edge Target ABSTRACT AND INTRODUCTION

Determining MTF with a Slant Edge Target Douglas A. Kerr Issue 2 October 13, 2010 ABSTRACT AND INTRODUCTION The modulation transfer function (MTF) of a photographic lens tells us how effectively the lens

### Digital Cameras The Imaging Capture Path

Manchester Group Royal Photographic Society Imaging Science Group Digital Cameras The Imaging Capture Path by Dr. Tony Kaye ASIS FRPS Silver Halide Systems Exposure (film) Processing Digital Capture Imaging

### IMAGE FORMATION. Light source properties. Sensor characteristics Surface. Surface reflectance properties. Optics

IMAGE FORMATION Light source properties Sensor characteristics Surface Exposure shape Optics Surface reflectance properties ANALOG IMAGES An image can be understood as a 2D light intensity function f(x,y)

### Unit 1.1: Information representation

Unit 1.1: Information representation 1.1.1 Different number system A number system is a writing system for expressing numbers, that is, a mathematical notation for representing numbers of a given set,

### Sampling and Signal Processing

Sampling and Signal Processing Sampling Methods Sampling is most commonly done with two devices, the sample-and-hold (S/H) and the analog-to-digital-converter (ADC) The S/H acquires a continuous-time signal

### Chapter 2: Digital Image Fundamentals. Digital image processing is based on. Mathematical and probabilistic models Human intuition and analysis

Chapter 2: Digital Image Fundamentals Digital image processing is based on Mathematical and probabilistic models Human intuition and analysis 2.1 Visual Perception How images are formed in the eye? Eye

### Sampling and Reconstruction of Analog Signals

Sampling and Reconstruction of Analog Signals Chapter Intended Learning Outcomes: (i) Ability to convert an analog signal to a discrete-time sequence via sampling (ii) Ability to construct an analog signal

### Digital Image Fundamentals. Digital Image Processing. Human Visual System. Contents. Structure Of The Human Eye (cont.) Structure Of The Human Eye

Digital Image Processing 2 Digital Image Fundamentals Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Those who wish to succeed must ask the right preliminary questions Aristotle Images

### F-number sequence. a change of f-number to the next in the sequence corresponds to a factor of 2 change in light intensity,

1 F-number sequence a change of f-number to the next in the sequence corresponds to a factor of 2 change in light intensity, 0.7, 1, 1.4, 2, 2.8, 4, 5.6, 8, 11, 16, 22, 32, Example: What is the difference

### Chapter 7. Introduction. Analog Signal and Discrete Time Series. Sampling, Digital Devices, and Data Acquisition

Chapter 7 Sampling, Digital Devices, and Data Acquisition Material from Theory and Design for Mechanical Measurements; Figliola, Third Edition Introduction Integrating analog electrical transducers with

### Digital camera. Sensor. Memory card. Circuit board

Digital camera Circuit board Memory card Sensor Detector element (pixel). Typical size: 2-5 m square Typical number: 5-20M Pixel = Photogate Photon + Thin film electrode (semi-transparent) Depletion volume

### Resolution test with line patterns

Resolution test with line patterns OBJECT IMAGE 1 line pair Resolution limit is usually given in line pairs per mm in sensor plane. Visual evaluation usually. Test of optics alone Magnifying glass Test

### Digital Image Processing

Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing

### Images and Graphics. 4. Images and Graphics - Copyright Denis Hamelin - Ryerson University

Images and Graphics Images and Graphics Graphics and images are non-textual information that can be displayed and printed. Graphics (vector graphics) are an assemblage of lines, curves or circles with

### The ultimate camera. Computational Photography. Creating the ultimate camera. The ultimate camera. What does it do?

Computational Photography The ultimate camera What does it do? Image from Durand & Freeman s MIT Course on Computational Photography Today s reading Szeliski Chapter 9 The ultimate camera Infinite resolution

### The Fundamentals of Mixed Signal Testing

The Fundamentals of Mixed Signal Testing Course Information The Fundamentals of Mixed Signal Testing course is designed to provide the foundation of knowledge that is required for testing modern mixed

### Background. Computer Vision & Digital Image Processing. Improved Bartlane transmitted image. Example Bartlane transmitted image

Background Computer Vision & Digital Image Processing Introduction to Digital Image Processing Interest comes from two primary backgrounds Improvement of pictorial information for human perception How

### OFFSET AND NOISE COMPENSATION

OFFSET AND NOISE COMPENSATION AO 10V 8.1 Offset and fixed pattern noise reduction Offset variation - shading AO 10V 8.2 Row Noise AO 10V 8.3 Offset compensation Global offset calibration Dark level is

### Problems from the 3 rd edition

(2.1-1) Find the energies of the signals: a) sin t, 0 t π b) sin t, 0 t π c) 2 sin t, 0 t π d) sin (t-2π), 2π t 4π Problems from the 3 rd edition Comment on the effect on energy of sign change, time shifting

### ME 6406 MACHINE VISION. Georgia Institute of Technology

ME 6406 MACHINE VISION Georgia Institute of Technology Class Information Instructor Professor Kok-Meng Lee MARC 474 Office hours: Tues/Thurs 1:00-2:00 pm kokmeng.lee@me.gatech.edu (404)-894-7402 Class

### Digital Image Processing

Digital Image Processing Digital Imaging Fundamentals Christophoros Nikou cnikou@cs.uoi.gr Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall, 2008. Digital Image Processing

### Noise Characteristics of a High Dynamic Range Camera with Four-Chip Optical System

Journal of Electrical Engineering 6 (2018) 61-69 doi: 10.17265/2328-2223/2018.02.001 D DAVID PUBLISHING Noise Characteristics of a High Dynamic Range Camera with Four-Chip Optical System Takayuki YAMASHITA

### Advanced Camera and Image Sensor Technology. Steve Kinney Imaging Professional Camera Link Chairman

Advanced Camera and Image Sensor Technology Steve Kinney Imaging Professional Camera Link Chairman Content Physical model of a camera Definition of various parameters for EMVA1288 EMVA1288 and image quality

### Defense Technical Information Center Compilation Part Notice

UNCLASSIFIED Defense Technical Information Center Compilation Part Notice ADPO 11345 TITLE: Measurement of the Spatial Frequency Response [SFR] of Digital Still-Picture Cameras Using a Modified Slanted

### ECEGR Lab #8: Introduction to Simulink

Page 1 ECEGR 317 - Lab #8: Introduction to Simulink Objective: By: Joe McMichael This lab is an introduction to Simulink. The student will become familiar with the Help menu, go through a short example,

### Chapter-2 SAMPLING PROCESS

Chapter-2 SAMPLING PROCESS SAMPLING: A message signal may originate from a digital or analog source. If the message signal is analog in nature, then it has to be converted into digital form before it can

### SAMPLING AND RECONSTRUCTING SIGNALS

CHAPTER 3 SAMPLING AND RECONSTRUCTING SIGNALS Many DSP applications begin with analog signals. In order to process these analog signals, the signals must first be sampled and converted to digital signals.

### Image Formation: Camera Model

Image Formation: Camera Model Ruigang Yang COMP 684 Fall 2005, CS684-IBMR Outline Camera Models Pinhole Perspective Projection Affine Projection Camera with Lenses Digital Image Formation The Human Eye

### Vision. The eye. Image formation. Eye defects & corrective lenses. Visual acuity. Colour vision. Lecture 3.5

Lecture 3.5 Vision The eye Image formation Eye defects & corrective lenses Visual acuity Colour vision Vision http://www.wired.com/wiredscience/2009/04/schizoillusion/ Perception of light--- eye-brain

### 15110 Principles of Computing, Carnegie Mellon University

1 Last Time Data Compression Information and redundancy Huffman Codes ALOHA Fixed Width: 0001 0110 1001 0011 0001 20 bits Huffman Code: 10 0000 010 0001 10 15 bits 2 Overview Human sensory systems and

### SAMPLING THEORY. Representing continuous signals with discrete numbers

SAMPLING THEORY Representing continuous signals with discrete numbers Roger B. Dannenberg Professor of Computer Science, Art, and Music Carnegie Mellon University ICM Week 3 Copyright 2002-2013 by Roger

### Chapter 2: Fundamentals of Data and Signals

Chapter 2: Fundamentals of Data and Signals TRUE/FALSE 1. The terms data and signal mean the same thing. F PTS: 1 REF: 30 2. By convention, the minimum and maximum values of analog data and signals are

### CCD Requirements for Digital Photography

IS&T's 2 PICS Conference IS&T's 2 PICS Conference Copyright 2, IS&T CCD Requirements for Digital Photography Richard L. Baer Hewlett-Packard Laboratories Palo Alto, California Abstract The performance

### Laboratory Assignment 5 Amplitude Modulation

Laboratory Assignment 5 Amplitude Modulation PURPOSE In this assignment, you will explore the use of digital computers for the analysis, design, synthesis, and simulation of an amplitude modulation (AM)

### Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi

Communication Engineering Prof. Surendra Prasad Department of Electrical Engineering Indian Institute of Technology, Delhi Lecture - 25 FM Receivers Pre Emphasis, De Emphasis And Stereo Broadcasting We

### ABSTRACT. Keywords: 0,18 micron, CMOS, APS, Sunsensor, Microned, TNO, TU-Delft, Radiation tolerant, Low noise. 1. IMAGERS FOR SPACE APPLICATIONS.

Active pixel sensors: the sensor of choice for future space applications Johan Leijtens(), Albert Theuwissen(), Padmakumar R. Rao(), Xinyang Wang(), Ning Xie() () TNO Science and Industry, Postbus, AD

### EE482: Digital Signal Processing Applications

Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 01 Introduction 14/01/21 http://www.ee.unlv.edu/~b1morris/ee482/

### The future of the broadloom inspection

Contact image sensors realize efficient and economic on-line analysis The future of the broadloom inspection In the printing industry the demands regarding the product quality are constantly increasing.

### Sensors and Sensing Cameras and Camera Calibration

Sensors and Sensing Cameras and Camera Calibration Todor Stoyanov Mobile Robotics and Olfaction Lab Center for Applied Autonomous Sensor Systems Örebro University, Sweden todor.stoyanov@oru.se 20.11.2014

### Digital Image Processing

Digital Image Processing Part 2: Image Enhancement Digital Image Processing Course Introduction in the Spatial Domain Lecture AASS Learning Systems Lab, Teknik Room T26 achim.lilienthal@tech.oru.se Course

### Digital Photographs, Image Sensors and Matrices

Digital Photographs, Image Sensors and Matrices Digital Camera Image Sensors Electron Counts Checkerboard Analogy Bryce Bayer s Color Filter Array Mosaic. Image Sensor Data to Matrix Data Visualization

### How does prism technology help to achieve superior color image quality?

WHITE PAPER How does prism technology help to achieve superior color image quality? Achieving superior image quality requires real and full color depth for every channel, improved color contrast and color

### A Digital Signal Processor for Musicians and Audiophiles Published on Monday, 09 February :54

A Digital Signal Processor for Musicians and Audiophiles Published on Monday, 09 February 2009 09:54 The main focus of hearing aid research and development has been on the use of hearing aids to improve

### EBU - Tech 3335 : Methods of measuring the imaging performance of television cameras for the purposes of characterisation and setting

EBU - Tech 3335 : Methods of measuring the imaging performance of television cameras for the purposes of characterisation and setting Alan Roberts, March 2016 SUPPLEMENT 19: Assessment of a Sony a6300

### SOME PHYSICAL LAYER ISSUES. Lecture Notes 2A

SOME PHYSICAL LAYER ISSUES Lecture Notes 2A Delays in networks Propagation time or propagation delay, t prop Time required for a signal or waveform to propagate (or move) from one point to another point.

### Capturing light and color

Capturing light and color Friday, 10/02/2017 Antonis Argyros e-mail: argyros@csd.uoc.gr Szeliski 2.2, 2.3, 3.1 1 Recap from last lecture Pinhole camera model Perspective projection Focal length and depth/field

### Lecture 15. Lecture 15

Lecture 15 Charge coupled device (CCD) The basic CCD is composed of a linear array of MOS capacitors. It functions as an analog memory and shift register. The operation is indicated in the diagram below:

### Image Processing for feature extraction

Image Processing for feature extraction 1 Outline Rationale for image pre-processing Gray-scale transformations Geometric transformations Local preprocessing Reading: Sonka et al 5.1, 5.2, 5.3 2 Image

### Part Number SuperPix TM image sensor is one of SuperPix TM 2 Mega Digital image sensor series products. These series sensors have the same maximum ima

Specification Version Commercial 1.7 2012.03.26 SuperPix Micro Technology Co., Ltd Part Number SuperPix TM image sensor is one of SuperPix TM 2 Mega Digital image sensor series products. These series sensors

### Image Processing (EA C443)

Image Processing (EA C443) OBJECTIVES: To study components of the Image (Digital Image) To Know how the image quality can be improved How efficiently the image data can be stored and transmitted How the

### CHAPTER 4. PULSE MODULATION Part 2

CHAPTER 4 PULSE MODULATION Part 2 Pulse Modulation Analog pulse modulation: Sampling, i.e., information is transmitted only at discrete time instants. e.g. PAM, PPM and PDM Digital pulse modulation: Sampling

### Εισαγωγική στην Οπτική Απεικόνιση

Εισαγωγική στην Οπτική Απεικόνιση Δημήτριος Τζεράνης, Ph.D. Εμβιομηχανική και Βιοϊατρική Τεχνολογία Τμήμα Μηχανολόγων Μηχανικών Ε.Μ.Π. Χειμερινό Εξάμηνο 2015 Light: A type of EM Radiation EM radiation:

Technical Note CMOS, EMCCD AND CCD CAMERAS FOR LIFE SCIENCES Camera Test Protocol Introduction The detector is one of the most important components of any microscope system. Accurate detector readings

### 2. TELECOMMUNICATIONS BASICS

2. TELECOMMUNICATIONS BASICS The purpose of any telecommunications system is to transfer information from the sender to the receiver by a means of a communication channel. The information is carried by

### Optical Signal Processing

Optical Signal Processing ANTHONY VANDERLUGT North Carolina State University Raleigh, North Carolina A Wiley-Interscience Publication John Wiley & Sons, Inc. New York / Chichester / Brisbane / Toronto

### Unit thickness. Unit area. σ = NΔX = ΔI / I 0

Unit thickness I 0 ΔI I σ = ΔI I 0 NΔX = ΔI / I 0 NΔX Unit area Δx Average probability of reaction with atom for the incident photons at unit area with the thickness of Delta-X Atom number at unit area

### Modulation Transfer Function

Modulation Transfer Function The resolution and performance of an optical microscope can be characterized by a quantity known as the modulation transfer function (MTF), which is a measurement of the microscope's

### Signal Sampling. Sampling. Sampling. Sampling. Sampling. Sampling

Signal Let s sample the signal at a time interval o Dr. Christopher M. Godrey University o North Carolina at Asheville Photo: C. Godrey Let s sample the signal at a time interval o Reconstruct the curve

### A CAMERA IS A LIGHT TIGHT BOX

HOW CAMERAS WORK A CAMERA IS A LIGHT TIGHT BOX Pinhole Principle All contemporary cameras have the same basic features A light-tight box to hold the camera parts and recording material A viewing system

### Voice Transmission --Basic Concepts--

Voice Transmission --Basic Concepts-- Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Amplitude Frequency Phase Telephone Handset (has 2-parts) 2 1. Transmitter

### Assignment: Light, Cameras, and Image Formation

Assignment: Light, Cameras, and Image Formation Erik G. Learned-Miller February 11, 2014 1 Problem 1. Linearity. (10 points) Alice has a chandelier with 5 light bulbs sockets. Currently, she has 5 100-watt

### Digital Signal Processing

Digital Signal Processing Lecture 9 Discrete-Time Processing of Continuous-Time Signals Alp Ertürk alp.erturk@kocaeli.edu.tr Analog to Digital Conversion Most real life signals are analog signals These

### Enhanced Waveform Interpolative Coding at 4 kbps

Enhanced Waveform Interpolative Coding at 4 kbps Oded Gottesman, and Allen Gersho Signal Compression Lab. University of California, Santa Barbara E-mail: [oded, gersho]@scl.ece.ucsb.edu Signal Compression

### An Evaluation of MTF Determination Methods for 35mm Film Scanners

An Evaluation of Determination Methods for 35mm Film Scanners S. Triantaphillidou, R. E. Jacobson, R. Fagard-Jenkin Imaging Technology Research Group, University of Westminster Watford Road, Harrow, HA1

### DFT: Discrete Fourier Transform & Linear Signal Processing

DFT: Discrete Fourier Transform & Linear Signal Processing 2 nd Year Electronics Lab IMPERIAL COLLEGE LONDON Table of Contents Equipment... 2 Aims... 2 Objectives... 2 Recommended Textbooks... 3 Recommended

### Pixel CCD RASNIK. Kevan S Hashemi and James R Bensinger Brandeis University May 1997

ATLAS Internal Note MUON-No-180 Pixel CCD RASNIK Kevan S Hashemi and James R Bensinger Brandeis University May 1997 Introduction This note compares the performance of the established Video CCD version

### CHAPTER. delta-sigma modulators 1.0

CHAPTER 1 CHAPTER Conventional delta-sigma modulators 1.0 This Chapter presents the traditional first- and second-order DSM. The main sources for non-ideal operation are described together with some commonly

### Guide to observation planning with GREAT

Guide to observation planning with GREAT G. Sandell GREAT is a heterodyne receiver designed to observe spectral lines in the THz region with high spectral resolution and sensitivity. Heterodyne receivers

### Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday.

L105/205 Phonetics Scarborough Handout 7 10/18/05 Reading: Johnson Ch.2.3.3-2.3.6, Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday Spectral Analysis 1. There are

### Capturing Light in man and machine

Capturing Light in man and machine CS194: Image Manipulation & Computational Photography Alexei Efros, UC Berkeley, Fall 2014 Etymology PHOTOGRAPHY light drawing / writing Image Formation Digital Camera

### Q A bitmap file contains the binary on the left below. 1 is white and 0 is black. Colour in each of the squares. What is the letter that is reve

R 25 Images and Pixels - Reading Images need to be stored and processed using binary. The simplest image format is for an image to be stored as a bitmap image. Bitmap images are made up of picture elements

### Lecture 2: SIGNALS. 1 st semester By: Elham Sunbu

Lecture 2: SIGNALS 1 st semester 1439-2017 1 By: Elham Sunbu OUTLINE Signals and the classification of signals Sine wave Time and frequency domains Composite signals Signal bandwidth Digital signal Signal

### Biomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar

Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative

### Set-up. Equipment required: Your issued Laptop MATLAB ( if you don t already have it on your laptop)

All signals found in nature are analog they re smooth and continuously varying, from the sound of an orchestra to the acceleration of your car to the clouds moving through the sky. An excerpt from http://www.netguru.net/ntc/ntcc5.htm

### Dental Radiography. One of the problems of dental radiography is having different dimensions than normal.

The prototype receptor (the recording medium) most commonly used in dental radiography is the radiographic film. However, there are many other new more efficient receptors than the formed one that can

### e2v Launches New Onyx 1.3M for Premium Performance in Low Light Conditions

e2v Launches New Onyx 1.3M for Premium Performance in Low Light Conditions e2v s Onyx family of image sensors is designed for the most demanding outdoor camera and industrial machine vision applications,

### Multirate Digital Signal Processing

Multirate Digital Signal Processing Basic Sampling Rate Alteration Devices Up-sampler - Used to increase the sampling rate by an integer factor Down-sampler - Used to increase the sampling rate by an integer

### Figure 1 HDR image fusion example

TN-0903 Date: 10/06/09 Using image fusion to capture high-dynamic range (hdr) scenes High dynamic range (HDR) refers to the ability to distinguish details in scenes containing both very bright and relatively

### INTRODUCTION THIN LENSES. Introduction. given by the paraxial refraction equation derived last lecture: Thin lenses (19.1) = 1. Double-lens systems

Chapter 9 OPTICAL INSTRUMENTS Introduction Thin lenses Double-lens systems Aberrations Camera Human eye Compound microscope Summary INTRODUCTION Knowledge of geometrical optics, diffraction and interference,

### Lecture Outline. Data and Signals. Analogue Data on Analogue Signals. OSI Protocol Model

Lecture Outline Data and Signals COMP312 Richard Nelson richardn@cs.waikato.ac.nz http://www.cs.waikato.ac.nz Analogue Data on Analogue Signals Digital Data on Analogue Signals Analogue Data on Digital

### CHARGE-COUPLED DEVICE (CCD)

CHARGE-COUPLED DEVICE (CCD) Definition A charge-coupled device (CCD) is an analog shift register, enabling analog signals, usually light, manipulation - for example, conversion into a digital value that

### Cameras. Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/2/26. with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros

Cameras Digital Visual Effects, Spring 2008 Yung-Yu Chuang 2008/2/26 with slides by Fredo Durand, Brian Curless, Steve Seitz and Alexei Efros Camera trial #1 scene film Put a piece of film in front of

### Copyright 2000 Society of Photo Instrumentation Engineers.

Copyright 2000 Society of Photo Instrumentation Engineers. This paper was published in SPIE Proceedings, Volume 4043 and is made available as an electronic reprint with permission of SPIE. One print or