Image Processing for Mechatronics Engineering For senior undergraduate students Academic Year 2017/2018, Winter Semester
|
|
|
- Gervase Spencer
- 6 years ago
- Views:
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 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 8: Color Image Processing 04.11.2017 Dr. Mohammed Abdel-Megeed Salem Media
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 2: Elementary Image Operations 16.09.2017 Dr. Mohammed Abdel-Megeed Salem
General Imaging System
General Imaging System Lecture Slides ME 4060 Machine Vision and Vision-based Control Chapter 5 Image Sensing and Acquisition By Dr. Debao Zhou 1 2 Light, Color, and Electromagnetic Spectrum Penetrate
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 5: Fundamentals of Signal and Image Processing 23.09.2017 Dr. Mohammed Abdel-Megeed
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 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 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
Photons and solid state detection
Photons and solid state detection Photons represent discrete packets ( quanta ) of optical energy Energy is hc/! (h: Planck s constant, c: speed of light,! : wavelength) For solid state detection, photons
Sampling and Reconstruction
Sampling and reconstruction COMP 575/COMP 770 Fall 2010 Stephen J. Guy 1 Review What is Computer Graphics? Computer graphics: The study of creating, manipulating, and using visual images in the computer.
Digital Imaging Rochester Institute of Technology
Digital Imaging 1999 Rochester Institute of Technology So Far... camera AgX film processing image AgX photographic film captures image formed by the optical elements (lens). Unfortunately, the processing
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
Cameras. CSE 455, Winter 2010 January 25, 2010
Cameras CSE 455, Winter 2010 January 25, 2010 Announcements New Lecturer! Neel Joshi, Ph.D. Post-Doctoral Researcher Microsoft Research neel@cs Project 1b (seam carving) was due on Friday the 22 nd Project
Aliasing and Antialiasing. What is Aliasing? What is Aliasing? What is Aliasing?
What is Aliasing? Errors and Artifacts arising during rendering, due to the conversion from a continuously defined illumination field to a discrete raster grid of pixels 1 2 What is Aliasing? What is Aliasing?
LENSES. INEL 6088 Computer Vision
LENSES INEL 6088 Computer Vision Digital camera A digital camera replaces film with a sensor array Each cell in the array is a Charge Coupled Device light-sensitive diode that converts photons to electrons
Image Formation and Capture. Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen
Image Formation and Capture Acknowledgment: some figures by B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, and A. Theuwissen Image Formation and Capture Real world Optics Sensor Devices Sources of Error
Introduction to Computer Vision
Introduction to Computer Vision CS / ECE 181B Thursday, April 1, 2004 Course Details HW #0 and HW #1 are available. Course web site http://www.ece.ucsb.edu/~manj/cs181b Syllabus, schedule, lecture notes,
CS 548: Computer Vision REVIEW: Digital Image Basics. Spring 2016 Dr. Michael J. Reale
CS 548: Computer Vision REVIEW: Digital Image Basics Spring 2016 Dr. Michael J. Reale Human Vision System: Cones and Rods Two types of receptors in eye: Cones Brightness and color Photopic vision = bright-light
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
What is Sound? Simple Harmonic Motion -- a Pendulum
What is Sound? As the tines move back and forth they exert pressure on the air around them. (a) The first displacement of the tine compresses the air molecules causing high pressure. (b) Equal displacement
Cvision 2. António J. R. Neves João Paulo Silva Cunha. Bernardo Cunha. IEETA / Universidade de Aveiro
Cvision 2 Digital Imaging António J. R. Neves (an@ua.pt) & João Paulo Silva Cunha & Bernardo Cunha IEETA / Universidade de Aveiro Outline Image sensors Camera calibration Sampling and quantization Data
CPSC 4040/6040 Computer Graphics Images. Joshua Levine
CPSC 4040/6040 Computer Graphics Images Joshua Levine levinej@clemson.edu Lecture 04 Displays and Optics Sept. 1, 2015 Slide Credits: Kenny A. Hunt Don House Torsten Möller Hanspeter Pfister Agenda Open
Light. Path of Light. Looking at things. Depth and Distance. Getting light to imager. CS559 Lecture 2 Lights, Cameras, Eyes
CS559 Lecture 2 Lights, Cameras, Eyes These are course notes (not used as slides) Written by Mike Gleicher, Sept. 2005 Adjusted after class stuff we didn t get to removed / mistakes fixed Light Electromagnetic
8.2 IMAGE PROCESSING VERSUS IMAGE ANALYSIS Image processing: The collection of routines and
8.1 INTRODUCTION In this chapter, we will study and discuss some fundamental techniques for image processing and image analysis, with a few examples of routines developed for certain purposes. 8.2 IMAGE
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
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
ELEC Dr Reji Mathew Electrical Engineering UNSW
ELEC 4622 Dr Reji Mathew Electrical Engineering UNSW Filter Design Circularly symmetric 2-D low-pass filter Pass-band radial frequency: ω p Stop-band radial frequency: ω s 1 δ p Pass-band tolerances: δ
Digital Image Processing. Lecture # 6 Corner Detection & Color Processing
Digital Image Processing Lecture # 6 Corner Detection & Color Processing 1 Corners Corners (interest points) Unlike edges, corners (patches of pixels surrounding the corner) do not necessarily correspond
ECC419 IMAGE PROCESSING
ECC419 IMAGE PROCESSING INTRODUCTION Image Processing Image processing is a subclass of signal processing concerned specifically with pictures. Digital Image Processing, process digital images by means
Islamic University of Gaza. Faculty of Engineering Electrical Engineering Department Spring-2011
Islamic University of Gaza Faculty of Engineering Electrical Engineering Department Spring-2011 DSP Laboratory (EELE 4110) Lab#4 Sampling and Quantization OBJECTIVES: When you have completed this assignment,
Overview. Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image
Camera & Color Overview Pinhole camera model Projective geometry Vanishing points and lines Projection matrix Cameras with Lenses Color Digital image Book: Hartley 6.1, Szeliski 2.1.5, 2.2, 2.3 The trip
Getting light to imager. Capturing Images. Depth and Distance. Ideal Imaging. CS559 Lecture 2 Lights, Cameras, Eyes
CS559 Lecture 2 Lights, Cameras, Eyes Last time: what is an image idea of image-based (raster representation) Today: image capture/acquisition, focus cameras and eyes displays and intensities Corrected
Acquisition. Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros
Acquisition Some slides from: Yung-Yu Chuang (DigiVfx) Jan Neumann, Pat Hanrahan, Alexei Efros Image Acquisition Digital Camera Film Outline Pinhole camera Lens Lens aberrations Exposure Sensors Noise
Cameras CS / ECE 181B
Cameras CS / ECE 181B Image Formation Geometry of image formation (Camera models and calibration) Where? Radiometry of image formation How bright? What color? Examples of cameras What is a Camera? A camera
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)
CS101 Lecture 19: Digital Images. John Magee 18 July 2013 Some material copyright Jones and Bartlett. Overview/Questions
CS101 Lecture 19: Digital Images John Magee 18 July 2013 Some material copyright Jones and Bartlett 1 Overview/Questions What is digital information? What is color? How do pictures get encoded into binary
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,
UNIT III -- DATA AND PULSE COMMUNICATION PART-A 1. State the sampling theorem for band-limited signals of finite energy. If a finite energy signal g(t) contains no frequency higher than W Hz, it is completely
CSCD 433 Network Programming Fall Lecture 5 Physical Layer Continued
CSCD 433 Network Programming Fall 2016 Lecture 5 Physical Layer Continued 1 Topics Definitions Analog Transmission of Digital Data Digital Transmission of Analog Data Multiplexing 2 Different Types of
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
EE390 Final Exam Fall Term 2002 Friday, December 13, 2002
Name Page 1 of 11 EE390 Final Exam Fall Term 2002 Friday, December 13, 2002 Notes 1. This is a 2 hour exam, starting at 9:00 am and ending at 11:00 am. The exam is worth a total of 50 marks, broken down
YEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS
YEDITEPE UNIVERSITY ENGINEERING FACULTY COMMUNICATION SYSTEMS LABORATORY EE 354 COMMUNICATION SYSTEMS EXPERIMENT 3: SAMPLING & TIME DIVISION MULTIPLEX (TDM) Objective: Experimental verification of the
Image Formation and Capture
Figure credits: B. Curless, E. Hecht, W.J. Smith, B.K.P. Horn, A. Theuwissen, and J. Malik Image Formation and Capture COS 429: Computer Vision Image Formation and Capture Real world Optics Sensor Devices
Graphics packages can be bit-mapped or vector. Both types of packages store graphics in a different way.
Graphics packages can be bit-mapped or vector. Both types of packages store graphics in a different way. Bit mapped packages (paint packages) work by changing the colour of the pixels that make up the
Integral 3-D Television Using a 2000-Scanning Line Video System
Integral 3-D Television Using a 2000-Scanning Line Video System We have developed an integral three-dimensional (3-D) television that uses a 2000-scanning line video system. An integral 3-D television
ECE 556 BASICS OF DIGITAL SPEECH PROCESSING. Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2
ECE 556 BASICS OF DIGITAL SPEECH PROCESSING Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2 Analog Sound to Digital Sound Characteristics of Sound Amplitude Wavelength (w) Frequency ( ) Timbre
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
Experiment 8: Sampling
Prepared By: 1 Experiment 8: Sampling Objective The objective of this Lab is to understand concepts and observe the effects of periodically sampling a continuous signal at different sampling rates, changing
Lecture 1: image display and representation
Learning Objectives: General concepts of visual perception and continuous and discrete images Review concepts of sampling, convolution, spatial resolution, contrast resolution, and dynamic range through
Digital Image Processing. Lecture # 8 Color Processing
Digital Image Processing Lecture # 8 Color Processing 1 COLOR IMAGE PROCESSING COLOR IMAGE PROCESSING Color Importance Color is an excellent descriptor Suitable for object Identification and Extraction
Charge-Coupled Device (CCD) Detectors pixel silicon chip electronics cryogenics
Charge-Coupled Device (CCD) Detectors As revolutionary in astronomy as the invention of the telescope and photography semiconductor detectors a collection of miniature photodiodes, each called a picture
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 SIGNAL PROCESSING. Chapter 1 Introduction to Discrete-Time Signals & Sampling
DIGITAL SIGNAL PROCESSING Chapter 1 Introduction to Discrete-Time Signals & Sampling by Dr. Norizam Sulaiman Faculty of Electrical & Electronics Engineering norizam@ump.edu.my OER Digital Signal Processing
The Xiris Glossary of Machine Vision Terminology
X The Xiris Glossary of Machine Vision Terminology 2 Introduction Automated welding, camera technology, and digital image processing are all complex subjects. When you combine them in a system featuring
CS 450: COMPUTER GRAPHICS REVIEW: RASTER IMAGES SPRING 2016 DR. MICHAEL J. REALE
CS 450: COMPUTER GRAPHICS REVIEW: RASTER IMAGES SPRING 2016 DR. MICHAEL J. REALE RASTER IMAGES VS. VECTOR IMAGES Raster = models data as rows and columns of equally-sized cells Most common way to handle
Fundamentals of CMOS Image Sensors
CHAPTER 2 Fundamentals of CMOS Image Sensors Mixed-Signal IC Design for Image Sensor 2-1 Outline Photoelectric Effect Photodetectors CMOS Image Sensor(CIS) Array Architecture CIS Peripherals Design Considerations
CS 376b Computer Vision
CS 376b Computer Vision 09 / 03 / 2014 Instructor: Michael Eckmann Today s Topics This is technically a lab/discussion session, but I'll treat it as a lecture today. Introduction to the course layout,
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
Image Enhancement in the Spatial Domain (Part 1)
Image Enhancement in the Spatial Domain (Part 1) Lecturer: Dr. Hossam Hassan Email : hossameldin.hassan@eng.asu.edu.eg Computers and Systems Engineering Principle Objective of Enhancement Process an image
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
Charged Coupled Device (CCD) S.Vidhya
Charged Coupled Device (CCD) S.Vidhya 02.04.2016 Sensor Physical phenomenon Sensor Measurement Output A sensor is a device that measures a physical quantity and converts it into a signal which can be read
Module 3: Video Sampling Lecture 18: Filtering operations in Camera and display devices. The Lecture Contains: Effect of Temporal Aperture:
The Lecture Contains: Effect of Temporal Aperture: Spatial Aperture: Effect of Display Aperture: file:///d /...e%20(ganesh%20rana)/my%20course_ganesh%20rana/prof.%20sumana%20gupta/final%20dvsp/lecture18/18_1.htm[12/30/2015
SECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS
RADT 3463 - COMPUTERIZED IMAGING Section I: Chapter 2 RADT 3463 Computerized Imaging 1 SECTION I - CHAPTER 2 DIGITAL IMAGING PROCESSING CONCEPTS RADT 3463 COMPUTERIZED IMAGING Section I: Chapter 2 RADT
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
DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 2002
DIGITAL IMAGE PROCESSING (COM-3371) Week 2 - January 14, 22 Topics: Human eye Visual phenomena Simple image model Image enhancement Point processes Histogram Lookup tables Contrast compression and stretching
Lecture 2 Digital Image Fundamentals. Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016
Lecture 2 Digital Image Fundamentals Lin ZHANG, PhD School of Software Engineering Tongji University Fall 2016 Contents Elements of visual perception Light and the electromagnetic spectrum Image sensing
Be aware that there is no universal notation for the various quantities.
Fourier Optics v2.4 Ray tracing is limited in its ability to describe optics because it ignores the wave properties of light. Diffraction is needed to explain image spatial resolution and contrast and
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)
CSCD 433 Network Programming Fall Lecture 5 Physical Layer Continued
CSCD 433 Network Programming Fall 2016 Lecture 5 Physical Layer Continued 1 Topics Definitions Analog Transmission of Digital Data Digital Transmission of Analog Data Multiplexing 2 Different Types of
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
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
Lecture 3 Digital image processing.
Lecture 3 Digital image processing. MI_L3 1 Analog image digital image 2D image matrix of pixels scanner reflection mode analog-to-digital converter (ADC) digital image MI_L3 2 The process of converting
Topic 9 - Sensors Within
Topic 9 - Sensors Within Learning Outcomes In this topic, we will take a closer look at sensor sizes in digital cameras. By the end of this video you will have a better understanding of what the various
CS 262 Lecture 01: Digital Images and Video. John Magee Some material copyright Jones and Bartlett
CS 262 Lecture 01: Digital Images and Video John Magee Some material copyright Jones and Bartlett 1 Overview/Questions What is digital information? What is color? How do pictures get encoded into binary
Lecture 26. PHY 112: Light, Color and Vision. Finalities. Final: Thursday May 19, 2:15 to 4:45 pm. Prof. Clark McGrew Physics D 134
PHY 112: Light, Color and Vision Lecture 26 Prof. Clark McGrew Physics D 134 Finalities Final: Thursday May 19, 2:15 to 4:45 pm ESS 079 (this room) Lecture 26 PHY 112 Lecture 1 Introductory Chapters Chapters
Continuous vs. Discrete signals. Sampling. Analog to Digital Conversion. CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals
Continuous vs. Discrete signals CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 22,
6.003: Signals and Systems. Sampling
6.003: Signals and Systems Sampling April 27, 200 Mid-term Examination #3 om orrow: W ednesday, A pril 2 8, 7 : 3 0-9 : 3 0 pm. No recitations tomorrow. Coverage: Lectures 20 Recitations 20 Homeworks Homework
Overview. Charge-coupled Devices. MOS capacitor. Charge-coupled devices. Charge-coupled devices:
Overview Charge-coupled Devices Charge-coupled devices: MOS capacitors Charge transfer Architectures Color Limitations 1 2 Charge-coupled devices MOS capacitor The most popular image recording technology
Introduction. Lighting
&855(17 )8785(75(1'6,10$&+,1(9,6,21 5HVHDUFK6FLHQWLVW0DWV&DUOLQ 2SWLFDO0HDVXUHPHQW6\VWHPVDQG'DWD$QDO\VLV 6,17()(OHFWURQLFV &\EHUQHWLFV %R[%OLQGHUQ2VOR125:$< (PDLO0DWV&DUOLQ#HF\VLQWHIQR http://www.sintef.no/ecy/7210/
Unit 1: Image Formation
Unit 1: Image Formation 1. Geometry 2. Optics 3. Photometry 4. Sensor Readings Szeliski 2.1-2.3 & 6.3.5 1 Physical parameters of image formation Geometric Type of projection Camera pose Optical Sensor
ENGR 210 Lab 12: Sampling and Aliasing
ENGR 21 Lab 12: Sampling and Aliasing In the previous lab you examined how A/D converters actually work. In this lab we will consider some of the consequences of how fast you sample and of the signal processing
Projection. Announcements. Müller-Lyer Illusion. Image formation. Readings Nalwa 2.1
Announcements Mailing list (you should have received messages) Project 1 additional test sequences online Projection Readings Nalwa 2.1 Müller-Lyer Illusion Image formation object film by Pravin Bhat http://www.michaelbach.de/ot/sze_muelue/index.html
5/17/2009. Digitizing Color. Place Value in a Binary Number. Place Value in a Decimal Number. Place Value in a Binary Number
Chapter 11: Light, Sound, Magic: Representing Multimedia Digitally Digitizing Color Fluency with Information Technology Third Edition by Lawrence Snyder RGB Colors: Binary Representation Giving the intensities
Digital Camera Technologies for Scientific Bio-Imaging. Part 2: Sampling and Signal
Digital Camera Technologies for Scientific Bio-Imaging. Part 2: Sampling and Signal Yashvinder Sabharwal, 1 James Joubert 2 and Deepak Sharma 2 1. Solexis Advisors LLC, Austin, TX, USA 2. Photometrics
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.
Exercise questions for Machine vision
Exercise questions for Machine vision This is a collection of exercise questions. These questions are all examination alike which means that similar questions may appear at the written exam. I ve divided
MULTIMEDIA SYSTEMS
1 Department of Computer Engineering, g, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang 01076531 MULTIMEDIA SYSTEMS Pakorn Watanachaturaporn, Ph.D. pakorn@live.kmitl.ac.th, pwatanac@gmail.com
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
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
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 Images taken from: R. Gonzalez and R. Woods. Digital Image Processing, Prentice Hall,
UNIT III Data Acquisition & Microcontroller System. Mr. Manoj Rajale
UNIT III Data Acquisition & Microcontroller System Mr. Manoj Rajale Syllabus Interfacing of Sensors / Actuators to DAQ system, Bit width, Sampling theorem, Sampling Frequency, Aliasing, Sample and hold
Unit 8: Color Image Processing
Unit 8: Color Image Processing Colour Fundamentals In 666 Sir Isaac Newton discovered that when a beam of sunlight passes through a glass prism, the emerging beam is split into a spectrum of colours The
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
Image Acquisition and Representation
Image Acquisition and Representation how digital images are produced how digital images are represented photometric models-basic radiometry image noises and noise suppression methods 1 Image Acquisition
Design of a digital holographic interferometer for the. ZaP Flow Z-Pinch
Design of a digital holographic interferometer for the M. P. Ross, U. Shumlak, R. P. Golingo, B. A. Nelson, S. D. Knecht, M. C. Hughes, R. J. Oberto University of Washington, Seattle, USA Abstract The
MULTIMEDIA SYSTEMS
1 Department of Computer Engineering, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang 01076531 MULTIMEDIA SYSTEMS Pk Pakorn Watanachaturaporn, Wt ht Ph.D. PhD pakorn@live.kmitl.ac.th,
Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals
Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego October 3, 2016 1 Continuous vs. Discrete signals
ISO INTERNATIONAL STANDARD. Photography Electronic still-picture cameras Resolution measurements
INTERNATIONAL STANDARD ISO 12233 First edition 2000-09-01 Photography Electronic still-picture cameras Resolution measurements Photographie Appareils de prises de vue électroniques Mesurages de la résolution
Image Formation and Camera Design
Image Formation and Camera Design Spring 2003 CMSC 426 Jan Neumann 2/20/03 Light is all around us! From London & Upton, Photography Conventional camera design... Ken Kay, 1969 in Light & Film, TimeLife
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
On spatial resolution
On spatial resolution Introduction How is spatial resolution defined? There are two main approaches in defining local spatial resolution. One method follows distinction criteria of pointlike objects (i.e.