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1 IE PŁ dr hab. Paweł Strumiłło
2 One picture is worth more than ten thousand words Anonymous
3 Literature: 1. Lecture notes (*.pdf files) 2. R.C. Gonzales, R. E. Woods, Digital image processing, Addison-Wesley Publishing Company, J. C. Russ, The image processing handbook, IEEE Press, W. K. Pratt, Digital image processing, John Wiley &Sons, A. Materka, Elements of image processing (in Polish), PWN, 1991.
4 Assesment method: Theory: - written examination (50%). Practice: - project report and presentation (50%)
5 Image processing objectives 1. Improvement of subjective image quality (e.g. in 1964, computerised image processing techniques were applied for correcting distortions of images transmitted from moon space probe Ranger 7 Jet Propulsion Laboratory, USA) JPL JPL
6 Objectives of image processing Improvement of image quality MIT
7 Objectives of image processing Processing of image data for machine perception, storage or transmission (e.g. first application of image processing techniques has enabled reduction of transmision time of an image across the Atlantic from one week to less than 3 hours) IOC, Antwerp 8 bpp JPEG 0.1 bpp Wavelet 0.1 bpp Playboy Magazine JPEG-2000
8 Objectives of image processing Improvement of subjective image quality for human interpretation X-ray image of wheat grain
9 Objectives of image processing Processing of image data for machine perception, storage or transmission FBI fingerprint database 1992 Biometry
10 Objectives of image processing Stereovision scene analysis Left camera image Right camera image Pseudocolored depth image Nearest objects extracted Reconstruction of depth
11 Image understanding trees sky land? water
12 Natural image processing scheme Image (90% of data) Decision
13 Computer vision system Image preprocessing Segmentation Feature extraction Image aquisition Computer + program + knowledge Image analysis and perception 9
14 Course material: 1. Image acquisition and representation 2. Image enhancement 3. Image restoration 4. Image analysis 5. Image coding 6. Stereovision
15 Image enhancement Gray-scale transformation
16 Image restoration Addison_Wesley Inc. motion blur restored image
17 Image analysis Image aquisition Feature extraction Feature description Recognition
18 Image compression (JPEG, MPEG, JPEG2000) i.e. how to push an elephant through a pin-hole Source image Transmission channel Reconstructed image
19 Image processing systems - applications science and industry (quality control, sorting,...) medicine (X-ray images, computed tomography, MRI, USG, microscopy,...) army (target tracking, quided missiles, unmanned flying vehicles) robotics (welding, painting, robots,...) Earth and space exploration (interpretation of satellite images, space probes,...) Biometrics, human computer interaction systems.
20 Image processing system developed at the Medical Electronics Division, Institute of Electronics in (1989) TV camera frame + grabber + + programs Microscope image of a nerve tissue Bacteria colony image Latex model of brain veins
21 Analysis of documents Student questionnaire
22 Electromagnetic spectrum frequency, Hz gamma rays X rays microwaves radio waves ultraviolet Visible light Infrared waves [nm]
23 Ultrasonography
24 Computed tomography
25 Magnetic resonance imaging (MRI) Przekrój głowy
26 Magnetic resonance imaging (MRI) COST B11 action "Quantitation of Magnetic Resonance Image Texture ( ) COST B21 "Physiological modelling of MR Image formation"
27 3D objects modelling Computer graphics Reconstruction 3D visualisation Surface rendering Virtual reality
28 The Visible Human Project Head slice CT MRI
29 Computed thermography Thermogram Visible light image Medicine Power eng. Civil eng.
30 Image processing applications: mammography
31 Image processing applications: biometrics Juliusz Jaksa, Krzysztof Ślot, Piotr Szczypiński Face recognition using deformable models, ICSES 2001
32 Image processing applications H. Nowak Computer lip-reading, PhD project conducted at the Medical Electronics Division
33 Image processing applications: biometrics J. Daugman
34 Image processing applications: image databases a concept of an image or a copy of an image database hit DWT C.E. Jacobs, A. Finkelstein, D.H. Salesis, Fast multiresolution image quering, 1999
35 Monochrome image as a 2D function Ω f(x,y) y x y M : 2 Ω R R+ ( x, y) f ( x, y)
36 Image brightness profile Distance along profile
37 RGB colour images R B G
38 RGB colour images Distance along profile RGB image and colour components profiles
39 Digital image discretisation + quantization pixel (picture element)
40 Digital image as pixel array (0,0) X f(x,y) Y
41 Digital image as pixel array Digital image f(x,y): f 2D array (M,N), ie. of M rows and N columns, of nonnegative elements assuming a limited number of levels ( x, y) = 0,1,..., L 1 (np. L=256) x y = 0,1,..., N = 0,1,..., M 1 1 Colour digital image?
42 Colour digital RGB image If each of the colour component is 8 bit coded then 2 24 different colours can be obtained! f ( x, y) = ( f, f, f ) R G B
43 Colour indexed image f=25 R G B Monochrome image... Colour palette (look-up table) Colour image
44 Image file formats Image file formats were devised for the two main reasons: file compatibility (exchange of data) data compression The most popular image file formats: JPEG (Joint Photographic Experts Group) NEW: JPEG2000 GIF (Graphics Interchange Format) PNG (Portable Network Graphic) TIFF (Tagged Image File Format) BMP, PCX,
45 Raster (bitmap) vs. Vector graphics Vector graphics: Images are built from simple geometrical shapes: points, lines, curves, polygons Raster graphics: Images are built from an array of elementary points (pixels), Wikipedia
46 Comparison of main image file formats Format File ext. Main features Application JPEG.jpg Transform based, CR defined lossy compression (DCT) Very good for compressing photographs JPEG 2000.jp2 New transform based, CR defined lossy compression (Discrete Wavelet Transform) Excellent for compressing photographs, to replace JPEG GIF.gif Indexed image format, max. 256 colours, lossless coding Internet, animated GIFs PNG.png Promoted by the www consortium to replace GIF Internet, no animation (foreseen MNG), alpha channel, better for text images than JPEG TIFF.tif Highly structured, complicated format, Mainly for scanning text documents BMP.bmp Simple uncompressed format For use in Windows OS
47 Hands on demo
Michał Strzelecki, Paweł Strumiłło [mstrzel,pstrumil]@p.lodz.pl
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Michał Strzelecki, Paweł Strumiłło [mstrzel,pstrumil]@p.lodz.pl One picture is worth more than ten thousand words Anonymous Literature: 1. Lecture notes (*.pdf files) www.eletel.p.lodz.pl 2. R.C. Gonzales,
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