Digital Image Processing
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1 Part 1: Course Introduction Achim J. Lilienthal AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, o'clock) Course Book Chapters 1 &
2 Contents 1. Introduction digital images human visual perception, optical illusions, e-m spectrum example application person tracking with mobile robots example image understanding tiny images approach 2. Course Contents 3. Digital Image Acquisition image formation model image sampling and quantization, zooming and shrinking
3 Contents Introduction Digital Images
4 Introduction Digital Images Digital Images a finite set of digital values (picture elements = pixels) each pixel is associated to a position in a 2D region each pixel has a value digital image of a rat magnification of the rat s nose
5 Introduction Digital Images Digital Images can be thought of as a matrix (raster image / raster map) of grey levels / intensity values magnification of the rat s nose
6 Introduction Digital Images Digital Images types dimensionality and nature of pixel values binary (bilevel) grey scale color false-color multi-spectral semantic (thematic),... 3D Digital Images picture elements are called voxels (from "volumetric" and "pixel") not addressed here
7 Introduction Electromagnetic Spectrum The Electromagnetic Spectrum we perceive only a small range of colours of the electromagnetic spectrum (~ 430nm 790nm) gamma rays, X rays, ultraviolet light, visible spectrum, infrared, microwaves, radio waves,...
8 Introduction Electromagnetic Spectrum The Electromagnetic Spectrum fundamental equations relation between wavelength (λ) and frequency (ν): relation between energy and frequency: E = hν λ = c ν
9 Introduction Electromagnetic Spectrum The Electromagnetic Spectrum we perceive only a small range of colours of the electromagnetic spectrum (~ 430nm 790nm) objects are perceived by the light they reflect achromatic light: all wavelengths are reflected equally chromatic light: some wavelengths are reflected predominantly
10 Contents Introduction Biological Vision
11 Introduction Visual Perception Metaphysics All men by nature desire to know. An indication of this is the delight we take in our senses; for even apart from their usefulness they are loved for themselves; and above all others the sense of sight. Aristotle (384 BC 322 BC)
12 Introduction Visual Perception The Human Eye
13 Introduction Visual Perception What happens? photons are reflected at objects pattern of reflected photons is sensed biological vision: with photoreceptors ( pixel) computer vision: with a (digital) camera and further processed as a multidimensional signal biological vision: in the visual cortex computer vision: DIP, computer vision Vision from Per-Erik Forssén "Visual Object Detection"
14 Introduction Visual Perception Image Formation Pinhole Camera Model from Per-Erik Forssén "Visual Object Recognition"
15 Introduction Visual Perception Image Formation Pinhole Camera Model focal length between 17 mm (min. refractive power, objects farther than 3m) and 14 mm (max. refractive power) 15 / 100 = h / 17 h = 2.55 mm focal length (min. refractive power)
16 Introduction Visual Perception The Human Eye sphere (diameter ~ 20 mm)
17 Introduction Visual Perception The Human Eye cornea constant thickness lens with fixed focal length responsible for ~ 75% of the refraction
18 Introduction Visual Perception The Human Eye cornea lens constant thickness lens with fixed focal length responsible for ~ 75% of the refraction can be contracted zoom (to a plane) shape of lens is varied to focus on objects at different distances IR and UV light are absorbed by proteins in the lens structure
19 Introduction Visual Perception The Human Eye cornea lens constant thickness lens with fixed focal length responsible for ~ 75% of the refraction can be contracted zoom (to a plane) 2D image on the retina represents the light pattern reflected from a thin plane in the 3D spatial world, the lens is focused on
20 Introduction Visual Perception The Human Eye pupil opening varies from 2 to 8 mm regulates the amount of light reaching the retina
21 Introduction Visual Perception The Human Eye pupil opening varies from 2 to 8 mm regulates the amount of light reaching the retina aperture of a camera source: Wikipedia (
22 Introduction Visual Perception The Human Eye pupil opening varies from 2 to 8 mm regulates the amount of light reaching the retina aperture of a camera light reaches the retinal surface (spherical, inner wall of the eyeball) photoreceptors "translate" light into electrical pulses distributed over the retinal surface non-uniform resolution
23 Introduction Visual Perception Foveal/Peripheral View
24 Introduction Visual Perception Foveal/Peripheral View
25 Introduction Visual Perception Foveal/Peripheral View
26 Introduction Visual Perception Foveal/Peripheral View
27 Introduction Visual Perception The Human Eye pupil opening varies from 2 to 8 mm regulates the amount of light reaching the retina aperture of the eye light reaches the retinal surface (spherical, inner wall of the eyeball) photoreceptors are distributed over the retinal surface cones & rods
28 Introduction Visual Perception The Human Eye two classes of light receptors distributed over the retinal surface cones (bright-light vision phototopic) 6-7 million around fovea colour & bright-light vision fine details cones with peak sensitivity for long, medium and short wavelengths (red, green, blue) only cones in the fovea
29 Introduction Visual Perception The Human Eye two classes of light receptors distributed over the retinal surface cones (bright-light vision phototopic) 6-7 million around fovea colour & bright-light vision fine details red, green, blue rods (dim-light vision scotopic) million coarse details "night vision"
30 Introduction Visual Perception Receptor Distribution in the Human Eye no receptors where the optic nerve emerges (blind spot) radially symmetric distribution around the fovea except from the blind spot distribution of rods and cones around the fovea
31 Introduction Visual Perception Why do we sometimes have red eyes in photos?
32 Introduction Visual Perception
33 Introduction Visual Perception The Fovea responsible for sharp vision (reading, watching television,...) circular indentation (diameter ~ 15 mm) approx cones in this area (~ a 15 x 15 mm 2 square sensor)
34 Introduction Visual Perception The Fovea responsible for sharp vision (reading, watching television,...) circular indentation (diameter ~ 15 mm) approx cones in this area (~ a 15 x 15 mm 2 square sensor) resolution that can be achieved with a CCD chip? 10 MP camera 7.2 x 5.3 mm 2 ( pixels / mm 2 ) "pixels" on 1.5 x 1.5 mm 2 ( "pixels" / mm 2 )
35 Introduction Visual Perception Receptor Position in the Human Eye photo-receptors turned away from the lens!
36 Introduction Brightness Adaptation in the Human Eye human eye can adapt over 10 orders of magnitude! 6 orders in phototopic vision (cones) accomplished by brightness adaptation (changes in the overall sensitivity) much smaller range for each brightness adaptation level B a subjective brightness is a log function of the light intensity brightness discrimination poor at low levels of illumination better with increasing illumination
37 Introduction Sensation vs Perception Ganglion Cells 125 million rods & cones 1 million ganglion cells implement local neighbourhood operations (local receptive field) respond if there is a difference between "center and surround" (center-surround cells) contrast-sensitive vision absolute intensity / color not available to the brain important for colour constancy
38 Introduction Visual Perception Image Formation in the Human Eye perceived breightness is not a simple function of intensity! Mach bands stripes appear darker near a more intense stripe (and vice versa) caused by inhibitory neural connections
39 Introduction Visual Perception Image Formation in the Human Eye perceived breightness is not a simple function of intensity! Mach bands stripes appear darker near a more intense stripe (and vice versa) caused by inhibitory neural connections simultaneous contrast a regions' perceived breightness depends on the intensity in the neighbourhood
40 Introduction Visual Perception perceived breightness is not a simple function of intensity! simultaneous contrast a regions perceived breightness depends on the intensity in the neighbourhood
41 Introduction Sensation vs Perception Sensation operation of basic sensory systems result of physical stimuli and low-level processes Perception involve higher-level processes in the percipient memories expectations emotions state of fatigue or alertness "The Great Ideas of Psychology" (TTC)
42 Introduction Visual Perception Biological Vision development responded to evolutionary necessities
43 Introduction Visual Perception Biological Vision bear pixels?
44 Introduction Visual Perception Importance of Context Torralba et al., CVPR 2007, Short Course
45 Introduction Visual Perception Importance of Context Torralba et al., CVPR 2007, Short Course
46 Introduction Visual Perception Image Formation in the Human Eye perceived breightness is not a simple function of intensity! Mach bands stripes appear darker near a more intense stripe (and vice versa) caused by inhibitory neural connections simultaneous contrast a regions perceived breightness depends on the intensity in the neighbourhood optical illusions
47 Introduction Optical Illusions Optical Illusions the eye / brain fills in nonexisting information perceives geometrical properties of an object wrongly characteristic of the human visual system and not yet fully understood... (some examples follow)
48 Introduction Optical Illusions concentrate on the dot in the middle and move your head back and forth
49 Introduction Optical Illusions movement created only in the brain
50 Introduction Optical Illusions concentrate on the cross in the middle and the moving circle turns green!... after a while the violet circles disappear!!
51 Introduction Optical Illusions 1. Relax and stare for 30s - 45s to the four dots in the centre 2. Then look slowly to a white wall (large uniformly coloured area) close to you 3. You will see a bright spot forms at the wall 4. Now blink a few times 5. What do you see? Whom do you see?
52 Introduction Optical Illusions
53 Contents Introduction Image Processing
54 1 Introduction Image Processing Image Processing versus Image Analysis world imaging image analysis data image computer graphics knowledge image understanding, computer vision image processing
55 1 Introduction Image Processing Image Processing versus Image Analysis world visualisation imaging image analysis image processing data image computer graphics knowledge image understanding, computer vision
56 Introduction Image Processing Fundamental Steps in problem Lara Croft has to get out of a room
57 Introduction Image Processing Fundamental Steps in problem image acquisition
58 Introduction Image Processing Fundamental Steps in problem image acquisition preprocessing
59 Introduction Image Processing Fundamental Steps in problem image acquisition preprocessing segmentation
60 Introduction Image Processing Fundamental Steps in problem image acquisition preprocessing segmentation representation and description model of objects
61 Introduction Image Processing Fundamental Steps in problem image acquisition preprocessing segmentation representation and description model of objects recognition and interpretation what are these objects?
62 Introduction Image Processing Fundamental Steps in problem image acquisition preprocessing segmentation representation and description model of objects recognition and interpretation what are these objects? solution
63 Contents Course Contents
64 2 Course Contents Filtering in the Spatial Domain (Image Enhancement) "Lena" with noise Median filtering edge detection
65 2 Course Contents Fourier Transform original image power spectrum after Fourier transformation inverse transform of filtered power spectrum
66 2 Course Contents Image Restoration?
67 2 Course Contents Binary Image Operations original image thresholding closing
68 2 Course Contents Segmentation? original image segmented (binary) image
69 2 Course Contents Morphological Image Processing & Shape Description... after morphological closing grey image... after segmentation... after skeletonization
70 2 Course Contents Colour Representation and Use RGB space CIE s chromaticity diagram
71 2 Course Contents Classification and Introduction to Pattern Recognition? original image result of classification
72 Contents Digital Image Acquisition
73 3 Digital Image Acquisition Digital Image Representation f(x,y) as a matrix of real numbers f (0,0) f (0,1)... f (0, N 1) f (1,0) f (1,1)... f (1, N 1) f ( x, y) = = ( aij ) f ( M 1,0) f ( M 1,1)... f ( M 1, N 1) elements of the matrix are called pixels (2D)
74 3 Image Formation and Image Sampling Image Formation Model illumination i(x,y) from a source reflectivity r(x,y) = reflection / absorption in the scene f(x,y) = i(x,y) r(x,y) i ~ 0.1 lm/m 2 (full moon) 1000 lm/m 2 (office) 10'000 lm/m 2 (cloudy day) 90'000 lm/m 2 (sunny day)
75 3 Image Formation and Image Sampling Image Formation Model illumination i(x,y) from a source reflectivity r(x,y) = reflection / absorption in the scene f(x,y) = i(x,y) r(x,y) r = 0.01 (black velvet) 0.65 (stainless steel) 0.80 (flat white wall) 0.90 (silver-plated metal) 0.93 (snow)
76 3 Image Formation and Image Sampling Image Formation Model illumination i(x,y) from a source reflectivity r(x,y) = reflection / absorption in the scene f(x,y) = i(x,y) r(x,y) Image Sampling digital image can be seen as a 2D function f(x,y) x and y are the spatial coordinates f(x,y) is the grey level / intensity at position (x,y) a digital image must be sampled (digitized) in space (x,y): image sampling in amplitude f(x,y): grey-level quantization
77 3 Digital Image Acquisition Image Sampling and Quantization conversion of continuous input signal to a digital form continuous signal digitized image
78 3 Digital Image Acquisition Image Sampling and Quantization conversion of continuous input signal to a digital form sample f(x,y) in both coordinates (sampling) continuous signal
79 3 Digital Image Acquisition Image Sampling and Quantization conversion of continuous input signal to a digital form sample f(x,y) in both coordinates (sampling) continuous signal
80 3 Digital Image Acquisition Image Sampling and Quantization conversion of continuous input signal to a digital form sample f(x,y) in both coordinates (sampling) sample f(x,y) in amplitude (quantization)
81 3 Digital Image Acquisition Image Sampling uniform same sampling frequency everywhere adaptive higher sampling frequency in areas with greater detail (not very common) determines the spatial resolution
82 3 Digital Image Acquisition Image Sampling spatial resolution: smallest discernible detail in the image (line pairs per mm, for example)
83 3 Digital Image Acquisition Image Quantisation greylevel quantization
84 Part 1: Course Introduction Achim J. Lilienthal Thank you! AASS Learning Systems Lab, Dep. Teknik Room T1209 (Fr, o'clock) Course Book Chapters 1 &
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