Computer Vision Course Lecture 02. Image Formation Light and Color. Ceyhun Burak Akgül, PhD cba-research.com. Spring 2015 Last updated 04/03/2015
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1 Computer Vision Course Lecture 02 Image Formation Light and Color Ceyhun Burak Akgül, PhD cba-research.com Spring 2015 Last updated 04/03/2015 Photo credit: Olivier Teboul vision.mas.ecp.fr/personnel/teboul These slides have been adapted from James Hays s 2014 Computer Vision course slides at Brown University.
2 Course Outline Image Formation and Processing Light, Shape and Color The Pin-hole Camera Model, The Digital Camera Linear filtering, Filter banks, Multiresolution Feature Detection and Matching Edge Detection, Interest Points: Corners and Blobs Local Image Descriptors Feature Matching and Hough Transform Multiple Views and Motion Geometric Transformations, Camera Calibration Feature Tracking, Stereo Vision Segmentation and Grouping Segmentation by Clustering, Region Merging and Growing Advanced Methods Overview: Active Contours, Level-Sets, Graph-Theoretic Methods Detection and Recognition Problems and Architectures Overview Statistical Classifiers, Bag-of-Words Model, Detection by Sliding Windows CBA Research Computer Vision 2
3 A Very Primitive Image Let s design a camera Idea 1: put a piece of film in front of an object Do we get a reasonable image? CBA Research Computer Vision 3 Slide source: Seitz
4 Pinhole Camera Idea 2: add a barrier to block off most of the rays This reduces blurring The opening known as the aperture CBA Research Computer Vision 4 Slide source: Seitz
5 Pinhole Camera f c f = focal length c = center of the camera CBA Research Computer Vision 5 Figure from Forsyth
6 Mapping 3D World to 2D Plane What is lost? Length Who is taller? Which is closer? CBA Research Computer Vision 6
7 Length is not preserved A C B CBA Research Computer Vision 7
8 Mapping 3D World to 2D Plane What is lost? Length Angle Parallel? Perpendicular? CBA Research Computer Vision 8
9 Mapping 3D World to 2D Plane What is preserved? Straight lines are still straight CBA Research Computer Vision 9
10 Vanishing Points and Lines Parallel lines in the world intersect in the image at a vanishing point CBA Research Computer Vision 10
11 Vanishing Points and Lines Vanishing Line Vanishing Point o Vanishing Point o CBA Research Computer Vision 11
12 Vanishing Points and Lines Vertical vanishing point (at infinity) Vanishing line Vanishing point Vanishing point CBA Research Computer Vision 12 Slide from Efros, Photo from Criminisi
13 Mapping 3D World to 2D Plane Projection = World coordinates Image coordinates Optical Center (u. 0, v 0 ) f Z Y.. P X Y Z. u v u p v Camera Center (t x, t y, t z ) CBA Research Computer Vision 13
14 Adding a Lens circle of confusion A lens focuses light onto the film There is a specific distance at which objects are in focus Other points project to a circle of confusion in the image Changing the shape of the lens changes this distance CBA Research Computer Vision 14
15 Basic Characteristics of a Lens F optical center (Center Of Projection) focal point A lens focuses parallel rays onto a single focal point Focal point at a distance f beyond the plane of the lens Aperture of diameter D restricts the range of rays CBA Research Computer Vision 15 Slide source: Seitz
16 Depth of Field Slide source: Seitz f / 5.6 f / 32 Changing the aperture size or focal length affects depth of field CBA Research Computer Vision 16
17 Image Formation Digital Camera Film The Eye CBA Research Computer Vision 17
18 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection? λ light source CBA Research Computer Vision 18
19 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 19
20 What Happens to a Traveling Photon? Absorption Diffuse Reflection Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 20
21 What Happens to a Traveling Photon? Absorption Diffusion Specular Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 21
22 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 22
23 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 23
24 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ 2 λ 1 light source CBA Research Computer Vision 24
25 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection λ light source CBA Research Computer Vision 25
26 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection t=n t=1 light source CBA Research Computer Vision 26
27 What Happens to a Traveling Photon? Absorption Diffusion Reflection Transparency Refraction Fluorescence Subsurface scattering Phosphorescence Interreflection (Specular Interreflection) λ light source CBA Research Computer Vision 27
28 Digital Camera A digital camera replaces film with a sensor array Each cell in the array is light-sensitive diode that converts photons to electrons Two common types: Charge Coupled Device (CCD) and CMOS CBA Research Computer Vision 28 Slide by Steve Seitz
29 Sensor Array CMOS sensor CBA Research Computer Vision 29
30 Sampling and Quantization CBA Research Computer Vision 30
31 The Eye The human eye is a camera! Iris Colored annulus with radial muscles Pupil The hole (aperture) whose size is controlled by the iris Retina: Cones and Rodes The «Film» CBA Research Computer Vision 31 Slide by Steve Seitz
32 The Retina Cross-section of eye Cross section of retina Ganglion axons Ganglion cell layer Bipolar cell layer Pigmented epithelium Receptor layer CBA Research Computer Vision 32
33 Two Types of Light-Sensitive Receptors Cones cone-shaped less sensitive operate in high light color vision Rods rod-shaped highly sensitive operate at night gray-scale vision (shape) CBA Research Computer Vision Stephen E. Palmer,
34 Electromagnetic vs. Visible Spectrum Human Luminance Sensitivity Function CBA Research Computer Vision 34
35 . Light Source Spectra Some examples of the spectra of light sources A. Ruby Laser B. Gallium Phosphide Crystal Wavelength (nm.) D. Normal Daylight # Photons # Photons Wavelength (nm.) # Photons # Photons C. Tungsten Lightbulb CBA Research Computer Vision 35
36 Reflectance Spectra Some examples of the reflectance spectra of surfaces CBA Research Computer Vision 36
37 Physics vs. Perception There is no simple functional description for the perceived color of all lights under all viewing conditions, but there is A helpful constraint: Consider only physical spectra with normal distributions CBA Research Computer Vision 37
38 # Photons Physics vs. Perception Mean Hue blue green yellow Wavelength CBA Research Computer Vision 38
39 # Photons Physics vs. Perception Variance Saturation hi. high med. low medium low Wavelength CBA Research Computer Vision 39
40 # Photons Physics vs. Perception Area Brightness B. Area Lightness bright dark Wavelength CBA Research Computer Vision 40
41 . Physiology of Color Vision nm. RELATIVE ABSORBANCE (%) 100 S M L WAVELENGTH (nm.) CBA Research Computer Vision 41
42 Tetrachromatism Bird cone responses Most birds, and many other animals, have cones for ultraviolet. Some humans, mostly female, have slight tetrachromatism. CBA Research Computer Vision 42
43 Practical Color Sensing: Bayer Grid Estimate RGB at G cells from neighboring values CBA Research Computer Vision 44 Slide by Steve Seitz
44 Color Image R G B CBA Research Computer Vision 45
45 Images in Matlab Images represented as a matrix Suppose we have a NxM RGB image called im im(1,1,1) = top-left pixel value in R-channel im(y, x, b) = y pixels down, x pixels to right in the b th channel im(n, M, 3) = bottom-right pixel in B-channel imread(filename) returns a uint8 image (values 0 to 255) Convert to double format (values 0 to 1) with im2double row column G CBA Research Computer Vision R B
46 Color Spaces How can we represent color? CBA Research Computer Vision 47
47 Color Spaces: RGB Default color space 0,1,0 R (G=0,B=0) 1,0,0 G (R=0,B=0) Some drawbacks Strongly correlated channels Non-perceptual 0,0,1 B (R=0,G=0) CBA Research Computer Vision 48 Image from:
48 Color spaces: HSV Intuitive color space H (S=1,V=1) S (H=1,V=1) V (H=1,S=0) CBA Research Computer Vision 49
49 Color spaces: YCbCr Fast to compute, good for compression, used by TV Y=0 Y=0.5 Y (Cb=0.5,Cr=0.5) Cr Cb Y=1 Cb (Y=0.5,Cr=0.5) Cr (Y=0.5,Cb=05) CBA Research Computer Vision 50
50 Color spaces: L*a*b* Perceptually uniform * color space L (a=0,b=0) a (L=65,b=0) b (L=65,a=0) CBA Research Computer Vision 51
51 Luminance or Chrominance? CBA Research Computer Vision 52
52 Only Color (Chrominance) CBA Research Computer Vision 53
53 Only Intensity (Luminance) CBA Research Computer Vision 54
54 Back to Grayscale CBA Research Computer Vision 55
55 Course Outline Image Formation and Processing Light, Shape and Color The Pin-hole Camera Model, The Digital Camera Linear filtering, Filter banks, Multiresolution Feature Detection and Matching Edge Detection, Interest Points: Corners and Blobs Local Image Descriptors Feature Matching and Hough Transform Multiple Views and Motion Geometric Transformations, Camera Calibration Feature Tracking, Stereo Vision Segmentation and Grouping Segmentation by Clustering, Region Merging and Growing Advanced Methods Overview: Active Contours, Level-Sets, Graph-Theoretic Methods Detection and Recognition Problems and Architectures Overview Statistical Classifiers, Bag-of-Words Model, Detection by Sliding Windows CBA Research Computer Vision 56
56 Resources Books R. Szeliski, Computer Vision: Algorithms and Applications, 2010 available online D. A. Forsyth and J. Ponce, Computer Vision: A Modern Approach, 2003 L. G. Shapiro and G. C. Stockman, Computer Vision, 2001 Web CVonline: The Evolving, Distributed, Non-Proprietary, On-Line Compendium of Computer Vision Dictionary of Computer Vision and Image Processing Computer Vision Online Programming Development environments/languages: Matlab, Python and C/C++ Toolboxes and APIs: OpenCV, VLFeat Matlab Toolbox, Piotr's Computer Vision Matlab Toolbox, EasyCamCalib Software, FLANN, Point Cloud Library PCL, LibSVM, Camera Calibration Toolbox for Matlab CBA Research Computer Vision 57
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