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Soc Inequality & Public Policy

by: Ernestine Rowe

Soc Inequality & Public Policy SOCC 152

Ernestine Rowe

GPA 3.83


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Class Notes
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This 5 page Class Notes was uploaded by Ernestine Rowe on Thursday October 22, 2015. The Class Notes belongs to SOCC 152 at University of California - San Diego taught by Staff in Fall. Since its upload, it has received 28 views. For similar materials see /class/226813/socc-152-university-of-california-san-diego in social psychology at University of California - San Diego.

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Date Created: 10/22/15
Analysis of Binary Images Introduction to Computer Vision Lecture 6 Assignment 1Due on next thursday Forthis Week there is alink on the Web page for reading which isn t yet active Announcem ents The appeamnce ofcolors Color appearance is strongly affected by at least flighting striking the retina a other nearby colors space a adaptation to previous views time 7 state ofmind mm c u lum Light Spectrum 3 1 2 Talking about colors Spectrum 7 A positive function over interval 400nm 700nm In nite number ofvalues needed Nam es red harvest gold cyan aquamarine aubum chestnut A large discrete set ofcolor names RGB values 39 Just3 numbers so Color Re ectance Measured color spectrum is afunction ofthe spectrum ofthe illumination and re ectance munrite mutant a mm W Color matching experiment gW 4 lo nugwmlmknmc LxumMENY mnmmmwulx 4 mm or AM W m 19quot 2 mm m w a m m m m umquot 11 W mm Aim Mn M Ma gamerlt14 ma mum WPquot lmmmwmmw In 7 Primary 1111 1PM mam pePgM Where p s the strenth ufeach pnmary and PI mhe spectrum ufpnmary magnum Color Matching Functions 1mm 7 Syshm mamchmmahc wavelengths are 526 Km 444 Am 54 2mm RGB Color Cube Black ufculuurs fur r g a b m the range U71 euef elem uf each mary In practice 7 pnmanes gvm by mm pm 915 e phasphmsaze39hematenals an39heface fth mm Samantha gww whzn N stuck by electors YIQ Model Y 0299 1 0596 Q 0212 0587 70275 70532 0114 R 70321 G 0311 B Used by NTSC TV standard Separates Hue amp Saturation LQ from Luminance Y m 971 CIE ny Chrom aticity Space HSV Hexcone Hue Saturation Value AKA Hue Saturatation Intensity HIS Magenta Hexagon arises from projection of cube onto plane orthogonal to RGB lll CSE152 Spr 05 Green 1120quot Saturation retro Computer Vision Metameric Lights Metamers A B 7 7 200 300 g IOO 400 I Idenmv ll In mnsl thwrvpn and are CSE152 Spr 05 400 Wavelength nm 411 METAMERIC LIGHTS Two lights with these spectral power distribution appear cal ed metamers Arl eclral power distribution of a tungsten bulb B The spectral power distribution at light emitted from a conventional television monitor whose three phosphor intensities were set to maLch the lighl in panel A in appearance 500 600 700 roximallon to the retro Computer Vision CSE152 Spr 05 Binary Image Processing retro Computer Vision RGBgt HSV Note that for HWl you may nd different ways to do this convertion in the literature If you use something different just inclue a citation so we can know where your equations came from Let MAXmaXRGB let MlNminRGB 0 gtlt 60 ifR MAX BR 4 x 60 ifB e MAX Smax min max V max rutro Computer Vision csE152 Spr 05 csE152 Spr 05 rutro Computer Vision Basic Steps 01 2 Morphological operators sometimes 3 Find pixels corresponding to a region 4 Compute properties of each region csE152 Spr 05 l Labeling pixels as foregroundbackground rutro Computer Vision Histogrambased Segmentation HOW do we 5616 a ThreShOId Ex brighf objecf on dark background Manually determine threshold experimentally Hisfogr39am 7 Good when lighting is stable and high contrast Numb of was seleCt threShOId 0 Automatic thresholding 0 Create binary image Mile method 1XgtY lt T 39gt OOBY 0 7 Mode method 10 gt T 39gt OOBY 1 7 Peakiness detection 7 Iterative algorithm Gray value T c5215 am i m gamma csm w Macmmv m P Tile Method Mode Method 0 If the Size of the object is approx known pick T MOdel intens y in eaCh region Ri as st the area under the histogram corresponds to the constan N0U I size of the object If 1711 5 R then may 2 m quoti 2 w TL 1 g TIquot 76 l i p 1 V2 T Em0 Enz2a 552152 anus l m Cmpmuvmnn 552152 was l m Emma mm Example Image with 3 regions Ideal hisfogr39am Finding the peaks and valleys 0 It is a not trivial problem 1 H2 H3 The valleys are Add noise separafe regions 1 H2 H3 25152 an m emu Wm smmm m Emma mm Peakiness Detection Algorithm I Find the two HIGHEST LocAL MAXIMA at a MINIMUM DISTANCE APART g and gJ Find lowest point between them gk Measure peakiness 7 minailtggtHltggtgtHltgigt Find gvgygk with highest peakiness 9 9k 9i smz an n l mo ampm me Regions csmz ms ma common vision What is a region I Maximal connected set of poinw in the image With same brightness Value eg 1 I Two points are connected if there exists a continuous path joining them I Regions can be simply connected For every pair of poinw in the region all smooth paths can be smoothly and continuously deformed into each other Otherwise region is multiply connected holes csm shins hm commm Connected I What are the connected regions in this binary image I Which regions are contained within which region csmz ms ma common vision Connected I What the connected regions in this binary image I Which regions are contained within which region ESEISZAHHS hm commm Four amp Eight Connectedness Four Connected Eight Connected C521529pr 5 noocmonmnn


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