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1 Paper Presenaion Schedle March 21 March 26 March 28 Bhara Joshi James Perry Brandon Hoseer Noah Harchelroad Jeremy Day Xiangy H Besy McCor Yhang L Noel Raley Zhiyan Li Seve Rbin Hao Chen Kevin Madison Yang Deng Josh McCormick Andrew Shler Brendan Odigwe Yxiang Sn Xiaoyi Sn Please send me yor slides before yor presenaion

2 Psedo Color Image Processing Psedo color/false color: assign colors o gray vales Enhance he visalizaion qaliy of he image Segmenaion resls Enhance he inensiy difference

3 Inensiy Slicing

4 Examples of Inensiy Slicing

5 Examples of Inensiy Slicing

6 Inensiy o Color Transformaion

7 Example

8 Fll-color Image Processing Process each componen/channel individally, hen generae he composie image Work on each pixel individally =,,,, y x p y x p y x p y x b g r p Pixel in color image

9 Color Transformaion For a color image wih n componens s i inp vales for all componens = T i 2 r1, r, L, r, i = 1,2, L n, n Op vale for i h componen Transformaion fncions Modify inensiy Color complemen negaive color image Color slicing Tonal correcion Color balancing Hisogram processing

10 Examples of Color Image Transformaion Original image Inensiy modificaion Complemen color Color slicing HSI RGB RGB

11 Tonal Correcion Correc he onal range disribion of color inensiies Recall he inensiy ransformaion in he gray level images For RGB model, each componen has he same ransformaion fncion For HSI model, he ransformaion is applied on he inensiy componen only

12 Color Balancing Correc color nbalance by analyzing a known color in image

13 Hisogram Processing Sep 1: Hisogram eqalizaion Sep 2: Saraion adjsmen

14 Reading Assignmen Reading Chaper 6.6, 6.7, 6.8 Read Chaper 7 Waveles and Mliresolion Processing

15 Review of Chaper 2- Chaper 5 Chaper 2 Hman vision sysem Basics of image processing Chaper 3 Inensiy ransformaion Spaial filering Chaper 4 Forier ransform Image convolion in freqency domain Chaper 5 Image denoise Image degradaion Image resoraion Chaper 6 Fndamenals of color image processing Color ransformaion

16 Hman Vision Sysem Brighness adapion Geomerical relaionship beween he real objec and he image of he objec The minimm size of he objec yo can see

17 Basics of Image Processing f x, y = i x, y r x, y Image sampling and qanizaion Spaial/inensiy resolion Dynamic range of he image I max / I min Image represenaion and soring Image inerpolaion Neares neighbor and bilinear Se operaions

18 Basics of Image Processing Con. Basic relaionships beween pixels Adjacency Conneciviy Pah Basic relaionships beween regions Adjacency bondary Disance measremen Mahemaical ools Difference beween marix and array operaion Linear/nonlinear operaion Applicaions of image averaging, sbracion, and mliplicaion

19 Inensiy Transformaion Log ransformaion Power-law gamma ransformaion Inensiy level slicing Applicaions and working condiions sing hese ransformaions

20 Hisogram Processing Wha is a hisogram of an image? Hisogram eqalizaion Hisogram maching

21 Spaial Filering Image convolion in spaial domain and has properies of Commaiviy, Associaiviy, disribiviy Image correlaion in spaial domain Spaial filers Smoohing filer Average filer Sharpening filer Laplacian filer Unsharp masking Sobel operaor Order-saisic filer Median filer Min/max filer

22 Forier Transform Forier series f = + n= c n e Uni implse and is sifing propery j2π n T f δ 0 d = f 0 Forier ransform F µ j2 = µ πµ f F e dµ j2πµ = f e d Image convolion in freqency domain

23 Basic Properies of FT Lineariy Translaion Modlaion Scaling Conjgaion Symmery bg af H bg af h + = + = F e H f h j π = = F H f e h j = = π 1 a F a H a f h = = * * F H f h = = f F F f µ

24 Image Degradaion Imporan noise models Gassian noise model Implse noise model Image denoise Varios mean filers and heir applicaions Order-saisic filers and heir applicaions Image resoraion Inverse filering, Wiener filering, and Consrained Leas Sqare filering Working condiions,,,,,,,, v N v F v H v G y x y x f y x h y x g + = + = η

25 Fndamenals of color image processing Primary/secondary colors Primary/secondary pigmens Wha s he difference beween hem? Chromaiciy: he and saraion Color gam: any color on a line segmen can be generaed by wo ending poins; he same color can be generaed by differen combinaions

26 Fndamenals of color image processing Con d RGB model CMYK model HSI model Reqiremen: how o represen a color in a specific model?

27 Color Transformaion Inensiy modificaion Color complemen Tonal correcion Color balancing Hisogram processing HSI model RGB model HSI model The choice of color model varies for a specific image HSI model Which model is he mos effecive o perform a specific ransformaion?