1. Dr. Ghassabi z.r.ghassabi@gmail.com Tehran shomal University Spring 2015 z.r.ghassabi@gmail.com Digital Image Processing 1

2. Course Link 2 cipcv.ir http://cipcv.ir/lessons/Digital-Image-Processing/Tehran-Shomal-University

3. Grades Home work – 30% Exam- 40% Presentation and Participation- 30% If you write a good paper, your grade will become 20 3

4. Homework TA: Ali Rahmani, Email: ali110rahmani@gmail.comali110rahmani@gmail.com Format (email(subject)& exercise(name)): – DIP-NAME-Family-Exe1 Deadline: 1 week Group works: More than 1 week 4

5. Goals To make the graduate students acquainted with the fundamental concepts of Image Processing and Computer Vision 5

6. Paper Presenting a paper – Research paper (2010,…,2014) Comparing results of several papers (an evaluation study) Writing a research paper 6 http://www.matlabsite.com/1127/mvpacw9210-choice-of-topic-and-thesis-writing.html http://www.matlabsite.com/1748/fvacw9304-practical-thesis-preparation-and-academic- paper-publication.html

7. How To find a paper? –کنفرانسهای مهم پردازش تصویر و بینایی ماشین CVPR, ICIP, MICCAI,ICPR –سایتهای پردازش تصویر و بینایی ماشین دانشگاهها –مجلات مهم IEEE, Elsevier, springer http://cipcv.ir/related-magazine 7

8. CVPR (Computer Vision and Pattern Recognition) http://www.pamitc.org/cvpr14/ 8

9. CVPR (Computer Vision and Pattern Recognition) لیست مقالات سال گذشته به همراه پیاده سازی و دیتاست http://www.cvpapers.com/cvpr2013.html 9

10. CVPR (Computer Vision and Pattern Recognition) لیست مقالات سال گذشته به همراه پیاده سازی و دیتاست http://www.cvpapers.com/cvpr2013.html 10

11. How to find a paper? ICPR (International Conference on Pattern Recognition) – ICPR 2013, 2012, …2010 http://www.icpr2012.org/overview.html 11

12. How to find a subject? Scope of subjects: – Face Recognition, Object Recognition, Document analysis, Biometric, remote sensing, Image registration, Image segmentation, etc.

13. How to find a Subject? See Scope In “Call Papers” http://www.icpr2012.org/cfp.html 13

14. Books – Main books: Digital Image Processing (3rd Edition), Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing using Matlab – Other books: – Image processing toolbox 14

15. Outline Introduction Digital Image Fundamentals Intensity Transformations and Spatial Filtering Filtering in the Frequency Domain Image Restoration and Reconstruction Color Image Processing Wavelets and Multi resolution Processing Image Compression Morphological Operation Object representation Object recognition 15

16. Introduction An image may be defined as: A two-dimensional function, f(x, y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates f(x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities.

17. Introduction 17 Digital picture produced in 1921 from a coded tape by a telegraph printer with special type faces. (McFarlane.†) Digital picture made in 1922 from a tape punched after the signals had crossed the Atlantic twice. Some errors are visible. (McFarlane.) Unretouched cable picture of Generals Pershing and Foch, transmitted in 1929 from London to New York by 15-tone equipment. (McFarlane.) The first picture of the moon by a U.S. spacecraft. Ranger 7 took this image on July 31, 1964 at 9 : 09 A.M. EDT, about 17minutes before impacting the lunar surface. (Courtesy of NASA.)

18. Introduction

19. 19 Chest X-Ray X-Ray Imaging

20. Introduction KneeSpine Head (MRI: Magnetic Resonance Image) Imaging in Radio Band

21. Introduction

22. X-rays CT Angiographie MRI UltraSound SPECT PET

23. Introduction PET-CT

24. Introduction Example – MR to CT Difference – Imaging technique – Patient position – Intensity pattern – Non-overlapping areas But – Same patient – No deformation of tissue

25. Introduction Ref. Image: MRITarget Image: SPECTRegistered: MRI + SPECT

26. Introduction MRA

27. Introduction MRI as a Multi Channels imaging modalities: PD T 1 T 2

28. Introduction MRI as a Multi Channels imaging modalities: PD weighted T2 weighted

29. Introduction

30. 30 To Monitoring environmental Conditions on the Planet Nasa’s LandSat : Washington DC Imaging in the Visible and Infrared Bands (Remote Sensing)

31. Introduction 31 Imaging in the Visible and Infrared Bands (Industry) A circuit Board : Inspect them for missing part Bottles : Look for bottle that are not filled up to an acceptable level Pill container: Look for missing pills

32. Introduction Defective Cookies Non-Defective Cookies Imaging in the Visible and Infrared Bands (Industry)

33. ساختن تصاویر Composite Introduction Unimodal Retinal Images

34. Introduction Multimodal Retinal Images

35. Introduction IRIS

36. Introduction Remote Sensing

37. Fundamental Steps in DIP

38. Image Enhancement Poor Contrast ImageEnhanced Image Original ImageSharpened Image

39. Image Enhancement Sharpening an image: (a) original and (b) sharpened. (a)(b)

40. Image Restoration Distorted Image Restored Image Geometric Distorted ImageRestored Image

41. Image Restoration (a) (b) An example of noise cleaning

42. Image Restoration (c) (b)(a) Image restoration (a)Original clean image, (b) Camera motion-induced blurred Image (c) Deblurred Image.

43. Image Compression Original image Compressed image(1/10) Compressed image(1/20) Compressed image(1/30)

44. Image Engineering Image Understanding Image Analysis Image Processing Image Processing High Layer Middle Layer Low Layer Image Engineering Feature Measurement Object Representation Image Segmentation Data Out Image In

45. Image Analysis قطعه بندی تصویر (Image Segmentation)

46. Image Analysis Object Representation (descriptors)

47. Representation Examples of descriptors

48. Object recognition

49. Fundamental Steps in DIP

50. Image Engineering Image Understanding Image Analysis Image Processing Image Processing High Layer Middle Layer Low Layer Image Engineering Feature Measurement Object Representation Image Segmentation Data Out Image In

51. OBJECTS ANIMALS INANIMATE PLANTS MAN-MADENATURAL VERTEBRATE ….. MAMMALS BIRDS GROUSEBOARTAPIR CAMERA Image Understanding

53. Image Categorization Training Labels Training Images Classifier Training Training Image Features Testing Test Image Trained Classifier Outdoor Prediction

54. Learning a classifier Given some set of features with corresponding labels, learn a function to predict the labels from the features xx x x x x x x o o o o o x2 x1

55. Formulation: binary classification Formulation +1 x1x1 x2x2 x3x3 xNxN … … x N+1 x N+2 x N+M ??? … Training data: each image patch is labeled as containing the object or background Test data Features x = Labels y = Where belongs to some family of functions Classification function Minimize misclassification error (Not that simple: we need some guarantees that there will be generalization)

56. Many classifiers to choose from SVM Neural networks Naïve Bayes Bayesian network Decision Trees K-nearest neighbor Etc. Which is the best one?

57. Summary Framework Feature representation Feature Extraction Image preprocessing Feature Classification Enhancement Restoration, etc. Enhancement Restoration, etc. Segmentation (Color, shape, texture) Segmentation (Color, shape, texture) About six weeks About two weeks About one week

58. Computer Vision Robotics Neuroscience Graphics Computational Photography Machine Learning Medical Imaging Human Computer Interaction Optics Image Processing Feature Matching Recognition