INTRODUCTION IMAGE PROCESSING >INTRODUCTION & HUMAN VISION UTRECHT UNIVERSITY RONALD POPPE

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1 INTRODUCTION IMAGE PROCESSING >INTRODUCTION & HUMAN VISION UTRECHT UNIVERSITY RONALD POPPE

2 OUTLINE Course info Image processing Definition Applications Digital images Human visual system Human eye Reflectivity and luminance Psychophysics

3 COURSE INFO

4 COURSE OVERVIEW 1 Your lecturer: Ronald Poppe (r.w.poppe@uu.nl) Practicum assistants: Daphne Odekerken Mark Raasveldt Sander Vanheste General: Course code: INFOIBV Number of credits: 7.5 ECTS

5 COURSE OVERVIEW 2 Course form: Lectures (12 x 2 hours) Practicals (2 x 2 hours) Assignment (1 x 35 hours) Exam (1 x 2 hours)

6 COURSE OVERVIEW 3 Knowledge to be gained from the course: Introduction to basic manipulations of images From acquisition to complex processing Tested using the exam Skills to be gained from the course: Insight into image processing concepts Experience to be able to use and develop a wide variety of image processing techniques Tested with practicals and assignment

7 COURSE OVERVIEW 4 Course material Reader Digital and Medical Image Processing by Twan Maintz, 2005 (chapters 1-6, 8-9) Lectures and slides (partly courtesy of Nicholas Pronost) Exercises in lectures All available on the course website (after the lectures)

8 COURSE OVERVIEW 5 The practicals are to apply knowledge into practice Two sessions (each 2 hours) Carried out in pairs Answers handed over at end of the sessions Download software from website Two sessions: First (September 18 11:00-12:45) covers chapters 1-4 Second (October 2 11:00-12:45) covers chapters 5-6

9 COURSE OVERVIEW 6 The assignment is to gain hands-on expertise with image processing tasks Several assignments available Carried out in pairs C# framework available Deadline Sunday November 8, 23:00 Estimated time is approximately 35 hours, so start early! We keep ourselves to the rules of academic honesty Until October 8, you can get feedback on the suitability of your assignment idea

10 COURSE OVERVIEW 7 Course planning overall: Lectures in the first weeks Practicals the week after the topics are covered Full-time work on the assignment in the final weeks Plan ahead: Exam and assignment deadline are in the same week Student assistants are available to help with the assignment in the assignment help sessions (4x)

11 COURSE OVERVIEW 8 Regarding the assignment deadline, there are very few excuses when you find out: There is not enough time The software framework doesn t work after the first assignment week In the last week that your partner didn t do anything In summary: Start early Notify me as soon as possible when things go wrong Check your assignment idea with me October 8 the latest

12 COURSE OVERVIEW 9 Exam: November 6, 2014 (11:00 13:00): chapters 1-6, 8-9 I will provide practice exams before There will be an exam Q&A in the last lecture You can submit questions that I will elaborate on during this lecture

13 COURSE OVERVIEW 10 The grading is as follows: IBV = ( P1 + P2 + 4 * A + 6 * E ) / 12 Conditions: Exam should be at least grade 4 Practicals should have been finished, with at least grade 4 Assignment should have been finished, with at least grade 4

14 COURSE OVERVIEW 11 Important: register for the course! Otherwise you will not receive s about assignments, grades, etc. Do NOT send me s about extending deadlines, unless you have a VERY good reason DO send me an if you have trouble finding a partner for the assignment, or if your partner does not put in the effort Don t wait until the last moment

15 COURSE OVERVIEW 12 I encourage active participation so don t hold back with questions and remarks So in summary: This is a very interesting and fun course! You will have to spend some time though You will gain plenty of knowledge, insights and expertise to impress yourself and others You will have a solid base in image processing, and might consider the course Computer Vision when doing a master

16 QUESTIONS?

17 IMAGE PROCESSING

18 IMAGE PROCESSING Images are everywhere Images are natural to us Vision is often the dominant sense Many image processing tasks are natural to us Focusing Lighting control Object segmentation Image processing combines intuition and mathematics

19 IMAGE PROCESSING 2 Definition of image processing: the manipulation and analysis of information contained in images Simple examples: Glasses and lenses TV settings (brightness, contrast) Photo developing (from negative to photo) Applying Instagram-like filters

20 IMAGE PROCESSING 3 Image processing will take an image as input and the output will be a processed (enhanced) image Computer vision aims to extract a model/pattern from an image Image Computer vision Model

21 APPLICATIONS Purpose of image processing: Image enhancement Pattern recognition Data compression Data reduction Image combination Image synthesis

22 APPLICATIONS 2 Concrete applications: Or how to make/save money using image processing Enhancing license plates for automatic ticketing Recognizing urban areas from satellite images Recognizing watermarks in copied movies

23 APPLICATIONS 3 More complex applications using computer vision: Face recognition Movie understanding, big data Detecting anomalous behavior from security cams Gaming Smart cars

24 APPLICATIONS 4 Data visualization: process images and present them in a human-understandable manner

25 APPLICATIONS 5 Image segmentation: divide an image in meaningful spatial regions

26 APPLICATIONS 6 Image registration: align multiple images so they match spatially

27 DIGITAL IMAGES An image can be: Photo MRI scan Satellite image Line drawing Etc. A digital image is an image in digital form

28 IMAGE ACQUISITION Acquisition device Camera, CT/MRI scanner etc. Analog image / electric signal Digitizer Often integrated with device Digital image Image processing

29 IMAGE ACQUISITION 2 The digitizer converts analog signals to digital ones: Sampling (location) Quantization (value)

30 IMAGE REPRESENTATION Images are 2D matrices: Each pixel is a cell Top-left is (0,0) We report (x, y) or (y, x)!

31 IMAGE REPRESENTATION 2 We can also represent an image as a function: MM = ff xx, yy = xxxx for xx, yy 0,1,2,3 0,1,2 Or in a visual form:

32 IMAGE REPRESENTATION 3 In grayscale images, we typically: Associate values of 0 to black Associate values of 1 or 255 to white Image type determines the maximum value: 8-bit integer: 255 Floating point: 1 Pay attention to this during the practicals!

33 IMAGE DISPLAY When displayed, pixel values are converted to grey values on paper or on screen For color, we can do this per channel (RGB, HSV) Alternatives Lowest occurring value black, and highest white. Map linearly in between (stretching) Lowest possible (in image type) value black, and highest white. Map linearly in between Palette (lookup table)

34 HUMAN VISUAL SYSTEM

35 HUMAN VISUAL SYSTEM Why cover it here? Difference between perceived and displayed image Luminance is actual physical brightness Brightness is how we perceive it (e.g., in the dark, we distinguish between dark and light ) We often try to emulate the human visual system Our eyes/brain can do tricks that we like to mimic

36 HUMAN VISUAL SYSTEM 2

37 HUMAN VISUAL SYSTEM 3 We are interested in the eye and the part of the brain that is tasked with visual processing Light rays with a sufficient strength and within the right range of the electromagnetic spectrum will generate an electric pulse in the brain Cornea protects the eye and refracts the beams Pupil and cornea regulate the total amount of light Lens further refracts the light, to focus on certain distances The retina converts light into electric pulses using rods and cones

38 HUMAN VISUAL SYSTEM 4 Rods Cones 100 million 6-7 million Evenly spread More responsive grey Night vision (scotopic) Especially around fovea Three types color Day vision (photopic)

39 HUMAN VISUAL SYSTEM 5 Luminous efficiency: How do things appear colored?

40 HUMAN VISUAL SYSTEM 6 Color blindness explained:

41 HUMAN VISUAL SYSTEM 7 ρρ VV EE II LL object eye / camera E is energy ρ is object, I is light reflected from the object V is luminous efficiency, L is brightness of object II λλ = ρρ λλ EE λλ LL = 00 II λλ VV λλ ddλλ

42 PSYCHOPHYSICS Contrast is more important than luminance Weber s law: Just noticeable difference is proportional to background luminance When it s dark, you can perceive much smaller differences in luminance. There are also other contrast effects that influence the perceived brightness

43 PSYCHOPHYSICS 2 Mach band effect: Simultaneous contrast effect:

44 PSYCHOPHYSICS 3 Simultaneous contrast, the Benussi ring: Also when perceiving colors:

45 PSYCHOPHYSICS 4 The brain is used to group parts of images together and view it as a unit This is also termed Gestalt

46 PSYCHOPHYSICS 5 Other mind tricks

47 PSYCHOPHYSICS 6

48 QUESTIONS?

49 NEXT LECTURE

50 NEXT LECTURE Next lecture is about: Digital image acquisition (chapter 3 of reader) Friday September 4, 9:00-10:45 (UNNIK-GROEN) Suggested homework : Read Chapter 1 from reader Find a partner for the assignment Check out the C# software framework for the assignment