Computer Vision. Alexandra Branzan Albu Spring 2009

Transcription

1 Computer Vision Alexandra Branzan Albu Spring 2009

2 Staff Instructor: Alexandra Branzan Albu Office hours (EOW 315): by appointment CENG 421/ ELEC 536 : Computer Vision 2

3 Who am I? since 2005: Assistant professor at UVic (ECE) : Assistant professor at Laval (ECE) my research: Computer Vision medical image analysis human motion analysis CENG 421/ ELEC 536 : Computer Vision 3

4 Course information Textbook: Sonka, Hlavac, and Boyle: Image Processing, Analysis, and Computer Vision, 3 rd edition Additional readings will be posted on-line Course website: Schedule Tu, We, Fri 9:30-10:30 am ECS 104 CENG 421/ ELEC 536 : Computer Vision 4

5 Grading scheme 2 midterm exams, 25% each Assignments 15 % 3 assignments, 5% each Project 35% CENG 421/ ELEC 536 : Computer Vision 5

6 Project Goals of this project assignment: Expose you to the computer vision literature Research a computer vision problem of your choice Team work (2 students per team) is strongly encouraged Projects will be on different topics and results will be presented in class active participation to oral presentations will be marked everyone will get a taste of everyone else's topic. CENG 421/ ELEC 536 : Computer Vision 6

7 Project: CENG 421 You will implement a published algorithm. This requires: reading and understanding a published paper on the method a certain degree of creativity for transferring the algorithm from the published form to a functional form Experimental design: database generation, experimental validation and testing of your algorithm, performance analysis writing and presenting your work using the language of computer vision. CENG 421/ ELEC 536 : Computer Vision 7

8 Project: ELEC 536 Formulating a research question which will be the main goal of the project. This requires: Selecting the best set of algorithms in order to achieve the goal above (lit review) Modifying and interconnecting algorithms Thorough validation (quantitative performance evaluation) of the approach implemented in the project Outlining clearly the strengths and limits of your approach Choose a topic that is relevant for your research Think about the final report as a future conference paper CENG 421/ ELEC 536 : Computer Vision 8

9 Project assessment Oral presentation - Your presentation will be graded on your ability to clearly explain the project, and to lead a class discussion on your project. - You will have 20 minutes to do your presentation, followed by 10 minutes for discussion. - You will also be marked on active participation during the discussions (for all project presentations). CENG 421/ ELEC 536 : Computer Vision 9

10 Project assessment (cont d) CD/DVD code and demonstration - The code must be well structured and explained through comments; flowcharts may be appended to the written report. - Make sure that the demonstration is functional on another computer than your own (ex: missing DLLs) CENG 421/ ELEC 536 : Computer Vision 10

11 Project assessment (cont d) Written report - structured as a IEEE conference paper - IEEE template will be available on the course website - Introduction, related work, approach, experimental results, conclusions and references. - Written communication skills are essential for good quality technical reports CENG 421/ ELEC 536 : Computer Vision 11

12 Project milestones Each team or student will have a different project Project proposal is due by February 15; worth 3% of final mark Guidelines for project proposal will be posted on the course site. in-class presentation and discussion of all project proposals (1h) Progress report due by March 10; worth 2% of final mark CENG 421/ ELEC 536 : Computer Vision 12

13 Exams 2 Midterms Tentative dates: February 13; March 27 Format: a mixture of open-ended and closed questions (definitions and multiplechoice); some sketching/graphing, some general vision-related problem solving. CENG 421/ ELEC 536 : Computer Vision 13

14 Assignments Programming; the focus here is on using programs to solve specific computer vision problems, not on the programming techniques themselves. Students are expected to be honest in their academic work. Any work that you present as your own must in fact be your own work and not that of another. CENG 421/ ELEC 536 : Computer Vision 14

15 Some basic questions about CV What is Computer Vision? Why study Computer Vision? Why is Computer Vision difficult?

16 What is Computer Vision? computer vision aims at duplicating/mimicking/simulating the effect of human vision by electronically perceiving and understanding an image [Computer vision] started as a branch of artificial intelligence, and it turned out to be a multifaceted and complex issue. Roberto Manduchi, Dept. Of Computer Engineering, University of California, Santa Cruz Original goal of vision : understanding a single image representing a static scene ( object identification, structure retrieval etc.) Most recent trend: video understanding (motion representation, activity recognition, tracking, optical flow etc.) CENG 421/ ELEC 536 : Computer Vision 16

17 What is Computer Vision? (cont d) Computer vision is the study of methods which allow computers to "understand" images [ ] The term "understand" means here that specific information is being extracted from the image data for a specific purpose: either for presenting it to a human operator (e. g., if cancerous cells have been detected in a microscopy image), or for controlling some process (e. g., an industry robot or an autonomous vehicle). The image data that is fed into a computer vision system is often a digital gray-scale or colour image, but can also be in the form of two or more such images (e. g., from a stereo camera pair), a video sequence, or a 3D volume (e. g., from a tomography device). Source: Wikipedia CENG 421/ ELEC 536 : Computer Vision 17

18 The beginnings of computer vision 1970: The MIT copy-demo: a system composed of a camera and a robot arm were programmed : - to perceive an arrangement of white wooden blocks against a black background. - to build a copy of the structure from additional blocks. CENG 421/ ELEC 536 : Computer Vision 18

19 The MIT copy demo CENG 421/ ELEC 536 : Computer Vision 19

20 The MIT copy demo (cont d) CENG 421/ ELEC 536 : Computer Vision 20

21 Early vision consistent line labeling edge detection contrast enhancement more emphasis on image processing than on pattern recognition CENG 421/ ELEC 536 : Computer Vision 21

22 Marr s era (1982) Using ideas from the human vision system : psychophysics and human perception Shape-from-X: one dimension is lost during the projection of a 3D world onto a 2D image plane. This dimension could be retrieved by using shadow, texture, motion, or multiple views information In parallel, development of image processing algorithms unrelated to the human vision. ex: the Canny edge detector (see Matlab Image Processing toolbox) CENG 421/ ELEC 536 : Computer Vision 22

23 The mathematical era pushing complex mathematical techniques into Computer Vision finding a problem for a solution CENG 421/ ELEC 536 : Computer Vision 23

24 Current state of Computer Vision Some shape-from-x problems have been almost completely solved with industrial applications (stereo) The task of object recognition from 3D data is not easier now! Gradual transition from static image understanding to video understanding Typical video understanding problems: gesture recognition, activity description, facial expressions etc. video understanding systems should have a learning capability Suggested reading : Mubarak Shah, The changing shape of Computer Vision, Guest introduction, IJCV CENG 421/ ELEC 536 : Computer Vision 24

25 Why study Computer Vision? Applications: images are pervasive Computer vision algorithms are increasingly used in new technologies Jobs: the computer vision industry CENG 421/ ELEC 536 : Computer Vision 25

26 Why study Computer Vision? Jobs Feeling Software is looking for a computer vision software developer to join our team in dazzling Montreal, Canada. In this position, you will be working directly on our breakthrough 3D reconstruction technology that will allow the average Joe to reconstruct 3D interiors in five minutes. Think Microsoft PhotoSynth, but much faster, for smaller spaces, and creating photo-realistic 3D models. Source: CENG 421/ ELEC 536 : Computer Vision 26

27 The Computer Vision Industry Iridian. Location: Moorestown, New Jersey Revenues: 50 employees, $33 million investment funding (2000). Products: Vision system that acquires a high resolution image of a person's eye and performs positive identification based on iris pattern. Image is automatically acquired from distance of up to 1 meter. Vision technologies: This system combines an impressive range of modern computer vision technologies. It uses stereo processing to determine head location and distance, face matching to determine eye location, and rapid pan-tilt-zoomfocus to acquire high resolution image within less than 1 second. System uses pyramid processing hardware from Sarnoff Corp. TriPath Imaging. Location: Redmond, Washington Revenues: $27 million (2001), mostly vision related (Yahoo) Products: AutoPap scans microscope slides from Pap smears to detect abnormal cells that may indicate cervical cancer. Vision technologies: The slide is scanned under an automated microscope. Proprietary algorithms are used to identify CENG suspicious 421/ cells. ELEC 536 : Computer Vision 27

28 The Computer Vision Industry (cont d) Neptec Location: Ottawa, Canada Products: Develops computer vision systems for the Space Shuttles and other space applications. Imagis Location: Vancouver, Canada Products: Face recognition and ID systems for law enforcement, casinos, and other application areas. Dipix Technologies Location: Ottawa, Canada Products: Vision systems for the baked goods industry. Systems monitor bake color, shape, and size of bread, cookies, tortillas BrainTech Location: Vancouver, Canada Products: Systems for industrial automation, including recognition and precise 3D pose determination. VisionSphere Location: Montreal, Canada Products: Face recognition and license plate reading systems. For more info : CENG 421/ ELEC 536 : Computer Vision 28

29 Why study Computer Vision? Applications Images and movies are everywhere Fast-growing collection of useful applications building representations of the 3D world from pictures automated surveillance (ex: who s doing what), biometrics (ex: face finding and recognition) CENG 421/ ELEC 536 : Computer Vision 29

30 Why is Computer Vision difficult? Loss of information 3D 2D Pinhole model does not distinguish size of objects Image interpretation humans use knowledge-based reasoning; the practical ability of a machine to understand visual observations remains very limited Noise Too much data ex: content-based image retrieval algorithms Brightness measure in an image is given by complicated image formation physics More about this on section 1.2 of textbook CENG 421/ ELEC 536 : Computer Vision 30

31 Topics covered in this course Image formation and acquisition: geometry and physics of imaging Image preprocessing for feature extraction filtering edge detection feature detection Image segmentation thresholding edge-based region-based Data structures for image analysis Binary shape analysis. Mathematical morphology Visual pattern recognition Performance quantification of computer vision algorithms Motion analysis in video data CENG 421/ ELEC 536 : Computer Vision 31

32 Next course Image formation. Reading: Ch. 2, 3. CENG 421/ ELEC 536 : Computer Vision 32