Nothing Special   »   [go: up one dir, main page]

Presentation is loading. Please wait.

Presentation is loading. Please wait.

Computer Vision CS302 Data Structures Dr. George Bebis

Similar presentations


Presentation on theme: "Computer Vision CS302 Data Structures Dr. George Bebis"— Presentation transcript:

1 Computer Vision CS302 Data Structures Dr. George Bebis http://www.cse.unr.edu/CVL Computer Vision CS302 Data Structures Dr. George Bebis

2 What is Computer Vision? Nice sunset! “Making computers see and understand” What is Computer Vision Nice sunset! Making computers see and understand

3 Connections to other disciplines Computer Vision Image Processing Pattern Recognition Machine Learning Artificial Intelligence Robotics Psychology Neuroscience Computer Graphics Connections to other disciplines Computer Vision Image Processing Pattern Recognition Machine Learning Artificial Intelligence Robotics Psychology Neuroscience Computer Graphics

4 Image Processing Image Processing

5 Image Processing (cont’d) Image enhancement Image Compression Image Processing (cont’d) Image enhancement Image Compression

6 Computer Graphics Computer Graphics

7 Computer Graphics (cont’d) Image Output: Geometric Models Synthetic Camera projection, shading, lighting models Computer Graphics (cont’d) Image Output: Geometric Models Synthetic Camera projection, shading, lighting models

8 Computer Vision Computer Vision

9 Computer Vision (cont’d) Model Output: Real Scene CamerasImages Computer Vision (cont’d) Model Output: Real Scene CamerasImages

10 Why is Computer Vision Difficult? (1) It is a many-to-one mapping. –Inverse mapping has non-unique solution. –A lot of information is lost in the transformation from the 3D world to the 2D image. (2) It is computationally intensive. - A typical video is 30 frames / sec (3) We do not understand the recognition problem. Why is Computer Vision Difficult. (1) It is a many-to-one mapping.

11 Viewpoint variations Michelangelo 1475-1564 Viewpoint variations Michelangelo

12 Illumination changes Illumination changes

13 Scale changes Scale changes

14 Deformations Deformations

15 Occlusions Occlusions

16 Background clutter Background clutter

17 Motion blurring Motion blurring

18 Intra-class variation Intra-class variation

19 Local ambiguity Local ambiguity

20 Applications Industry (visual inspection and assembly) Security and Surveillance (object detection, recognition, and tracking) Human Activity Recognition Traffic Monitoring and Analysis Robotics Medical Applications Many more … Applications Industry (visual inspection and assembly) Security and Surveillance (object detection, recognition, and tracking) Human Activity Recognition Traffic Monitoring and Analysis Robotics Medical Applications Many more …

21 Industrial Computer Vision (Machine Vision) Industrial computer vision systems work really well. Make strong assumptions about lighting conditions Make strong assumptions about the position of objects Make strong assumptions about the type of objects Industrial Computer Vision (Machine Vision) Industrial computer vision systems work really well.

22 Visual Inspection COGNEX Visual Inspection COGNEX

23 Optical character recognition (OCR) Digit recognition, AT&T labs http://yann.lecun.com/exdb/lenet/ Technology to convert scanned docs to text License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition Automatic check processing Optical character recognition (OCR) Digit recognition, AT&T labs   Technology to convert scanned docs to text License plate readers   Automatic check processing

24 Biometrics Biometrics

25 Login without a password… Fingerprint scanners on many new laptops, other devices Face recognition systems begin to appear more widely http://www.sensiblevision.com/ http://www.sensiblevision.com/ Login without a password… Fingerprint scanners on many new laptops, other devices Face recognition systems begin to appear more widely

26 Face Recognition: Apple iPhoto http://www.apple.com/ilife/iphoto/ Face Recognition: Apple iPhoto

27 Face detection Many new digital cameras now detect faces –Canon, Sony, Fuji, … Face detection Many new digital cameras now detect faces –Canon, Sony, Fuji, …

28 Smile detection? Sony Cyber-shot® T70 Digital Still Camera Smile detection Sony Cyber-shot® T70 Digital Still Camera

29 How the Afghan Girl was Identified by Her Iris Patterns Iris Biometrics How the Afghan Girl was Identified by Her Iris Patterns Iris Biometrics

30 Hand-based Biometrics Hand-based Biometrics

31 Object Recognition (in supermarkets) LaneHawk by EvolutionRobotics “A smart camera is flush-mounted in the checkout lane, continuously watching for items. When an item is detected and recognized, the cashier verifies the quantity of items that were found under the basket, and continues to close the transaction. The item can remain under the basket, and with LaneHawk,you are assured to get paid for it… “ Object Recognition (in supermarkets) LaneHawk by EvolutionRobotics A smart camera is flush-mounted in the checkout lane, continuously watching for items.

32 Mobile visual search: Google GogglesGoogle Goggles http://www.google.com/mobile/goggles/ Mobile visual search: Google GogglesGoogle Goggles

33 Visual Surveillance and Human Activity Recognition Surveillance and security Visual Surveillance and Human Activity Recognition Surveillance and security

34 Traffic Monitoring http://www.honeywellvideo.com/ Traffic Monitoring

35 Smart cars: –Vision systems currently in high-end BMW, GM, Volvo models. Mobileye Smart cars: –Vision systems currently in high-end BMW, GM, Volvo models. Mobileye

36 Automatic Panorama Stitching Automatic Panorama Stitching

37 Automatic Panorama Stitching (cont’d) find correspondences Automatic Panorama Stitching (cont’d) find correspondences

38 3D Modeling 3D Modeling

39 Medical Imaging Skin/Breast Cancer Detection 3D imaging MRI, CT Enable surgeons to visualize internal structures through an automated overlay of 3D reconstructions of internal anatomy on top of live video views of a patient. Image guided surgery Grimson et al., MIT Medical Imaging Skin/Breast Cancer Detection 3D imaging MRI, CT Enable surgeons to visualize internal structures through an automated overlay of 3D reconstructions of internal anatomy on top of live video views of a patient.

40 Robotics http://www.robocup.org/ Semantic Robot Vision Challenge http://www.semantic-robot-vision-challenge.org/ http://www.youtube.com/watch?v=GItjILILB50 Robotics   Semantic Robot Vision Challenge     v=GItjILILB50

41 Vision in space Vision systems (JPL) used for several tasks –Panorama stitching –3D terrain modeling –Obstacle detection, position tracking –For more, read “Computer Vision on Mars” by Matthies et al.Computer Vision on Mars NASA'S Mars Exploration Rover Spirit NASA'S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007. Vision in space Vision systems (JPL) used for several tasks –Panorama stitching –3D terrain modeling –Obstacle detection, position tracking –For more, read Computer Vision on Mars by Matthies et al.Computer Vision on Mars NASA S Mars Exploration Rover Spirit NASA S Mars Exploration Rover Spirit captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.

42 Vision-based Interaction and Games Nintendo Wii has camera-based IR tracking built in. See Lee’s work at CMU on clever tricks on using it to create a multi-touch display!Lee’s work at CMU multi-touch display Assistive technologies Kinect Vision-based Interaction and Games Nintendo Wii has camera-based IR tracking built in.

43 Movie Special Effects Movie special effects Insert synthetic objects in real image sequences;. Change artificially the position or the orientation of a camera. Freeze a moving 3D scene. Movie Special Effects Movie special effects Insert synthetic objects in real image sequences;.

44 Computer Vision Jobs !! Academia –MIT, UC-Berkeley, CMU, UIUC, USC …… UNR! National Labs and Government –Los Alamos National Lab –Lawrence Livermore National Lab –Navy, Air-force, Army Industry –Microsoft, Intel, IBM, Xerox, Compaq, Siemens, HP, TI, Motorola, Phillips, Honeywell, Ford http://www.cs.ubc.ca/spider/lowe/vision.html See: Computer Vision Jobs !. Academia –MIT, UC-Berkeley, CMU, UIUC, USC …… UNR.

45 What skills would you need to succeed in this field? Strong programming skills (i.e., C, C++, Matlab) Very good knowledge of Data Structures and Algorithms Very good background in Mathematics, especially in: –Calculus –Linear Algebra –Probabilities and Statistics –Numerical Analysis –Geometry What skills would you need to succeed in this field.

46 Related Courses at UNR CS474/674 Image Processing and Interpretation (every Fall) CS485/685 Computer Vision (every Spring) CS486/686 Advanced Computer Vision (every Fall) CS479/679 Pattern Recognition (every other Spring) Special Topics –Biometrics, Object Recognition, Neural Networks, –Mathematical Methods for Computer Vision CS482/682 Artificial Intelligence CS773A Machine Intelligence CS791Q Machine Learning CS480/680 Computer Graphics Related Courses at UNR CS474/674 Image Processing and Interpretation (every Fall) CS485/685 Computer Vision (every Spring) CS486/686 Advanced Computer Vision (every Fall) CS479/679 Pattern Recognition (every other Spring) Special Topics –Biometrics, Object Recognition, Neural Networks, –Mathematical Methods for Computer Vision CS482/682 Artificial Intelligence CS773A Machine Intelligence CS791Q Machine Learning CS480/680 Computer Graphics

47 CS474/674 Image Processing 2 exams Homework 5-6 programming assignments noise eliminationlight correction contrast enhancement CS474/674 Image Processing 2 exams Homework 5-6 programming assignments noise eliminationlight correction contrast enhancement

48 CS485/685 Computer Vision Two exams Homework 5-6 programming assignments Paper presentations (grad students) face recognition3D reconstructionobject recognition CS485/685 Computer Vision Two exams Homework 5-6 programming assignments Paper presentations (grad students) face recognition3D reconstructionobject recognition

49 More information on Computer Vision Computer Vision Home Page http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html UNR Computer Vision Laboratory http://www.cs.unr.edu/CVL More information on Computer Vision Computer Vision Home Page     UNR Computer Vision Laboratory


Download ppt "Computer Vision CS302 Data Structures Dr. George Bebis"

Similar presentations


Ads by Google