Image Processing and Computer Visionranga/files/mathematics_society_talk.pdf · Image Processing and Computer Vision Ranga Rodrigo Department of Electronic and Telecommunication Engineering

Image Processing and ComputerVision

Ranga RodrigoDepartment of Electronic and Telecommunication Engineering

University of MoratuwaSri Lanka

Mathematics Society Talk

February 24, 2009

Mathematics Society Talk Image Processing and Computer Vision 1/35

Outline

1 Introduction

2 Applications

3 Vision in Automation

4 Software Tools

5 Examples of State-of-the-Art

6 Summary


Introduction

What Is Image Processing?

Image ProcessingIn image processing, we attempt to manipulate theinput image to obtain a “better” image.


Introduction

What Is Computer Vision?

Computer VisionIn computer vision, we analyze the input image andobtain an understanding or make a decision.


Introduction

What Is Computer Vision?

The goal is the emulation of the visual capabilityof human beings using computers.In other words, computer vision is making themachine see as we do!It is challenging.Steps:

1 Image acquisition2 Image manipulation3 Image understanding4 Decision making


Introduction

Main Driving Technologies

Signal processing.Multiple view geometry [2].Optimization.Machine learning.Hardware and algorithms.


Applications

Applications 1

Automotive:Lane departure warning systems.Head tracking systems for drowsiness detection.Driver assistance systems.Reading automobile license plates, and trafficmanagement.

Photography:In camera face detection [5], red eye removal, andother functions.Automatic panorama stitching [1].

1(From http://www.cs.ubc.ca/spider/lowe/vision.html)Mathematics Society Talk Image Processing and Computer Vision 9/35

Applications

ApplicationsMovie and video (a very big industry):

Augmented reality.Tracking objects in video or film and solving for 3-Dmotion to allow for precise augmentation with 3-Dcomputer graphics.Multiple cameras to precisely track tennis and cricketballs.Human expression recognition.Software for 3-D visualization for sportsbroadcasting and analysis.Tracking consistent regions in video and insertvirtual advertising.Tracking for character animation.Motion capture, camera tracking, panoramastitching, and building 3D models for movies.


Applications

Camera Tracking

Source: http://www.2d3.com/capability

Show 2d3 video.


http://www.2d3.com/capability

Applications

ApplicationsGames:

Tracking human gestures for playing games orinteracting with computers.Tracking the hand and body motions of players (tocontrol the Sony Playstation).Image-based rendering, vision for graphics.

General purpose:Inspection and localization tasks, people counting,biomedical, and security. etc.Object recognition and navigation for mobilerobotics, grocery retail, and recognition from cellphone cameras.Laser-based 3D vision systems for use on the spaceshuttles and other applications.Image retrieval based on content.


Applications

ApplicationsIndustrial automation (a very big industry):

Vision-guided robotics in the automotive industry.Electronics inspection systems for componentassembly.

Medical and biomedical (maturing):Vision to detect and track the pose of markers forsurgical applications, needle insertion, and seedplanting.Teleoperations.Quantitative analysis of medical imaging, includingdiagnosis such as cancer.

Security and biometrics (thriving):Intelligent video surveillance.Biometric face, fingerprint, and iris recognition.Behavior detection.


Applications

Minimal Invasive Surgery

Source: http://www.davincisurgery.com/surgery/system/index.aspx


http://www.davincisurgery.com/surgery/system/index.aspx

Applications

Areas of Advancement

Hardware.Image segmentation.3-D reconstruction.Object detection.Navigation.Scene understanding.


Vision in Automation

What’sneeded?

cameras

software

actuators



Cameras

Camera, and a frame grabber.IEEE 1394 or USB cameras.Ethernet cameras.



Source: http://www.ptgrey.com/products/chameleon/index.asp

Source: http://www.matrox.com/imaging/products/vio/home.cfm


http://www.ptgrey.com/products/chameleon/index.asp

http://www.matrox.com/imaging/products/vio/home.cfm


Source: http://www3.elphel.com/sites/default/files/images/nc353 io geo.jpeg


http://www3.elphel.com/sites/default/files/images/nc353_io_geo.jpeg

Software Tools

Software Tools

Octave or Matlab.C or C++ with a library such as OpenCV.


Software Tools

Image Processing using Octave orMatlab

Simple and quick.A lot of library functions.Interpreted.


Software Tools

Octave Examples

Image reading and writing.Histograms.Filtering.


Software Tools

Image Processing using OpenCV

Power of C++.Well coded.


Software Tools

OpenCV Examples

1 Image reading and writing.2 Edge detection.3 Template matching.4 Capturing video.


Examples of State-of-the-Art

Segmentation Using Graph Cuts [4]



3-D Reconstruction

Can we obtain a 3-D view of a scene, given onlya set of (2-D) images?Yes. Using multiple view geometry, we canreconstruct a scene.Show Leibe et al. video [3].



Object Detection: Face Detection

Show OpenCV sample.Mathematics Society Talk Image Processing and Computer Vision 30/35


Navigation: Sanford’s Robot Stanley

Show video.


Summary

Conclusion

Vision-based automation is promising.Solutions are simple in a controlledenvironment.State-of-the-art is very interesting.


Summary

Thank you.

OpenCV examples, and Octave examples are here:http://www.ent.mrt.ac.lk/ ranga/publications.html


http://www.ent.mrt.ac.lk/~ranga/publications.html

Summary

Matthew Brown and David Lowe.Recognising panoramas.In Proceedings of the 9th International Conference on Computer Vision, pages1218–1225, Nice, France, October 2003.

Richard Hartley and Andrew Zisserman.Multiple View Geometry in Computer Vision.Cambridge University Press, 2nd edition, 2003.

Bastian Leibe, Nico Cornelis, Kurt Cornelis, and Luc Van Gool.Dynamic 3D scene analysis from a moving vehicle.In Proceedings of the IEEE Computer Society Conference on Computer Vision andPattern Recognition, pages 1–8, Minneapolis, MN, June 2007.

Carsten Rother, Vladimir Kolmogorov, and Andrew Blake.“GrabCut”: Interactive foreground extraction using iterated graph cuts.ACM Transactions on Graphics: Proceedings of the 2004 SIGGRAPH Conference,23(3):309–314, August 2004.

Paul Viola and Michael Jones.Rapid object selection using a boosted cascade of simple features.In Proceedings of the IEEE Computer Society Conference on Computer Vision andPattern Recognition, pages 511–518, Hawaii, December 2001.


Documents

Image Processing and Computer Visionranga/files/mathematics_society_talk.pdf · Image Processing and Computer Vision Ranga Rodrigo Department of Electronic and Telecommunication Engineering