9. Asking ExpertsProject Architecture Challenges Tools Time plan References

11. Computer Vision

12. Machine Learning

14. Object Classification

15. Stereo Vision

19. The primary motivation for our work is helping blind people

20. Interested In Computer Vision

21. Interested In Machine Learning

24. Developing object classification system that may be useful for a large scale of applications. Medical systems Surveillancesystems

26. Using stereo vision techniques and narration to develop an object recognition system that help blind to independently navigate the surrounding.

27. Improve segmentation accuracy using depth information.

29. “Object Categorization by Learned Universal Visual Dictionary” by Microsoft Research

32. Data Acquisition(Stereo Camera) Segmentation Classification Feature Extraction Learned Models Classification Post Processing Depth Estimation Narrator(Text To Speech Engine)

34. Partial Occlusion

35. Segmentation Accuracy

36. Complex Scene

37. Scale Illumination Rotation

39. Languages C++ Open Source OpenCV Library Software Visual Studio 2010 Professional Edition Matlab 2010 Hardware Stereo Camera

43. Books Pattern Classification (2nd Edition) by Richard O. Duda,Peter E. Hart,David G. Stork. Digital Image Processing3rd Ed by Gonzalez and Woods. Computer Vision: Algorithms and Applications by Richard Szeliski, Microsoft Research. Papers Peter Carbonetto, Nando de Freitas, Paul Gustafson and Natalie Thompson. Bayesian feature weighting for unsupervised learning, with application to object recognition. Scott Helmer and David G.Lowe, “Using Stereo for Object Recognition,” International Conference on Robotics and Automation(ICRA), Anchorage, Alaska (May 2010). D. Hoiem, A. Efros, and M. Heber, “Putting Objects In Perspective, ” in CVPR, 2006