Talking Glasses [First Seminar]
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Talking Glasses [First Seminar]

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[Talking Glasses Project]

[Talking Glasses Project]
My First Seminar\'s Presentation.

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Talking Glasses [First Seminar] Talking Glasses [First Seminar] Presentation Transcript

    • Theoretical Areas
    • Project Domain
    Problem Definition
    Motivations
    Project Objective
    • Main Objective
    • Other Objectives
    Project Methodology
    Survey
    • Related works
    • Asking Experts
    Project Architecture
    Challenges
    Tools
    Time plan
    References
  • Computer Vision
  • Machine Learning
  • Object Classification
  • Stereo Vision
  • The primary motivation for our work is helping blind people
  • Interested
    In Computer Vision
  • Interested
    In Machine Learning
  • Developing object classification system that may be useful for a large scale of applications.
    Medical systems
    Surveillancesystems
  • Using stereo vision techniques and narration to develop an object recognition system that help blind to independently navigate the surrounding.
  • Improve segmentation accuracy using depth information.
  • “Object Categorization by Learned Universal Visual Dictionary” by Microsoft Research
    • “This field of research is very interesting, but it needs a lot of work to reach to the goals.” Prof. Dr. Mostafa Gadal-Haqq
    • “I think your project is somewhat ambitious for a senior project.” Prof. David G. Stork
  • Data Acquisition(Stereo Camera)
    Segmentation
    Classification
    Feature Extraction
    Learned Models
    Classification
    Post Processing
    Depth Estimation
    Narrator(Text To Speech Engine)
  • Partial Occlusion
  • Segmentation Accuracy
  • Complex Scene
  • Scale
    Illumination
    Rotation
  • Languages
    C++
    Open Source
    OpenCV Library
    Software
    Visual Studio 2010 Professional Edition
    Matlab 2010
    Hardware
    Stereo Camera
  • 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