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sign language recognition using HMM

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  • This is amazing!!!
    I am looking to do exactly the same in my graduation project!
    Can you help me and provide me with the source code, please?
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here
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  • 1. Sign Language RecognitionUsingHidden Markov Model
    Presented by:
    VipulAgarwal - 070905060
  • 2. Outline
    • INTRODUCTION
    • 3. SIGN LANGUAGE
    • 4. PRE-PROCESSING
    • 5. SKIN AND HAND DETECTION
    • 6. OPTICAL FLOW ANALYSIS
    • 7. FEATURE EXTRACTION FOR TRAINING DATA
    • 8. HIDDEN MARKOV MODEL & ITS USE
    • 9. PROGRESS REPORT
    • 10. DEMONSTRATION
  • Introduction
    • Interaction with computers may often not be a comfortable experience.
    • 11. Computers should be able to communicate with people with body language.
    • 12. Hand gesture recognition becomes important …
    Interactive human-machine interface and virtual environment
  • 13. Introduction
    Two common technologies for hand gesture recognition
    GLOVE-BASED METHOD
    Using special glove-based device to extract hand posture
    VISION-BASED METHOD
    3D hand/arm modeling
    Appearance modeling
  • 14. Introduction
    3D hand/arm modeling
    Highly computational complexity
    Using many approximation process
    Appearance modeling
    Low computational complexity
    Real-time processing
  • 15. Sign Language
    Rely on the hearing society
    Two main elements:
    Low and simple level signed alphabet, mimics the letters of the spoken language.
    Higher level signed language, using actions to mimic the meaning or description of the sign.
    The project aim is to make the computer recognize low and simple level American Sign Language.
  • 16. Sign Language
    American Sign Language
    26 signs to denote the alphabets.
    10 signs to denote numbers
  • 17. Pre - Processing
    The video sequence used has a lot of noise due to:
    Low quality of the webcam
    Improper lighting conditions
    Background
  • 18. Pre - Processing
    Pre-processing involves reducing the noise and illumination problems.
    The morphological operations used for reducing the noise involves:
    Dilation
    Statistical Elimination
  • 19. Pre - Processing
    DILATION>
    A disc shaped region is traversed over every blob and the ones which do not fit the disc are removed completely.
  • 20. Pre - Processing
    STATISTICAL ELIMINATION>
    For every region the area is computed. Since hand is the one with the largest area, all blobs having less than a specified area are removed.
  • 21. Hand Detection
    First all the noise is removed in the pre-processing stage.
    Now we assume that the hand is the largest skin blob in our video sequence.
    We calculate the area of every blob and take the one with the largest area.
    We also calculate the bounding box of the region containing the hand for further analysis
  • 22. Hand Detection
  • 23. Optical Flow Analysis
    DEFINITION:
    Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative motion between an observer (an eye or a camera) and the scene.
  • 24. Optical Flow Analysis
    Why Optical Flow Analysis?
    Till now the system is just able to detect the hand and follow the bounding box as the hand moves.
    The problem now is that we need to define a way to take a snapshot of the hand when the hand is not moving.
  • 25. Optical Flow Analysis
    Using this technique we find the motion in the hand. When the hand has stabilized, we assume that the gesture is ready. We then take a snapshot of the hand and perform the recognition on that image.
  • 26. Feature Extraction
    For training the network with test images we perform the following feature extraction technique:-
    Thresholding of the test hand
    Converting to a binary image
    Finding the centroid of the hand and orientation of the minor axis.
    Making feature vectors using a predefined number of features.
  • 27. Feature Extraction
    Extracting the intersection of the feature vectors with the boundary points.
    Finding the scalar length of the vectors from the centroid.
    Normalising the lengths in a scale of 1 to 100 to make it scaling invariant.
  • 28. Feature Extraction
  • 29. Hidden Markov Model (HMM)
    HMMs allow you to estimate probabilities of unobserved events
    Given plain text, which underlying parameters generated the surface
  • 30. HMMs and their Usage
    HMMs are very common in Computational Linguistics:
    GESTURE RECOGNITION (observed: image, hidden: alphabets)
  • 31. Progress Report
    WORK COMPLETED:
    Data Collection
    Pre-processing
    Skin And Hand Detection
    Optical Flow Analysis
    Feature Extraction For Training Data
    WORK REMAINING:
    Training The Hidden Markov Model
  • 32. Any Questions …?

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