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Response to [Computer Vision for …] by Freeman et al.


  1. Large object tracking
    • Make use of ‘Image Moments’ – it is useful based on the assumption that the interactive applications only need a very coarse summary of global averages of orientation and position”;
    • Two methods: motion-based calculates the differences between frames, shape-based analyze at a frame-by-frame basis.
    • Applications: coarse control e.g., controlling the moving directions of a robot, controlling the movement of a game character.
  2. Shape Recognition
    • Orientation histogram – a histogram summarizing the orientations of an image’s edges;
    • Example-based applications involved two phases: training and running. In the training process, the user provides the system with one or more examples of a particular hand shape. The computer stores these hand shapes and calculate their orientation histograms. In run time, it compares the orientation histogram of the input hand shape with the pre-stored ones and determine the closest match.
  3. Motion Analysis
    • Usually achieved using optical flow. But given certain application context, the algorithm can be tremendously simplified, e.g., by measuring horizontal/vertical pixel offsetting frequency one can estimate how fast (and how big the range) the object is moving.
  4. Small object tracking
    • Example: how to track a hand that appears in a much larger image. Form a template of what a hand looks like; use the template to scan the image to determine at what location a hand is most likely to occur.

About Xiang 'Anthony' Chen

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2 thoughts on “Response to [Computer Vision for …] by Freeman et al.

  1. this paper is too old…

    Posted by Kun He (@_HorrorKid) | January 1, 2012, 10:00 pm

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