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Dynamic shape and appearance models.

Gianfranco Doretto1, Stefano Soatto

  • 1Visualization and Computer Vision Lab, GE Global Research Center, One Research Circle, Niskayuna, NY 12309, USA. doretto@research.ge.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 17, 2006
PubMed
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We introduce a new model for analyzing how image shapes and appearances change together over time. This method enhances active appearance models by using temporal correlations for efficient parameter inference.

Area of Science:

  • Computer Vision
  • Image Analysis
  • Machine Learning

Background:

  • Active Appearance Models (AAMs) are widely used for image analysis.
  • Existing AAMs often do not fully exploit temporal correlations in image sequences.
  • Modeling joint shape and appearance variation is crucial for dynamic scene understanding.

Purpose of the Study:

  • To propose a novel conditionally linear model for joint shape and appearance variation in image sequences.
  • To extend active appearance models by incorporating temporal dynamics.
  • To enable efficient inference of model parameters for dynamic image analysis.

Main Methods:

  • Developed a conditionally linear model for joint shape and appearance.
  • Leveraged temporal correlations between adjacent image frames.

Related Experiment Videos

  • Employed numerical optimization techniques from finite-element analysis and system identification.

    • Main Results:

    • The proposed model effectively captures joint variations in shape and appearance.
    • Efficient parameter inference was achieved using established optimization methods.
    • The model extends the capabilities of traditional active appearance models.

    Conclusions:

    • The new model provides an efficient and effective approach for analyzing dynamic image sequences.
    • Exploiting temporal correlations significantly enhances appearance modeling.
    • This work offers advancements in image sequence analysis and dynamic modeling.