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Bayesian decision feedback for segmentation of binary images.

S R Kadaba1, S B Gelfand, R L Kashyap

  • 1Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1996
PubMed
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We developed real-time algorithms for segmenting noisy binary images using Markov mesh random fields (MMRFs). These algorithms efficiently estimate pixel values, proving effective for image segmentation tasks.

Area of Science:

  • Computer Vision
  • Image Processing
  • Statistical Modeling

Background:

  • Binary image segmentation is crucial for image analysis.
  • Markov Mesh Random Fields (MMRFs) offer a probabilistic framework for image modeling.
  • Real-time processing is essential for many image segmentation applications.

Purpose of the Study:

  • To develop real-time recursive algorithms for binary image segmentation.
  • To compute the Maximum A Posteriori (MAP) estimate of pixels using MMRFs.
  • To reduce computational complexity while maintaining high performance.

Main Methods:

  • Formulated the MAP fixed-lag estimation problem for MMRFs.
  • Derived an optimal recursive estimator.
  • Introduced hard and soft decision feedbacks to reduce computational complexity.

Related Experiment Videos

  • Applied algorithms to synthetic and real-world images.

    • Main Results:

    • Developed computationally viable real-time algorithms for MMRF-based image segmentation.
    • Demonstrated effective MAP estimation with a fixed lookahead.
    • Achieved a balance between computational complexity and segmentation performance.
    • Validated the algorithms on diverse image datasets.

    Conclusions:

    • The proposed real-time algorithms are effective for segmenting binary images modeled by MMRFs.
    • Decision feedback mechanisms significantly reduce computational load.
    • The algorithms show strong subjective relevance and performance for image segmentation.