Modeling and segmentation of noisy and textured images using gibbs random fields

Area of Science:

• Computer Vision
• Image Processing
• Statistical Modeling

Background:

• Traditional image segmentation methods struggle with noisy and textured data.
• Gibbs distributions (GD) offer a powerful framework for modeling complex image data.
• Accurate parameter estimation is crucial for the performance of GD-based models.

Purpose of the Study:

• To develop a new approach using Gibbs distributions (GD) for modeling and segmenting noisy and textured images.
• To introduce dynamic programming-based segmentation algorithms under a maximum a posteriori (MAP) criterion.
• To propose a novel parameter estimation technique for GD models.

Main Methods:

• Hierarchical Gibbs distributions (GD) were employed to model noisy and textured image data.
• Dynamic programming algorithms were adapted for image segmentation using a statistical MAP criterion.
• Approximations were introduced to create computationally feasible, sub-optimal algorithms.
• A new parameter estimation technique was developed for GD models.

Main Results:

• The proposed Gibbsian model effectively represents noisy and textured image characteristics.
• The dynamic programming segmentation algorithms demonstrated significant effectiveness.
• The novel parameter estimation procedure proved useful for GD models.
• Example applications validated the approach's practical utility.

Conclusions:

• The developed Gibbs distribution approach provides a robust framework for noisy and textured image analysis.
• The dynamic programming segmentation algorithms, despite approximations, offer effective solutions.
• The new parameter estimation technique enhances the applicability of GD in image processing.