Robust segmentation based tracing using an adaptive wrapper for inducing priors.

Area of Science:

• Computer Vision
• Image Analysis
• Machine Learning

Background:

• Segmentation-based tracing algorithms identify object boundaries by propagating information across image frames.
• Existing methods are often application-specific, limiting their adaptability to new domains.
• Automatic adaptation of tracing techniques across diverse applications remains an underexplored area.

Purpose of the Study:

• To develop an adaptive segmentation tracing technique that automatically adjusts to different applications.
• To improve the robustness and accuracy of object tracing in challenging imaging conditions.
• To obviate the need for manual parameter tuning in image segmentation tasks.

Main Methods:

• Learning a prior model on topological dynamics to guide segmentation transitions.
• Augmenting a generic tracing technique with a locality-sensitive prior derived from optic flow fields.
• A two-stage approach involving a generic tracer with perturbed parameters and a learned topology prior for propagation.

Main Results:

• Demonstrated applicability across surveillance, biological, and medical image datasets.
• Achieved good tracing performance even under severe clutter and complex object dynamics.
• The learned topology prior effectively adapts the generic tracer, removing the need for manual parameter optimization.

Conclusions:

• The proposed method offers a robust and adaptive solution for segmentation-based object tracing.
• The learned topological dynamics prior significantly enhances tracing performance and generalizability.
• This approach reduces manual effort and improves efficiency in image analysis across various fields.