A Hybrid Level Set With Semantic Shape Constraint for Object Segmentation

Area of Science:

• Computer Vision
• Image Processing
• Computational Imaging

Background:

• Object segmentation is a fundamental task in image analysis.
• Existing level set methods often require multiple shape priors or struggle with complex image properties.

Purpose of the Study:

• To present a novel hybrid level set method for object segmentation.
• To improve segmentation accuracy and robustness, especially for textured and noisy images.

Main Methods:

• A hybrid level set framework deconstructing segmentation into shape transformation and curve evolution.
• Utilizing shape context for semantic shape prior encoding to guide curve evolution.
• Incorporating topology-preserving and kernelized energy functional terms.

Main Results:

• The proposed method demonstrates competitive and superior performance compared to existing methods.
• Effective segmentation achieved on synthetic, healthcare, and natural images.
• The hybrid approach enhances the level set framework's ability to incorporate diverse shape-related techniques.

Conclusions:

• The novel hybrid level set method with shape context guidance offers a significant advancement in object segmentation.
• The method's unique paradigm provides semantic understanding from a single shape prior.
• It offers improved segmentation for challenging images, outperforming traditional approaches.