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Directed Connected Operators: Asymmetric Hierarchies for Image Filtering and Segmentation.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|September 11, 2015
Summary
This study introduces directed connected operators for image processing, moving beyond symmetric pixel relations. This enables new directed acyclic graph models for richer image representations and versatile applications.
Area of Science:
- Computer Science
- Image Processing
- Graph Theory
Background:
- Connected operators are standard for digital image processing, often using hierarchical schemes.
- Existing graph-based methods rely on symmetric pixel adjacency, limiting representation flexibility.
Purpose of the Study:
- To introduce directed connected operators for hierarchical image processing.
- To extend image representation models beyond trees to directed acyclic graphs.
- To demonstrate the framework's utility in image filtering and segmentation.
Main Methods:
- Developed a novel framework for directed connected operators considering non-symmetric pixel adjacency.
- Introduced directed acyclic graph models generalizing traditional tree structures.
- Described efficient methods for building and managing these advanced data structures.
Main Results:
- The proposed directed operators enable richer image representation models.
- These models generalize standard hierarchical structures like component trees and binary partition trees.
- The framework efficiently handles these complex graph structures.
Conclusions:
- Directed connected operators offer a versatile approach to hierarchical image processing.
- The use of directed acyclic graphs provides a more generalized and powerful image representation.
- The framework demonstrates significant potential for advanced image filtering and segmentation tasks.
