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Generalized flooding and Multicue PDE-based image segmentation.

Anastasia Sofou¹, Petros Maragos

¹School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece. soufou@cs.ntua.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 14, 2008

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This study enhances image segmentation by integrating multiple cues like contrast and texture, improving results over single-cue methods. The novel approach uses partial differential equations for better image analysis quality.

Area of Science:

Computer Vision
Image Analysis
Computational Mathematics

Background:

Image segmentation is challenging due to application dependency and lack of a priori information.
High-quality image analysis necessitates integrating multiple segmentation cues.

Purpose of the Study:

To improve image segmentation by combining intensity contrast, region size, and texture information.
To develop an efficient segmentation scheme using partial differential equations (PDEs) and cartoon-texture decomposition.

Main Methods:

Extended watershed transform segmentation using PDE formulation for varied flooding criteria.
Introduced a coupled segmentation scheme integrating contrast and texture via cartoon-texture decomposition.
Quantified image characteristics like geometrical complexity and orientation using simplification operators and feature extraction.

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Main Results:

Demonstrated improved segmentation results by combining multiple cues compared to individual cue usage.
Validated the proposed coupled scheme using quantitative and qualitative experimental analysis.
Showcased the scheme's effectiveness driven by cartoon (U) and texture (V) image components.

Conclusions:

The proposed coupled segmentation scheme effectively integrates contrast and texture information for superior image segmentation.
The PDE-based approach offers a flexible and robust method for diverse image analysis tasks.
This research advances image segmentation techniques by leveraging multi-cue integration and advanced mathematical modeling.