Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation
Despite great advances, finding accurate segmentation remains a challenging task, especially in scenarios with cluttered backgrounds, complex intensity variations and topology appearance. Minimal path models have exhibited their strong ability in addressing image segmentation tasks. However, the performance of minimal paths-based segmentation approaches is heavily influenced by model initialization, hence limiting their application scope in practice. In this work, we propose a novel mask proposal voting framework that overcomes the major drawback of classical approaches, allowing robust segmentation even in complicated scenarios. Firstly, we introduce an efficient method for constructing ...
| Authors | Li Liu, Mingzhu Wang, Zhenjiang Li et al. |
|---|---|
| Primary category | cs.CV |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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