# Mask Proposal Voting Based on Geodesic Framework for Robust Image Segmentation

arXiv: https://arxiv.org/abs/2606.14912 · HTML: https://mlsanity.com/paper/2606.14912/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation](https://mlsanity.com/paper/2312.04484/) (2023)
- [Learning deep structured active contours end-to-end](https://mlsanity.com/paper/1803.06329/) (2018)
- [PolarMask++: Enhanced Polar Representation for Single-Shot Instance Segmentation and Beyond](https://mlsanity.com/paper/2105.02184/) (2021)
- [Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation](https://mlsanity.com/paper/1704.03593/) (2017)
- [End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery](https://mlsanity.com/paper/2007.11691/) (2020)
- [Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning](https://mlsanity.com/paper/2209.07774/) (2022)
- [Global Minimum for a Finsler Elastica Minimal Path Approach](https://mlsanity.com/paper/1612.00343/) (2016)
- [A Region-based Randers Geodesic Approach for Image Segmentation](https://mlsanity.com/paper/1912.10122/) (2019)
- [HAFormer: Unleashing the Power of Hierarchy-Aware Features for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2407.07441/) (2024)
- [A Generalized Asymmetric Dual-front Model for Active Contours and Image Segmentation](https://mlsanity.com/paper/2006.07839/) (2020)
- [Geodesic Paths for Image Segmentation with Implicit Region-based Homogeneity Enhancement](https://mlsanity.com/paper/2008.06909/) (2020)
- [Geodesic Models with Convexity Shape Prior](https://mlsanity.com/paper/2111.00794/) (2021)
- [Grouping Boundary Proposals for Fast Interactive Image Segmentation](https://mlsanity.com/paper/2309.04169/) (2023)
- [Computing Geodesic Paths Encoding a Curvature Prior](https://mlsanity.com/paper/2308.15729/) (2023)

## Abstract (arXiv)

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 adaptive domain cuts as a constraint for initializing the region-based min-cut evolution, by which diverse and reliable mask proposal candidates can be generated, substantially increasing the possibility of accurately covering the objective region by these proposals. Secondly, we propose a new mask voting scheme to build a voting score map encoding the final segmentation information. In contrast to classical path voting methods, our model allows incorporating priors to assign different importance to each individual mask. As a consequence, the proposed segmentation model is capable of accurately delineating object boundaries under complex scenarios, and is insensitive to initialization. Experiments demonstrate that our method consistently outperforms state-of-the-art minimal path-based approaches in both accuracy and robustness.