Unsupervised Region-Growing Network for Object Segmentation in
Atmospheric Turbulence
- URL: http://arxiv.org/abs/2311.03572v1
- Date: Mon, 6 Nov 2023 22:17:18 GMT
- Title: Unsupervised Region-Growing Network for Object Segmentation in
Atmospheric Turbulence
- Authors: Dehao Qin, Ripon Saha, Suren Jayasuriya, Jinwei Ye and Nianyi Li
- Abstract summary: We present a two-stage unsupervised object segmentation network tailored for dynamic scenes affected by atmospheric turbulence.
In the first stage, we utilize averaged optical flow from turbulence-distorted image sequences to craft preliminary masks for each moving object.
We release the first moving object segmentation dataset of turbulence-affected videos, complete with manually annotated ground truth masks.
- Score: 11.62754560134596
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we present a two-stage unsupervised foreground object
segmentation network tailored for dynamic scenes affected by atmospheric
turbulence. In the first stage, we utilize averaged optical flow from
turbulence-distorted image sequences to feed a novel region-growing algorithm,
crafting preliminary masks for each moving object in the video. In the second
stage, we employ a U-Net architecture with consistency and grouping losses to
further refine these masks optimizing their spatio-temporal alignment. Our
approach does not require labeled training data and works across varied
turbulence strengths for long-range video. Furthermore, we release the first
moving object segmentation dataset of turbulence-affected videos, complete with
manually annotated ground truth masks. Our method, evaluated on this new
dataset, demonstrates superior segmentation accuracy and robustness as compared
to current state-of-the-art unsupervised methods.
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