# Invisible-to-Visible: Privacy-Aware Human Segmentation using Airborne Ultrasound via Collaborative Learning Probabilistic U-Net

arXiv: https://arxiv.org/abs/2205.05293 · HTML: https://mlsanity.com/paper/2205.05293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 11 May 2022.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [A Probabilistic U-Net for Segmentation of Ambiguous Images](https://mlsanity.com/paper/1806.05034/) (2018)
- [EV-SegNet: Semantic Segmentation for Event-based Cameras](https://mlsanity.com/paper/1811.12039/) (2018)
- [Learning Disentangled Joint Continuous and Discrete Representations](https://mlsanity.com/paper/1804.00104/) (2018)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds](https://mlsanity.com/paper/2003.04210/) (2020)
- [Beyond Image to Depth: Improving Depth Prediction using Echoes](https://mlsanity.com/paper/2103.08468/) (2021)
- [So you think you can DAS? A viewpoint on delay-and-sum beamforming](https://mlsanity.com/paper/2007.11960/) (2020)

## Built on by (most cited first, 1 of 1)

- [hear-your-action: human action recognition by ultrasound active sensing](https://mlsanity.com/paper/2309.08087/) (2023)

## Abstract (arXiv)

Color images are easy to understand visually and can acquire a great deal of information, such as color and texture. They are highly and widely used in tasks such as segmentation. On the other hand, in indoor person segmentation, it is necessary to collect person data considering privacy. We propose a new task for human segmentation from invisible information, especially airborne ultrasound. We first convert ultrasound waves to reflected ultrasound directional images (ultrasound images) to perform segmentation from invisible information. Although ultrasound images can roughly identify a person's location, the detailed shape is ambiguous. To address this problem, we propose a collaborative learning probabilistic U-Net that uses ultrasound and segmentation images simultaneously during training, closing the probabilistic distributions between ultrasound and segmentation images by comparing the parameters of the latent spaces. In inference, only ultrasound images can be used to obtain segmentation results. As a result of performance verification, the proposed method could estimate human segmentations more accurately than conventional probabilistic U-Net and other variational autoencoder models.