# Image-free single-pixel segmentation

arXiv: https://arxiv.org/abs/2108.10617 · HTML: https://mlsanity.com/paper/2108.10617/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Accelerating the Super-Resolution Convolutional Neural Network](https://mlsanity.com/paper/1608.00367/) (2016)
- [HRank: Filter Pruning using High-Rank Feature Map](https://mlsanity.com/paper/2002.10179/) (2020)
- [Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice](https://mlsanity.com/paper/1911.01685/) (2019)
- [Understanding Deep Learning Techniques for Image Segmentation](https://mlsanity.com/paper/1907.06119/) (2019)
- [Compressed Learning: A Deep Neural Network Approach](https://mlsanity.com/paper/1610.09615/) (2016)
- [Optical machine learning with incoherent light and a single-pixel detector](https://mlsanity.com/paper/1904.10851/) (2019)
- [Reconstruction-free action inference from compressive imagers](https://mlsanity.com/paper/1501.04367/) (2015)
- [Experimental comparison of single-pixel imaging algorithms](https://mlsanity.com/paper/1707.03164/) (2017)
- [Single-Pixel Pattern Recognition with Coherent Nonlinear Optics](https://mlsanity.com/paper/2010.02273/) (2020)
- [Robust and Efficient Single-Pixel Image Classificationwith Nonlinear Optics](https://mlsanity.com/paper/2101.11696/) (2021)

## Built on by (most cited first, 2 of 2)

- [Weighted Encoding Optimization for Dynamic Single-pixel Imaging and Sensing](https://mlsanity.com/paper/2201.02833/) (2022)
- [The Lift Spectrum: How Measurement-to-Space Adaptivity Shapes Robustness in Image-Free Single-Pixel Sensing](https://mlsanity.com/paper/2607.22077/) (2026)

## Abstract (arXiv)

The existing segmentation techniques require high-fidelity images as input to perform semantic segmentation. Since the segmentation results contain most of edge information that is much less than the acquired images, the throughput gap leads to both hardware and software waste. In this letter, we report an image-free single-pixel segmentation technique. The technique combines structured illumination and single-pixel detection together, to efficiently samples and multiplexes scene's segmentation information into compressed one-dimensional measurements. The illumination patterns are optimized together with the subsequent reconstruction neural network, which directly infers segmentation maps from the single-pixel measurements. The end-to-end encoding-and-decoding learning framework enables optimized illumination with corresponding network, which provides both high acquisition and segmentation efficiency. Both simulation and experimental results validate that accurate segmentation can be achieved using two-order-of-magnitude less input data. When the sampling ratio is 1%, the Dice coefficient reaches above 80% and the pixel accuracy reaches above 96%. We envision that this image-free segmentation technique can be widely applied in various resource-limited platforms such as UAV and unmanned vehicle that require real-time sensing.