# Partial Coherence for Object Recognition and Depth Sensing

arXiv: https://arxiv.org/abs/2401.02432 · HTML: https://mlsanity.com/paper/2401.02432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Reflections on Shannon Information: In search of a natural information-entropy for images](https://mlsanity.com/paper/1609.01117/) (2016)
- [Optically lightweight tracking of objects around a corner](https://mlsanity.com/paper/1606.01873/) (2016)
- [Direct Object Recognition Without Line-of-Sight Using Optical Coherence](https://mlsanity.com/paper/1903.07705/) (2019)
- [Imaging-free object recognition enabled by optical coherence](https://mlsanity.com/paper/1901.08118/) (2019)

## Abstract (arXiv)

We show a monotonic relationship between performances of various computer vision tasks versus degrees of coherence of illumination. We simulate partially coherent illumination using computational methods, propagate the lightwave to form images, and subsequently employ a deep neural network to perform object recognition and depth sensing tasks. In each controlled experiment, we discover that, increased coherent length leads to improved image entropy, as well as enhanced object recognition and depth sensing performance.