# A Survey on Hough Transform, Theory, Techniques and Applications

arXiv: https://arxiv.org/abs/1502.02160 · HTML: https://mlsanity.com/paper/1502.02160/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2015.

## Built on by (most cited first, 11 of 11)

- [A Neuromorphic Vision-Based Measurement for Robust Relative Localization in Future Space Exploration Missions](https://mlsanity.com/paper/2206.11541/) (2022)
- [A Multi-Stage model based on YOLOv3 for defect detection in PV panels based on IR and Visible Imaging by Unmanned Aerial Vehicle](https://mlsanity.com/paper/2111.11709/) (2021)
- [Deep-TEMPEST: Using Deep Learning to Eavesdrop on HDMI from its Unintended Electromagnetic Emanations](https://mlsanity.com/paper/2407.09717/) (2024)
- [Persistence-based Hough Transform for Line Detection](https://mlsanity.com/paper/2504.16114/) (2025)
- [Spatial ModernBERT: Spatial-Aware Transformer for Table and Key-Value Extraction in Financial Documents at Scale](https://mlsanity.com/paper/2507.08865/) (2025)
- [Stateless actor-critic for instance segmentation with high-level priors](https://mlsanity.com/paper/2107.02600/) (2021)
- [Real-Time Wheel Detection and Rim Classification in Automotive Production](https://mlsanity.com/paper/2304.06560/) (2023)
- [EpidermaQuant: Unsupervised detection and quantification of epidermal differentiation markers on H-DAB-stained images of reconstructed human epidermis](https://mlsanity.com/paper/2406.03103/) (2024)
- [Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs](https://mlsanity.com/paper/2412.12743/) (2024)
- [HieroGlyphTranslator: Automatic Recognition and Translation of Egyptian Hieroglyphs to English](https://mlsanity.com/paper/2512.03817/) (2025)
- [Supermarket Product Detection and Recognition: Utilizing Deep Learning with Rectified Imagery](https://mlsanity.com/paper/2610.08126/) (2026)

## Abstract (arXiv)

For more than half a century, the Hough transform is ever-expanding for new frontiers. Thousands of research papers and numerous applications have evolved over the decades. Carrying out an all-inclusive survey is hardly possible and enormously space-demanding. What we care about here is emphasizing some of the most crucial milestones of the transform. We describe its variations elaborating on the basic ones such as the line and circle Hough transforms. The high demand for storage and computation time is clarified with different solution approaches. Since most uses of the transform take place on binary images, we have been concerned with the work done directly on gray or color images. The myriad applications of the standard transform and its variations have been classified highlighting the up-to-date and the unconventional ones. Due to its merits such as noise-immunity and expandability, the transform has an excellent history, and a bright future as well.