Efficient Non-Maximum Suppression
Abstract
In this work we scrutinize a low level computer vision task - non-maximum suppression (NMS) - which is a crucial preprocessing step in many computer vision applications. Especially in real time scenarios, efficient algorithms for such preprocessing algorithms, which operate on the full image resolution, are important. In the case of NMS, it seems that merely the straightforward implementation or slight improvements are known. We show that these are far from being optimal, and derive several algorithms ranging from easy-to-implement to highly-efficient
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A technique inspired by non-maximum suppression (NMS) was developed to refine spatial probabilistic maps, enhancing decomposition performance by reducing ambiguity in complex spatial scenarios and focusing predictions on the most confident cell-type assignments.
“We developed a technique inspired by non-maximum suppression (NMS)58 to refine spatial probabilistic maps, enhancing decomposition performance by reducing ambiguity in complex spatial scenarios and focusing predictions on the most confident cell-type assignments.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [58]
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“Two-stage detectors usually work by first identifying regions of interest or possible bounding boxes and then running a classifier on those regions with sometimes a post-processing step for filtering out duplicated detections [16].”
Background
- Authors
- Neubeck, A., & Van Gool, L.
- Affiliations
- ETH Zurich (CH)
- Venue
- 18th International Conference on Pattern Recognition (ICPR'06)
- Publisher
- IEEE
- Published
- 2006
- Pages
- 850-855
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W2144506857
- Cited by
- 2,118
- References
- 2
- Citations by year
- 2026: 127 · 2025: 233 · 2024: 295 · 2023: 294 · 2022: 311 · 2021: 275 · 2020: 203 · 2019: 133 · 2018: 66 · 2017: 43 · 2016: 28 · 2015: 22 · 2014: 28 · 2013: 15 · 2012: 14
- FWCI
- 3.68
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Object Detection Techniques
- Keywords
- non-maximum suppression, real-time image processing, preprocessing algorithms, algorithm efficiency
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