Modelisation of Raindrops Based on Declivity Principle
Abstract
Performances of driver assistance systems depend on weather conditions: when the later deteriorate, for instance due to fog or rain, the performances deteriorate too. We hereby present a new approach of modelling raindrops on the windshield using the declivity principle applied in different plans. Our goal is to define a profile of raindrops by analyzing intensities behavior in different planes -- horizontal, vertical, left-diagonal, and right-diagonal -- based on the declivity principle's properties.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Due to their irregular shapes, closeness to image sensors, and long temporal presence in captured image frames, adherent raindrop detection and removal problem is relatively harder than that of rain streaks detection and removal.
“Due to their irregular shapes, closeness to image sensors, and long temporal presence in captured image frames, adherent raindrop detection and removal problem is relatively harder than that of rain streaks detection and removal [11,12].”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [11]
Fouad et al.'s declivity-based algorithm's main issue is that the assumption of the peak-valley appearance of raindrops does not always hold.
“The main issue with this algorithm is that the assumption of the peak-valley appearance of raindrops does not always hold.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [11]
Cites
Rainy weather recognition from in-vehicle camera images for driver assistance
Kurihata, H., Takahashi, T., Ide, I., Mekada, Y., Murase, H., Tamatsu, Y., & Miyahara, T. (2005) · IEEE Proceedings. Intelligent Vehicles Symposium, 2005. · doi:10.1109/ivs.2005.1505103 · Cited by 177 · see citing works
Video-based raindrop detection for improved image registration
Roser, M., & Geiger, A. (2009) · 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops · doi:10.1109/iccvw.2009.5457650 · Cited by 126 · see citing works
Towards night fog detection through use of in-vehicle multipurpose cameras
Gallen, R., Cord, A. M., Hautière, N., & Aubert, D. (2011) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2011.5940486 · Cited by 49 · see citing works
Fast and automatic stereo vision matching algorithm based on dynamic programming method
Bensrhair, A., Miché, P., & Debrie, R. (1996) · Pattern Recognition Letters · doi:10.1016/0167-8655(96)00004-9 · Cited by 47 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Review of Detection and Removal of Raindrops in Automotive Vision Systems
Hamzeh, Y., & Rawashdeh, S. A. (2021) · Journal of Imaging · doi:10.3390/jimaging7030052 · Cited by 21 · see citing works
“[11] Use a declivity operator to describe raindrops as a sequence of peaks and valleys.”
BackgroundMethodInfluential
- Authors
- Fouad, E., Salma, A., & Abdelhak, E.
- Venue
- 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 249-252
- OpenAlex
- W2393583748
- Cited by
- 4
- References
- 4
- Citations by year
- 2022: 1 · 2021: 1 · 2019: 1 · 2018: 1
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer Graphics, Imaging and Visualization (CGIV) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Keywords
- windshields, driver assistance systems, rainfall intensity, visibility impairment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
