Video-based water drop detection and removal method for a moving vehicle
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?
Liao et al.'s overall processing time was between 732 and 972 ms, which might be too long for real-time image-based automotive applications.
“The overall processing time was between 732 and 972 ms which might be too long for real-time image-based automotive applications.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Liao et al.'s technique relied on on-road lane markings and complex building arrangements for image segmentation and raindrop detection; it is not clear how the process would perform with the absence of road marks or lack of buildings.
“Another drawback of the proposed technique is that it relied on on-road lane markings and complex building arrangements for image segmentation and raindrop detection. It is not clear how the process would perform with the absence of road marks or lack of buildings.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Liao et al.'s test results showed acceptable PSNR (above 30 dB) on frames with light concentrations of raindrops.
“Test results showed acceptable Peak signal-to-noise ratio (PSNR) on frames with light concentrations of raindrops showed acceptable PSNR (above 30 dB).”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Liao et al.'s removal phase time seemed to increase rapidly with increased raindrop concentration.
“The detection rate was not affected by raindrop concentration but the removal phase time seemed to increase rapidly with the increased raindrop concentration.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Liao et al.'s process assumed a straight driving scenario, and it was difficult to identify raindrop patches when the vehicle was turning.
“In addition, the process assumed a straight driving scenario, and Liao et al. observed that it was difficult to identify raindrop patches when the vehicle was turning.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Liao et al.'s detection rate was not affected by raindrop concentration.
“The detection rate was not affected by raindrop concentration but the removal phase time seemed to increase rapidly with the increased raindrop concentration.”
From A Review of Detection and Removal of Raindrops in Automotive Vision Systems (2021) · cites this paper as [36]
Cites
Use of the hough transformation to detect lines and curves in pictures
Duda, R. O., & Hart, P. E. (1972) · Communications of the ACM · doi:10.1145/361237.361242 · Cited by 6,633 · see citing works
Region Filling and Object Removal by Exemplar-Based Image Inpainting
Criminisi, A., Perez, P., & Toyama, K. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.833105 · Cited by 3,258 · see citing works
Adaptive Thresholding using the Integral Image
Bradley, D., & Roth, G. (2007) · Journal of Graphics Tools · doi:10.1080/2151237x.2007.10129236 · Cited by 1,423 · see citing works
Simultaneous structure and texture image inpainting
Bertalmı́o, M., Vese, L. A., Sapiro, G., & Osher, S. (2003) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.815261 · Cited by 1,083 · see citing works
Rain Removal in Video by Combining Temporal and Chromatic Properties
Zhang, X., Li, H., Qi, Y., Leow, W. K., & Ng, T. (2006) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2006.262572 · Cited by 298 · see citing works
Garg, K., & Nayar, S. K. (2005) · doi:10.1109/iccv.2005.253 · Cited by 235 · see citing works
Moving Vehicle Detection for Automatic Traffic Monitoring
Zhou, J., Gao, D., & Zhang, D. D. (2007) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2006.883735 · Cited by 232 · see citing works
Chan, T. F.-C., & Shen, J. (2005) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20075 · Cited by 157 · see citing works
A High-Performance Sum of Absolute Difference Implementation for Motion Estimation
Vanne, J., Aho, E., Hämäläinen, T. D., et al. (2006) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2006.877150 · Cited by 129 · 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
Color balancing of digital photos using simple image statistics
Gasparini, F., & Schettini, R. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2003.12.007 · Cited by 114 · see citing works
Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing
Chang, B. R., Tsai, H. F., & Young, C.-P. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2009.07.036 · Cited by 81 · see citing works
Halimeh, J. C., & Roser, M. (2009) · 2009 IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2009.5164347 · Cited by 76 · see citing works
Hautière, N., Aubert, D., Dumont, É., et al. (2008) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2008.922096 · Cited by 27 · see citing works
Visibility Estimation in Foggy Conditions by In-Vehicle Camera and Radar
Mori, K., Kato, T., Takahashi, T., et al. (2006) · doi:10.1109/icicic.2006.373 · Cited by 26 · see citing works
Removal of adherent noises from image sequences by spatio-temporal image processing
Yamashita, A., Fukuchi, I., Kaneko, T., & Miura, K. T. (2008) · 2008 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2008.4543570 · Cited by 21 · see citing works
Virtual wiper — removal of adherent noises from images of dynamic scenes by using a pan–tilt camera
Yamashita, A., Harada, T., Kaneko, T., et al. (2005) · Advanced Robotics · doi:10.1163/1568553053583643 · Cited by 16 · see citing works
IVS 09: Future Research in Vehicle Vision Systems
Li, Z., Li, L., & Zhang, Y. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.119 · Cited by 14 · see citing works
Tanaka, Y., Yamashita, A., Kaneko, T., et al. (2005) · Cited by 3 · see citing works
Street crossing pedestrian detection based on edge curves motion
Mazoul, A., Zebbara, K., & Ansari, M. E. (2012) · International Journal of Computer Applications · doi:10.5120/5624-7927 · Cited by 3 · 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
“[36] For buildings ROI, replace the raindrop area with the closest non-rain area (using an 8connected area template).”
BackgroundMethodInfluentialDetection of Raindrop with Various Shapes on aWindshield
Ishizuka, J., & Onoguchi, K. (2016) · Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0005796004750483 · Cited by 5 · see citing works
“Liao et al. (Liao et al., 2013) detected raindrops on a windshield based on three characteristics that a raindrop exists in the video frames for a period of time, its shape is close to an ellipse and it is bright.”
Background
- Authors
- Liao, H.-C., Wang, D.-Y., Yang, C.-L., & Shin, J.
- ORCID
- Shin, Jungpil
- Venue
- Information Technology Journal
- Publisher
- Science Alert
- Published
- 2013-04-01
- Volume
- 12
- Issue
- 4
- Pages
- 569-583
- ISSN
- 1812-5638 (ISSN-L)
- OpenAlex
- W2321302368
- Cited by
- 9
- References
- 20
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 2 · 2017: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Technology Journal (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electrowetting and Microfluidic Technologies
- Keywords
- moving vehicles, video processing, rain detection
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
