2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops 2014
DOI: 10.1109/cvprw.2014.105
|View full text |Cite
|
Sign up to set email alerts
|

Exploiting Traffic Scene Disparity Statistics for Stereo Vision

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2015
2015
2019
2019

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 24 publications
0
2
0
Order By: Relevance
“…For image and video processing, FPGAs have boosted different applications allowing real-time processing for stereo vision systems [661,667,[1617][1618][1619], face detection/recognition [675,677,679], object tracking [732,1620,1621] and digital watermarking for intellectual property protection [706,1622,1623]. These kind of applications have been used for different systems such as the Mars rovers computer vision [669,670], autonomous vehicles vision [1624][1625][1626][1627][1628][1629], atmospheric turbulence mitigation for telescope pictures reconstruction [723,724], among others. In addition, we found image and video compression standards implemented in FPGAs, from JPEG [1630], to most recent video compression standard H.265 [593].…”
Section: Discussionmentioning
confidence: 99%
“…For image and video processing, FPGAs have boosted different applications allowing real-time processing for stereo vision systems [661,667,[1617][1618][1619], face detection/recognition [675,677,679], object tracking [732,1620,1621] and digital watermarking for intellectual property protection [706,1622,1623]. These kind of applications have been used for different systems such as the Mars rovers computer vision [669,670], autonomous vehicles vision [1624][1625][1626][1627][1628][1629], atmospheric turbulence mitigation for telescope pictures reconstruction [723,724], among others. In addition, we found image and video compression standards implemented in FPGAs, from JPEG [1630], to most recent video compression standard H.265 [593].…”
Section: Discussionmentioning
confidence: 99%
“…Alternatively, objects can be classified using clustering in the disparity map, as seen in [28]. In [32], [33], temporal and scene priors from good conditions are used with the purpose of improving the disparity map in adverse weather conditions, such as night, rain, and snow. Using these priors, the object detection rate improves on a database of 3000 frames including bad weather while reducing the false positive rate.…”
Section: Introductionmentioning
confidence: 99%