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Author
Saad Minhas; Zeba Khanam; Shoaib Ehsan; Klaus McDonald Maier; Aura Hernandez-Sabate
Title
Weather Classification by Utilizing Synthetic Data
Type
Journal Article
Year
2022
Publication
Sensors
Abbreviated Journal
SENS
Volume
22
Issue
9
Pages
3193
Keywords
Weather classification; synthetic data; dataset; autonomous car; computer vision; advanced driver assistance systems; deep learning; intelligent transportation systems
Abstract
Weather prediction from real-world images can be termed a complex task when targeting classification using neural networks. Moreover, the number of images throughout the available datasets can contain a huge amount of variance when comparing locations with the weather those images are representing. In this article, the capabilities of a custom built driver simulator are explored specifically to simulate a wide range of weather conditions. Moreover, the performance of a new synthetic dataset generated by the above simulator is also assessed. The results indicate that the use of synthetic datasets in conjunction with real-world datasets can increase the training efficiency of the CNNs by as much as 74%. The article paves a way forward to tackle the persistent problem of bias in vision-based datasets.
Address
21 April 2022
Corporate Author
Thesis
Publisher
MDPI
Place of Publication
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
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Conference
Notes
IAM; 600.139; 600.159; 600.166; 600.145;
Approved
no
Call Number
Admin @ si @ MKE2022
Serial
3761
Permanent link to this record
Author
Patricia Marquez; Debora Gil; Aura Hernandez-Sabate
Title
A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth
Type
Conference Article
Year
2011
Publication
IEEE International Conference on Computer Vision – Workshops
Abbreviated Journal
Volume
Issue
Pages
2042-2049
Keywords
IEEE International Conference on Computer Vision – Workshops
Abstract
Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems.
Address
Corporate Author
Thesis
Publisher
IEEE
Place of Publication
Barcelona (Spain)
Editor
Language
English
Summary Language
English
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
Medium
Area
Expedition
Conference
ICCVW
Notes
IAM; ADAS
Approved
no
Call Number
IAM @ iam @ MGH2011
Serial
1682
Permanent link to this record
Author
Aura Hernandez-Sabate; Debora Gil
Title
The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries
Type
Book Chapter
Year
2012
Publication
Intravascular Ultrasound
Abbreviated Journal
Volume
Issue
Pages
185-206
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
Intech
Place of Publication
Editor
Yasuhiro Honda
Language
English
Summary Language
english
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
ISBN
978-953-307-900-4
Medium
Area
Expedition
Conference
Notes
IAM; ADAS
Approved
no
Call Number
IAM @ iam @ HeG2012
Serial
1684
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