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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
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Summary Language
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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
Error Analysis for Lucas-Kanade Based Schemes
Type
Conference Article
Year
2012
Publication
9th International Conference on Image Analysis and Recognition
Abbreviated Journal
Volume
7324
Issue
I
Pages
184-191
Keywords
Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
Abstract
Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Address
Aveiro, Portugal
Corporate Author
Thesis
Publisher
Springer-Verlag Berlin Heidelberg
Place of Publication
Editor
Language
english
Summary Language
Original Title
Series Editor
Campilho, Aurélio and Kamel, Mohamed
Series Title
Lecture Notes in Computer Science
Abbreviated Series Title
LNCS
Series Volume
Series Issue
Edition
ISSN
0302-9743
ISBN
978-3-642-31294-6
Medium
Area
Expedition
Conference
ICIAR
Notes
IAM
Approved
no
Call Number
IAM @ iam @ MGH2012a
Serial
1899
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