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Author
Debora Gil; Petia Radeva
Title
Inhibition of false landmarks
Type
Journal Article
Year
2006
Publication
Pattern Recognition Letters
Abbreviated Journal
PRL
Volume
27
Issue
9
Pages
1022-1030
Keywords
Abstract
Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.
Address
Corporate Author
Thesis
Publisher
Elsevier Science Inc.
Place of Publication
New York, NY, USA
Editor
Language
Summary Language
Original Title
Series Editor
Series Title
Abbreviated Series Title
Series Volume
Series Issue
Edition
ISSN
0167-8655
ISBN
Medium
Area
Expedition
Conference
Notes
IAM;MILAB
Approved
no
Call Number
IAM @ iam @ GiR2006
Serial
1529
Permanent link to this record
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
Expedition
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
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil
Title
EP01.05-001 Radiomics to Increase the Effectiveness of Lung Cancer Screening Programs. Radiolung Preliminary Results
Type
Journal Article
Year
2022
Publication
Journal of Thoracic Oncology
Abbreviated Journal
JTO
Volume
17
Issue
9
Pages
S182
Keywords
Abstract
Address
Corporate Author
Thesis
Publisher
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
Expedition
Conference
Notes
IAM
Approved
no
Call Number
Admin @ si @ RBG2022b
Serial
3834
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