Search

Your search keyword '"Maldague, Xavier"' showing total 952 results
Start Over Author "Maldague, Xavier"
952 results on '"Maldague, Xavier"'

4. Crédits

Author

Maldague, Xavier

Published
2023

5. Lexique

Author

Maldague, Xavier

Published
2023

12. Index

Author

Maldague, Xavier

Published
2023

20. Multispectral Non-invasive Inspection of Marqueteries Coupled with Finite Element Analyses

Author

Ding, Yinuo, Zhang, Hai, Sfarra, Stefano, Pivarčiová, Elena, Maldague, Xavier P. V., Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Osman, Ahmad, editor, Moropoulou, Antonia, editor, and Lampropoulos, Kyriakos, editor

Published
2024
Full Text
View/download PDF

31. Embedded Deep Regularized Block HSIC Thermomics for Early Diagnosis of Breast Cancer

Author

Yousefi, Bardia, Sharifipour, Hossein Memarzadeh, and Maldague, Xavier P. V.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Thermography has been used extensively as a complementary diagnostic tool in breast cancer detection. Among thermographic methods matrix factorization (MF) techniques show an unequivocal capability to detect thermal patterns corresponding to vasodilation in cancer cases. One of the biggest challenges in such techniques is selecting the best representation of the thermal basis. In this study, an embedding method is proposed to address this problem and Deep-semi-nonnegative matrix factorization (Deep-SemiNMF) for thermography is introduced, then tested for 208 breast cancer screening cases. First, we apply Deep-SemiNMF to infrared images to extract low-rank thermal representations for each case. Then, we embed low-rank bases to obtain one basis for each patient. After that, we extract 300 thermal imaging features, called thermomics, to decode imaging information for the automatic diagnostic model. We reduced the dimensionality of thermomics by spanning them onto Hilbert space using RBF kernel and select the three most efficient features using the block Hilbert Schmidt Independence Criterion Lasso (block HSIC Lasso). The preserved thermal heterogeneity successfully classified asymptomatic versus symptomatic patients applying a random forest model (cross-validated accuracy of 71.36% (69.42%-73.3%))., Comment: Authors version. arXiv admin note: text overlap with arXiv:2010.06784

Published
2021
Full Text
View/download PDF

36. Low-rank Convex/Sparse Thermal Matrix Approximation for Infrared-based Diagnostic System

Author

Yousefi, Bardia, Castanedo, Clemente Ibarra, and Maldague, Xavier P. V.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Active and passive thermography are two efficient techniques extensively used to measure heterogeneous thermal patterns leading to subsurface defects for diagnostic evaluations. This study conducts a comparative analysis on low-rank matrix approximation methods in thermography with applications of semi-, convex-, and sparse- non-negative matrix factorization (NMF) methods for detecting subsurface thermal patterns. These methods inherit the advantages of principal component thermography (PCT) and sparse PCT, whereas tackle negative bases in sparse PCT with non-negative constraints, and exhibit clustering property in processing data. The practicality and efficiency of these methods are demonstrated by the experimental results for subsurface defect detection in three specimens (for different depth and size defects) and preserving thermal heterogeneity for distinguishing breast abnormality in breast cancer screening dataset (accuracy of 74.1%, 75.8%, and 77.8%)., Comment: Authors version

Published
2020
Full Text
View/download PDF

37. Skeletonization and Reconstruction based on Graph Morphological Transformations

Author

Sharifipour, Hossein Memarzadeh, Yousefi, Bardia, and Maldague, Xavier P. V.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Multiscale shape skeletonization on pixel adjacency graphs is an advanced intriguing research subject in the field of image processing, computer vision and data mining. The previous works in this area almost focused on the graph vertices. We proposed novel structured based graph morphological transformations based on edges opposite to the current node based transformations and used them for deploying skeletonization and reconstruction of infrared thermal images represented by graphs. The advantage of this method is that many widely used path based approaches become available within this definition of morphological operations. For instance, we use distance maps and image foresting transform (IFT) as two main path based methods are utilized for computing the skeleton of an image. Moreover, In addition, the open question proposed by Maragos et al (2013) about connectivity of graph skeletonization method are discussed and shown to be quite difficult to decide in general case.

Published
2020

44. Infrared, Terahertz and Air-Coupled Ultrasonic Non-invasive Inspection for Artworks: A Comparative Study on an Old Hand-Bound Book of the XIXth Century

Author

Lei, Qinqian, Zhang, Hai, Sfarra, Stefano, Osman, Ahmad, Ibarra-Castanedo, Clemente, Maldague, Xavier P. V., Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Osman, Ahmad, editor, and Moropoulou, Antonia, editor

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results