Carlos Becker
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Carlos Becker has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Pascal Fua
École Polytechnique Fédérale de Lausanne (EPFL)
1 shared publication - Roberto Rigamonti
School of Engineering and Management Vaud, HES-SO University of Applied Sciences and Arts Western Switzerland, Yverdon-les-Bains, Vaud, Switzerland
1 shared publication Vincent Lepetit
CVLab, École Polytechnique Fédérale de Lausanne, Switzerland
1 shared publication
Latest Publications
- 1 January 2013
- Published by Springer Nature
- Vol. 16, 526-533
Supervised Feature Learning for Curvilinear Structure Segmentation
We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for hand-designed features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-the-art curvilinear segmentation methods on both 2D images and 3D image stacks.
- 1 January 2013
- Published by Springer Nature
- Vol. 16, 526-533
Supervised Feature Learning for Curvilinear Structure Segmentation
We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for hand-designed features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-the-art curvilinear segmentation methods on both 2D images and 3D image stacks.