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Jean-Philippe Thiran

Researcher at École Polytechnique Fédérale de Lausanne

Publications -  43
Citations -  1899

Jean-Philippe Thiran is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Convolutional neural network & Normalization (image processing). The author has an hindex of 10, co-authored 43 publications receiving 1115 citations. Previous affiliations of Jean-Philippe Thiran include University of Lausanne.

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Journal ArticleDOI

The challenge of mapping the human connectome based on diffusion tractography

Klaus H. Maier-Hein, +76 more
TL;DR: The encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent) is reported, however, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups.
Proceedings ArticleDOI

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

TL;DR: This work presents a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms, and is the first publicly available dataset with comprehensive annotations to address FoUn task.
Proceedings ArticleDOI

SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

TL;DR: In this paper, the authors optimize a deep network-based decoder with a targeted objective function that penalizes images at different semantic levels using the corresponding terms, which results in more realistic textures and sharper edges.
Posted ContentDOI

Tractography-based connectomes are dominated by false-positive connections

Klaus H. Maier-Hein, +76 more
- 07 Nov 2016 - 
TL;DR: The results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
Proceedings ArticleDOI

Combining LiDAR space clustering and convolutional neural networks for pedestrian detection

TL;DR: In this article, LiDAR data is utilized to generate region proposals by processing the three dimensional point cloud that it provides and these candidate regions are then further processed by a state-of-the-art CNN classifier that has been fine-tuned for pedestrian detection.