Publications

Book Chapters & Books top

2019
Bellot P, Salembier P, Pham NC, Meyer PE. Unsupervised GRN Ensemble. In Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology . New York, NY: Springer science, Humana Press; 2019. pp. 283-302.
2018
Casamitjana A, Vilaplana V, Petrone P, Molinuevo JLuis, Gispert JD. Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer’s Disease. In PRedictive Intelligence in MEdicine. Springer International Publishing; 2018. pp. 60-67.
Górriz M, Aparicio A, Raventós B, Vilaplana V, Sayrol E, López-Codina D. Leishmaniasis Parasite Segmentation and Classification Using Deep Learning. In Articulated Motion and Deformable Objects. Springer International Publishing; 2018. pp. 53-62.
Combalia M, Vilaplana V. Monte-Carlo Sampling Applied to Multiple Instance Learning for Histological Image Classification. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing; 2018. pp. 274-281.
Campos V, Giró-i-Nieto X, Jou B, Torres J, Chang S-F. Sentiment concept embedding for visual affect recognition. In Multimodal Behavior Analysis in theWild. 1st ed. Elsevier; 2018.

Conference Papers top

Theses top

2019
Salvador A. Computer Vision beyond the visible: Image understanding through language. Giró-i-Nieto X, Marqués F. Signal Theory and Communications. [Barcelona]: Universitat Politecnica de Catalunya; 2019.
2018
Lin X. Semantic and Generic Object Segmentation for Scene Analysis Using RGB-D Data. Casas J, Pardàs M. Signal Theory and Communications (TSC). [download link]: Universitat Politècnica de Catalunya (UPC); 2018.
2017
Maceira M. Multi-view depth coding based on a region representation combining color and depth information. Ruiz-Hidalgo J, Morros JR. Signal Theory and Communications (TSC). Universitat Politècnica de Catalunya (UPC); 2017.
Bellot P. Study of Gene Regulatory Networks Inference Methods from Gene Expression Data. Salembier P. 2017.  (2.73 MB)
Perez-Pellitero E. Manifold Learning for Super Resolution. Rosenhahn B, Ruiz-Hidalgo J. [Hannover]: Leibniz Universität Hannover; 2017.  (18.6 MB)