53 citations · 141 across the 16 of their papers we have counts for
4 papers · 1 filter
A study of the classification of low-dimensional data with supervised manifold learning
Elif Vural, Christine Guillemot
Supervised manifold learning methods learn data representations by preserving the geometric structure of data while enhancing the separation between data samples from different cla…
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction
Julio Cesar Ferreira, Elif Vural, Christine Guillemot
Local learning of sparse image models has proven to be very effective to solve inverse problems in many computer vision applications. To learn such models, the data samples are oft…
Partial light field tomographic reconstruction from a fixed-camera focal stack
A. Mousnier, E. Vural, C. Guillemot
This paper describes a novel approach to partially reconstruct high-resolution 4D light fields from a stack of differently focused photographs taken with a fixed camera. First, a f…
Out-of-sample generalizations for supervised manifold learning for classification
Elif Vural, Christine Guillemot
Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving w…