57 citations · 155 across the 48 of their papers we have counts for
7 papers · 1 filter
Finsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Thomas Dagès, Simon Weber, Ya-Wei Eileen Lin +5
Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications…
Metric Convolutions: A Unifying Theory to Adaptive Image Convolutions
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Standard convolutions are prevalent in image processing and deep learning, but their fixed kernels limits adaptability. Several deformation strategies of the reference kernel grid…
Seeing Things in Random-Dot Videos
Thomas Dagès, Michael Lindenbaum, Alfred M. Bruckstein
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world. Human perception can effortlessly detect and correctly group featur…
Real-Time Depth Refinement for Specular Objects
Roy Or - El, Rom Hershkovitz, Aaron Wetzler +3
The introduction of consumer RGB-D scanners set off a major boost in 3D computer vision research. Yet, the precision of existing depth scanners is not accurate enough to recover fi…
Postprocessing of Compressed Images via Sequential Denoising
Yehuda Dar, Alfred M. Bruckstein, Michael Elad +1
In this work we propose a novel postprocessing technique for compression-artifact reduction. Our approach is based on posing this task as an inverse problem, with a regularization…
Depth Perception in Autostereograms: 1/f-Noise is Best
Yael Yankelevsky, Ishai Shvartz, Tamar Avraham +1
An autostereogram is a single image that encodes depth information that pops out when looking at it. The trick is achieved by replicating a vertical strip that sets a basic two-dim…