1 code implementation • 8 Mar 2024 • Xavier Bou, Gabriele Facciolo, Rafael Grompone von Gioi, Jean-Michel Morel, Thibaud Ehret
Moreover, we study the performance of both visual and image-text features, namely DINOv2 and CLIP, including two CLIP models specifically tailored for remote sensing applications.
no code implementations • 6 Mar 2024 • Xavier Bou, Thibaud Ehret, Rafael Grompone von Gioi, Jeremy Anger
Identifying flood affected areas in remote sensing data is a critical problem in earth observation to analyze flood impact and drive responses.
no code implementations • 9 Jul 2023 • Xavier Bou, Aitor Artola, Thibaud Ehret, Gabriele Facciolo, Jean-Michel Morel, Rafael Grompone von Gioi
Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.
no code implementations • 13 Dec 2021 • Rafael Grompone von Gioi, Ignacio Ramírez Paulino, Gregory Randall
This work explores the connections between the Minimum Description Length (MDL) principle as developed by Rissanen, and the a-contrario framework for structure detection proposed by Desolneux, Moisan and Morel.
no code implementations • 25 May 2018 • José Lezama, Samy Blusseau, Jean-Michel Morel, Gregory Randall, Rafael Grompone von Gioi
Using a computational quantitative version of the non-accidentalness principle, we raise the possibility that the psychophysical and the (older) gestaltist setups, both applicable on dot or Gabor patterns, find a useful complement in a Turing test.
no code implementations • 18 Mar 2016 • Boshra Rajaei, Rafael Grompone von Gioi, Jean-Michel Morel
In this paper, we reconsider the early computer vision bottom-up program, according to which higher level features (geometric structures) in an image could be built up recursively from elementary features by simple grouping principles coming from Gestalt theory.
no code implementations • CVPR 2014 • Jose Lezama, Rafael Grompone von Gioi, Gregory Randall, Jean-Michel Morel
We present a novel method for automatic vanishing point detection based on primal and dual point alignment detection.