Prof. Dr. Didier Stricker
Dr. Alain Pagani
Dr. Gerd Reis
Eric Thil
Keonna Cunningham
Dr. Oliver Wasenmüller
Dr. Gabriele Bleser
Dr. Bruno Mirbach
Dr. Jason Raphael Rambach
Dr. Bertram Taetz
Dr. Muhammad Zeshan Afzal
Sk Aziz Ali
Mhd Rashed Al Koutayni
Murad Almadani
Alaa Alshubbak
Yuriy Anisimov
Jilliam Maria Diaz Barros
Ramy Battrawy
Hammad Butt
Mahdi Chamseddine
Steve Dias da Cruz
Fangwen Shu
Torben Fetzer
Ahmet Firintepe
Sophie Folawiyo
David Michael Fürst
Kamalveerkaur Garewal
Christiano Couto Gava
Leif Eric Goebel
Tewodros Amberbir Habtegebrial
Simon Häring
Khurram Hashmi
Jigyasa Singh Katrolia
Andreas Kölsch
Onorina Kovalenko
Stephan Krauß
Paul Lesur
Muhammad Jameel Nawaz Malik
Michael Lorenz
Markus Miezal
Mina Ameli
Nareg Minaskan Karabid
Mohammad Minouei
Pramod Murthy
Mathias Musahl
Peter Neigel
Manthan Pancholi
Qinzhuan Qian
Engr. Kumail Raza
Dr. Nadia Robertini
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar
Alexander Schäfer
Pascal Schneider
René Schuster
Mohamed Selim
Lukas Stefan Staecker
Dennis Stumpf
Yongzhi Su
Xiaoying Tan
Yaxu Xie
Dr. Vladislav Golyanik
Dr. Aditya Tewari
André Luiz Brandão
Representing Feature Location Uncertainties in Spherical Images
Representing Feature Location Uncertainties in Spherical Images
International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 21st, June 24-27, Plzen, Czech Republic
- Abstract:
- Pose uncertainty estimation of calibrated cameras is a common task in the field of computer vision and uses location uncertainties of image features. For spherical cameras, those uncertainties cannot be optimally described using conventional latitude-longitude representation. Increasing distortions close to the poles of the spherical coordinate system prevent a suitable description through Gaussians. To overcome this limitation, we present a consistent location uncertainty representation for spherical image features: Our approach is based on normal vectors in Cartesian space and applicable to any kind of camera with convex projection surfaces, such as catadioptric and spherical systems. We compare its performance against latitude-longitude representation by estimating camera pose uncertainties through first order error propagation in a weighted least squares pose estimation scenario. Our experiments on synthetic and real data show that the proposed approach delivers consistent results outperforming conventional latitude-longitude representation.