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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
Occlusion-Aware Video Registration for Highly Non-Rigid Objects
Occlusion-Aware Video Registration for Highly Non-Rigid Objects
Bertram Taetz, Gabriele Bleser, Vladislav Golyanik, Didier Stricker
IEEE Winter Conference on Applications of Computer Vision (WACV-2016), March 7-9, Lake Placid, NY, USA
- Abstract:
- This paper addresses the problem of video registration for dense non-rigid structure from motion under suboptimal conditions, such as noise, self-occlusions, considerable external occlusions or specularities, i.e. the computation of optical flow between the reference image and each of the subsequent images in a video sequence when the camera observes a highly deformable object. We tackle this challenging task by improving previously proposed variational optimization techniques for multi-frame optical flow (MFOF) through detection, tracking and handling of uncertain flow field estimates. This is based on a novel Bayesian inference approach incorporated into the MFOF. At the same time, computational costs are significantly reduced through iterative pre-computation of the flow fields. As shown through experiments, the resulting method performs superior to other state-of-the-art (MF)OF methods on video sequences showing a highly non-rigidly deforming object with considerable occlusions.