Real-time image-based tracking of planes using efficient second-order minimization
By Selim Benhimane, Ezio Malis. IEEE/RJS International Conference on Intelligent RObots and Systems, 2004
369 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/18b8d0c44adc2e347d5da450bb56d033205dea05
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Daniel Cremers
- Hideaki Uchiyama
- Ming-Ming Cheng
- Tom Kamiel Magda Vercauteren
- Nicholas Ayache
- Ming-Hsuan Yang
- Luc Van Gool
- Jakob J. Engel
Citations in the corpus
- GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence (2017, methodology): “We evaluate GMS on two metrics: a) Its recall, precision and F-measure, F = 2 · (Precision ·Recall)/(Precision+Recall), relative to the ratio-test; b) GMS’s matchings effectiveness in improving performance of pose estimation used in SLAM [3] and Visual Structure from Motion [39].”
- LSD-SLAM: Large-Scale Direct Monocular SLAM (2014, result): “Still, we found that in practice the convergence radius is sufficiently large even for large-scale loop closures - in particular we found that the convergence radius can be substantially increased by the following measures: – Efficient Second Order Minimization (ESM) [3]: While our results confirm previo”
- Pose Estimation for Augmented Reality: A Hands-On Survey (2016, methodology): “We propose as a supplementary material of this paper (here) an example of how to use such tracker.”
- Diffeomorphic demons: Efficient non-parametric image registration (2009, methodology): “One of the main results of this theoretical analysis we initially presented in (Vercauteren et al., 2007a) is to show that the symmetric forces variant can be related to the ESM scheme of (Benhimane and Malis, 2004; Malis, 2004).”
- Homography-based 2D Visual Tracking and Servoing (2007, methodology): “We used the ESM1 visual tracking algorithm (Benhimane and Malis 2004) to track the area of interest and at the same time to estimate the homography matrix H.”
- Evaluation of Interest Point Detectors and Feature Descriptors for Visual Tracking (2011, background): “5), as well as non-feature-based solutions such as alignmentbased techniques (Benhimane and Malis 2004; Adams et al. 2008).”