Search Results

Documents authored by Stricker, Didier

Document
DOI: 10.4230/OASIcs.VLUDS.2011.89
Texture-based Tracking in mm-wave Images

Authors: Peter Salz, Gerd Reis, and Didier Stricker

Published in: OASIcs, Volume 27, Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011

Abstract
Current tracking methods rely on color-, intensity-, and edge-based features to compute a description of an image region. These approaches are not well-suited for low-quality images such as mm-wave data from full-body scanners. In order to perform tracking in such challenging grayscale images, we propose several enhancements and extensions to the Visual Tracking Decomposition (VTD) by Kwon and Lee. A novel region descriptor, which uses texture-based features, is presented and integrated into VTD. We improve VTD by adding a sophisticated weighting scheme for observations, better motion models, and a more realistic way for sampling and interaction. Our method not only outperforms VTD on mm-wave data but also has comparable results on normal-quality images. We are confident that our region descriptor can easily be extended to other kinds of features and applications such that tracking can be performed in a large variety of image data, especially low-resolution, low-illumination and noisy images.
Cite as

Peter Salz, Gerd Reis, and Didier Stricker. Texture-based Tracking in mm-wave Images. In Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011. Open Access Series in Informatics (OASIcs), Volume 27, pp. 89-101, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

Copy BibTex To Clipboard

@InProceedings{salz_et_al:OASIcs.VLUDS.2011.89,
  author =	{Salz, Peter and Reis, Gerd and Stricker, Didier},
  title =	{{Texture-based Tracking in mm-wave Images}},
  booktitle =	{Visualization of Large and Unstructured Data Sets: Applications in Geospatial Planning, Modeling and Engineering - Proceedings of IRTG 1131 Workshop 2011},
  pages =	{89--101},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-46-0},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{27},
  editor =	{Garth, Christoph and Middel, Ariane and Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2011.89},
  URN =		{urn:nbn:de:0030-drops-37437},
  doi =		{10.4230/OASIcs.VLUDS.2011.89},
  annote =	{Keywords: Visual Tracking decomposition, low-quality images, texture features, mm-wave imagery}
}
Document
DOI: 10.4230/OASIcs.VLUDS.2010.36
Detection and Identification Techniques for Markers Used in Computer Vision

Authors: Johannes Köhler, Alain Pagani, and Didier Stricker

Published in: OASIcs, Volume 19, Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop) (2011)

Abstract
This paper summarizes and compares techniques for detecting and identifying markers in the context of computer vision. Existing approaches either use correlation, digital or topological methods for marker identification. The comparison points out, that all marker processing algorithms which employ sophisticated digital codes perform more robust and reliable. Existing bit representation schemes for these codes and marker designs are compared with each other. In the overall context it is illustrated, why the marker processing algorithm is the best performer regarding marker occlusion and minimal detectable pattern size.
Cite as

Johannes Köhler, Alain Pagani, and Didier Stricker. Detection and Identification Techniques for Markers Used in Computer Vision. In Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop). Open Access Series in Informatics (OASIcs), Volume 19, pp. 36-44, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

Copy BibTex To Clipboard

@InProceedings{kohler_et_al:OASIcs.VLUDS.2010.36,
  author =	{K\"{o}hler, Johannes and Pagani, Alain and Stricker, Didier},
  title =	{{Detection and Identification Techniques for Markers Used in Computer Vision}},
  booktitle =	{Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)},
  pages =	{36--44},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-29-3},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{19},
  editor =	{Middel, Ariane and Scheler, Inga and Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2010.36},
  URN =		{urn:nbn:de:0030-drops-30951},
  doi =		{10.4230/OASIcs.VLUDS.2010.36},
  annote =	{Keywords: Marker Identification, Computer Vision}
}
Document
DOI: 10.4230/OASIcs.VLUDS.2010.45
Markerless Camera Pose Estimation - An Overview

Authors: Tobias Nöll, Alain Pagani, and Didier Stricker

Published in: OASIcs, Volume 19, Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop) (2011)

Abstract
As shown by the human perception, a correct interpretation of a 3D scene on the basis of a 2D image is possible without markers. Solely by identifying natural features of different objects, their locations and orientations on the image can be identified. This allows a three dimensional interpretation of a two dimensional pictured scene. The key aspect for this interpretation is the correct estimation of the camera pose, i.e. the knowledge of the orientation and location a picture was recorded. This paper is intended to provide an overview of the usual camera pose estimation pipeline as well as to present and discuss the several classes of pose estimation algorithms.
Cite as

Tobias Nöll, Alain Pagani, and Didier Stricker. Markerless Camera Pose Estimation - An Overview. In Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop). Open Access Series in Informatics (OASIcs), Volume 19, pp. 45-54, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

Copy BibTex To Clipboard

@InProceedings{noll_et_al:OASIcs.VLUDS.2010.45,
  author =	{N\"{o}ll, Tobias and Pagani, Alain and Stricker, Didier},
  title =	{{Markerless Camera Pose Estimation - An Overview}},
  booktitle =	{Visualization of Large and Unstructured Data Sets - Applications in Geospatial Planning, Modeling and Engineering (IRTG 1131 Workshop)},
  pages =	{45--54},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-29-3},
  ISSN =	{2190-6807},
  year =	{2011},
  volume =	{19},
  editor =	{Middel, Ariane and Scheler, Inga and Hagen, Hans},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.VLUDS.2010.45},
  URN =		{urn:nbn:de:0030-drops-30960},
  doi =		{10.4230/OASIcs.VLUDS.2010.45},
  annote =	{Keywords: Pose Estimation}
}
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact