loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Alexander Bachmann and Irina Lulcheva

Affiliation: University of Karlsruhe (TH), Germany

ISBN: 978-989-8111-69-2

Keyword(s): Stereo vision, Motion segmentation, Markov Random fields, Object classification, Global image context.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Detecting 3D Objects Using Patterns of Motion and Appearance ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Motion, Tracking and Stereo Vision ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Segment Cluster Tracking ; Sensor Networks ; Soft Computing

Abstract: In this paper we address the problem of detecting objects form a moving camera by jointly considering lowlevel image features and high-level object information. The proposed method partitions an image sequence into independently moving regions with similar 3-dimensional (3D) motion and distance to the observer. In the recognition stage category-specific information is integrated into the partitioning process. An object category is represented by a set of descriptors expressing the local appearance of salient object parts. To account for the geometric relationships among object parts a structural prior over part configurations is designed. This prior structure expresses the spatial dependencies of object parts observed in a training data set. To achieve global consistency in the recognition process, information about the scene is extracted from the entire image based on a set of global image features. These features are used to predict the scene context of the image from which characte ristic spatial distributions and properties of an object category are derived. The scene context helps to resolve local ambiguities and achieves locally and globally consistent image segmentation. Our expectations on spatial continuity of objects are expressed in a Markov Random Field (MRF) model. Segmentation results are presented based on real image sequences. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.87.61.215

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bachmann A.; Lulcheva I. and (2009). BAYESIAN SCENE SEGMENTATION INCORPORATING MOTION CONSTRAINTS AND CATEGORY-SPECIFIC INFORMATION.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 291-298. DOI: 10.5220/0001653302910298

@conference{visapp09,
author={Alexander Bachmann and Irina Lulcheva},
title={BAYESIAN SCENE SEGMENTATION INCORPORATING MOTION CONSTRAINTS AND CATEGORY-SPECIFIC INFORMATION},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={291-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001653302910298},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - BAYESIAN SCENE SEGMENTATION INCORPORATING MOTION CONSTRAINTS AND CATEGORY-SPECIFIC INFORMATION
SN - 978-989-8111-69-2
AU - Bachmann, A.
AU - Lulcheva, I.
PY - 2009
SP - 291
EP - 298
DO - 10.5220/0001653302910298

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.