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Knowledge Bases for Visual Dynamic Scene Understanding

Topics: Active and Robot Vision; Cognitive Models for Interpretation, Integration and Control; Device Calibration, Characterization and Modeling; Image-Based Modeling and 3D Reconstruction; Mobile Imaging; Object Detection and Localization; Shape Representation and Matching; Tracking and Visual Navigation; Vision for Robotics

Author: Ernst D. Dickmanns

Affiliation: University of the Bundeswehr and Department of Aero-Space Technology (LRT), Germany

ISBN: 978-989-758-089-5

Keyword(s): Knowledge Representation, Real-time Machine Vision, Behavior Decision, Scene Understanding.

Related Ontology Subjects/Areas/Topics: Active and Robot Vision ; Applications ; Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Device Calibration, Characterization and Modeling ; Geometry and Modeling ; Image and Video Analysis ; Image Formation and Preprocessing ; Image-Based Modeling ; Mobile Imaging ; Motion, Tracking and Stereo Vision ; Pattern Recognition ; Robotics ; Shape Representation and Matching ; Software Engineering ; Tracking and Visual Navigation

Abstract: In conventional computer vision the actual 3-D state of objects is of primary interest; it is embedded in a temporal sequence analyzed in consecutive pairs. In contrast, in the 4-D approach to machine vision the primary interest is in temporal processes with objects and subjects (defined as objects with the capability of sensing and acting). All perception of 4-D processes is achieved through feedback of prediction errors according to spatiotemporal dynamical models constraining evolution over time. Early jumps to object/subject-hypotheses including capabilities of acting embed the challenge of dynamic scene understanding into a richer environment, especially when competing alternatives are pursued in parallel from beginning. Typical action sequences (maneuvers) form an essential part of the knowledge base of subjects. Expectation-based Multi-focal Saccadic (EMS-) vision has been developed in the late 1990s to demonstrate the advantages and flexibility of this approach. Based on this experience, the paper advocates knowledge elements integrating action processes of subjects as general elements for perception and control of temporal changes, dubbed ‘maneuvers’ here. − As recently discussed in philosophy, emphasizing individual subjects and temporal processes may avoid the separation into a material and a mental world; EMS-vision quite naturally leads to such a monistic view. (More)

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Paper citation in several formats:
D. Dickmanns, E. and D. Dickmanns, E. (2015). Knowledge Bases for Visual Dynamic Scene Understanding.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-089-5, pages 209-215. DOI: 10.5220/0005340802090215

@conference{visapp15,
author={Ernst D. Dickmanns. and Ernst D. Dickmanns.},
title={Knowledge Bases for Visual Dynamic Scene Understanding},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={209-215},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005340802090215},
isbn={978-989-758-089-5},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2015)
TI - Knowledge Bases for Visual Dynamic Scene Understanding
SN - 978-989-758-089-5
AU - D. Dickmanns, E.
AU - D. Dickmanns, E.
PY - 2015
SP - 209
EP - 215
DO - 10.5220/0005340802090215

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