Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1939
Title: Bottom-up spatiotemporal visual attention model for video analysis
Authors: Rapantzikos, Konstantinos 
Tsapatsoulis, Nicolas 
Avrithis, Yannis 
Kollias, Stefanos D. 
Major Field of Science: Engineering and Technology
Keywords: Video signal processing;Video signal processing;Image-oriented computational model;Image sequences
Issue Date: 2007
Source: Image Processing, IET, 2007, vol. 1, no. 2, pp. 237-248.
Volume: 1
Issue: 2
Start page: 237
End page: 248
Journal: IEEE Transactions on Image Processing 
Abstract: The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown.
Description: Research Paper
URI: https://hdl.handle.net/20.500.14279/1939
ISSN: 17519667
DOI: 10.1049/iet-ipr:20060040
Rights: © IEEE
Type: Article
Affiliation : National Technical University Of Athens 
University of Cyprus 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

CORE Recommender
Sorry the service is unavailable at the moment. Please try again later.
Show full item record

SCOPUSTM   
Citations

38
checked on Nov 9, 2023

WEB OF SCIENCETM
Citations 50

28
Last Week
0
Last month
0
checked on Oct 17, 2023

Page view(s)

650
Last Week
9
Last month
0
checked on Feb 17, 2025

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.