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 |
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