Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75303
Title: iJADE scene segmentator – a real-time scene segmentation system using watershed-based neuro-oscillatory network
Authors: Li, GCL 
Lee, RST 
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3214 LNCS, no. , p. 549-556 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Scene segmentation is one of the most important tasks in research and commercial applications. With the rapid development of Internet, there is an increasing demand for real-time, mobile and autonomous multi-media system to increase the user-friendliness of e-shopping experience and to provide value-added e-services. Traditional scene segmentation techniques, which are mainly sequential and offline, cannot segment images in a real-time manner. This paper introduces a real-time, multi-media system known as iJADE Scene Segmentater – an agent-based scene segmentation system using Solely Excitatory Oscillator networks (SEON) for real-time scene segmentation. It is based on an intelligent multi-agent based model, namely, Intelligent Java Agent Development Environment (iJADE), which supports various e-commerce applications. Using a gallery of 1200 images, our system shows an average segmentation rate of over 98%. iJADE Scene Segmentater segments faster and more powerfully than other contemporary image segmentation methods. The improvement in speed is more significant for large images. We hope that the implementation of iJADE Scene Segmentater can provide a new era of future e-commerce: intelligent multi-media e-commerce.
Description: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems [KES], 20-25 September 2004, Wellington, New Zealand
URI: http://hdl.handle.net/10397/75303
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-30133-2_72
Appears in Collections:Conference Paper

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