Mehmood, Irfan; Sajjad, Muhammad; Baik, Sung Wook
Sensors (Basel, Switzerland)
2014NA ; 14 ( 9 ) :17112-45.
PMID : 25225874
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Mehmood, Irfan - College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea. irfanmehmood@sju.ac.kr.
Sajjad, Muhammad - College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea. sajjad@sju.ac.kr.
Baik, Sung Wook - College of Electronics and Information Engineering, Sejong University, Seoul 143-747, Korea. sbaik@sejong.ac.kr.
ABSTRACT
Wireless capsule endoscopy (WCE) has great advantages over traditional endoscopy because it is portable and easy to use, especially in remote monitoring health-services. However, during the WCE process, the large amount of captured video data demands a significant deal of computation to analyze and retrieve informative video frames. In order to facilitate efficient WCE data collection and browsing task, we present a resource- and bandwidth-aware WCE video summarization framework that extracts the representative keyframes of the WCE video contents by removing redundant and non-informative frames. For redundancy elimination, we use Jeffrey-divergence between color histograms and inter-frame Boolean series-based correlation of color channels. To remove non-informative frames, multi-fractal texture features are extracted to assist the classification using an ensemble-based classifier. Owing to the limited WCE resources, it is impossible for the WCE system to perform computationally intensive video summarization tasks. To resolve computational challenges, mobile-cloud architecture is incorporated, which provides resizable computing capacities by adaptively offloading video summarization tasks between the client and the cloud server. The qualitative and quantitative results are encouraging and show that the proposed framework saves information transmission cost and bandwidth, as well as the valuable time of data analysts in browsing remote sensing data.
wireless capsule sensor; video summarization; mobile-cloud computing; energy saving; remote monitoring; implantable sensors
MESH
Capsule Endoscopy/*methods, Data Compression/*methods, Humans, Image Enhancement/*methods, Image Interpretation, Computer-Assisted/*methods, *Signal Processing, Computer-Assisted, Telemedicine/*methods, Video Recording/*methods
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