Interest Point Detection Based on Stochastically Derived Stability

Interest Point Detection Based on Stochastically Derived Stability

We propose a novel framework called StochasticSIFT for detecting interest points (IPs) in video sequences. The proposed framework incorporates a stochastic model considering the temporal dynamics of videos into the SIFT detector to improve robustness against fluctuations inherent to video signals. I...

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Veröffentlicht in:Information and Media Technologies 2012, Vol.7(1), pp.256-267
Hauptverfasser: Watchareeruetai, Ukrit, Kimura, Akisato, Bao, Robert Cheng, Kawanishi, Takahito, Kashino, Kunio
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container_start_page 256
container_title Information and Media Technologies
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creator Watchareeruetai, Ukrit
Kimura, Akisato
Bao, Robert Cheng
Kawanishi, Takahito
Kashino, Kunio
description We propose a novel framework called StochasticSIFT for detecting interest points (IPs) in video sequences. The proposed framework incorporates a stochastic model considering the temporal dynamics of videos into the SIFT detector to improve robustness against fluctuations inherent to video signals. Instead of detecting IPs and then removing unstable or inconsistent IP candidates, we introduce IP stability derived from a stochastic model of inherent fluctuations to detect more stable IPs. The experimental results show that the proposed IP detector outperforms the SIFT detector in terms of repeatability and matching rates.
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