Robust digital image stabilization using feature tracking

Wang, Chuntao; Kim, Jinhyung; Byun, Keun-Yung; Ko, Sung-Jea

doi:10.1109/icce.2009.5012257

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…It is also easy to implement in practical applications. The Kalman Filter has numerous applications in technology and is widely employed in the fields of the communication, GPS navigation, robot vision and image painting [40,41,42]. …”

Section: Discrete Kalman Filter Modelmentioning

confidence: 99%

An Efficient Recommendation Filter Model on Smart Home Big Data Analytics for Enhanced Living Environments

Chen

Xie

Shu

et al. 2016

Sensors

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With the rapid growth of wireless sensor applications, the user interfaces and configurations of smart homes have become so complicated and inflexible that users usually have to spend a great amount of time studying them and adapting to their expected operation. In order to improve user experience, a weighted hybrid recommender system based on a Kalman Filter model is proposed to predict what users might want to do next, especially when users are located in a smart home with an enhanced living environment. Specifically, a weight hybridization method was introduced, which combines contextual collaborative filter and the contextual content-based recommendations. This method inherits the advantages of the optimum regression and the stability features of the proposed adaptive Kalman Filter model, and it can predict and revise the weight of each system component dynamically. Experimental results show that the hybrid recommender system can optimize the distribution of weights of each component, and achieve more reasonable recall and precision rates.

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Section: Discrete Kalman Filter Modelmentioning

confidence: 99%