Design of automatic force application system and outlier detection for force sensor

He, Chi; Dong, Guangling; Li, Qiang; Wei, Hongqiang; Zhang, Jihuan; Lu, Jian

doi:10.1109/icsenst.2013.6727756

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…This actionable information can be used in many applications. These applications include but not restricted to fraud detection for credit cards [4], control systems [5], medical research, parallel software applications [6], steganalysis in image sharing applications [7], intelligent transportation system, wireless sensor networks [8,9], and human skin detection [10]. Outlier detection methods can be classified into three categories [3], [11], [12]: statistical methods, proximity-based methods, and clustering-based methods.…”

Section: Introductionmentioning

confidence: 99%

Image noise Detection and Removal based on Enhanced GridLOF Algorithm

Elmogy¹,

Mahmoud²,

Turki³

2017

ijacsa

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Abstract-Image noise removal is a major task in image processing where noise can harness any information inferred from the image especially when the noise level is high. Although there exists many outlier detection approaches used for this task, more enhancements are needed to achieve better performance specifically in terms of time. This paper proposes a new algorithm to detect and remove noise from images depending on an enhanced version of GridLOF algorithm. The enhancement aims to reduce the time and complexity of the algorithm while attaining comparable accuracy. Simulation results on a set of different images proved that proposed algorithm achieves the standard accuracy.

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