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An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis
School of Information Science and Engineering, Ocean University of China, 238 Songling Road, Qingdao 266100, China
* Author to whom correspondence should be addressed.
Received: 8 June 2013; in revised form: 6 July 2013 / Accepted: 8 July 2013 / Published: 16 July 2013
Abstract: Underwater vision is one of the dominant senses and has shown great prospects in ocean investigations. In this paper, a hierarchical Independent Component Analysis (ICA) framework has been established to explore and understand the functional roles of the higher order statistical structures towards the visual stimulus in the underwater artificial vision system. The model is inspired by characteristics such as the modality, the redundancy reduction, the sparseness and the independence in the early human vision system, which seems to respectively capture the Gabor-like basis functions, the shape contours or the complicated textures in the multiple layer implementations. The simulation results have shown good performance in the effectiveness and the consistence of the approach proposed for the underwater images collected by autonomous underwater vehicles (AUVs).
Keywords: ocean investigations; AUV; early human vision system; ICA; underwater vision model
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MDPI and ACS Style
Nian, R.; Liu, F.; He, B. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis. Sensors 2013, 13, 9104-9131.
Nian R, Liu F, He B. An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis. Sensors. 2013; 13(7):9104-9131.
Nian, Rui; Liu, Fang; He, Bo. 2013. "An Early Underwater Artificial Vision Model in Ocean Investigations via Independent Component Analysis." Sensors 13, no. 7: 9104-9131.