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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 609801, 8 pages
http://dx.doi.org/10.1155/2014/609801
Research Article

A Biological Hierarchical Model Based Underwater Moving Object Detection

1College of Computer and Information, Hohai University, Nanjing 210098, China
2College of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China
3School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Received 26 May 2014; Accepted 11 July 2014; Published 22 July 2014

Academic Editor: Shengyong Chen

Copyright © 2014 Jie Shen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable background, the mechanism of the visual information sensing and processing pattern from the eye of frogs are imitated to produce a hierarchical background model for detecting underwater objects. Firstly, the image is segmented into several subblocks. The intensity information is extracted for establishing background model which could roughly identify the object and the background regions. The texture feature of each pixel in the rough object region is further analyzed to generate the object contour precisely. Experimental results demonstrate that the proposed method gives a better performance. Compared to the traditional Gaussian background model, the completeness of the object detection is 97.92% with only 0.94% of the background region that is included in the detection results.