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A biological hierarchical model based underwater moving object detection.

Shen J, Fan T, Tang M, Zhang Q, Sun Z, Huang F - Comput Math Methods Med (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Computer and Information, Hohai University, Nanjing 210098, China ; College of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China.

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.

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Block diagram of the proposed underwater moving object detection method.
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fig1: Block diagram of the proposed underwater moving object detection method.

Mentions: Therefore, the subblock and the pixel-based operation are mutually compensative. The asymmetric forward feedback mechanism is then applied to jointly combine these two strategies to form a hierarchical background model for object detection. Firstly, intensity features are extracted in the subblock and the difference between the subblocks is taken as the cue for classifying the rough object and background region. The rough object region is extracted afterwards and the background model is updated. Then texture features of every single pixel which belongs to the rough object region are extracted to establish the pixel-based background model. Figure 1 illustrates the process of the proposed underwater moving object detection algorithm.


A biological hierarchical model based underwater moving object detection.

Shen J, Fan T, Tang M, Zhang Q, Sun Z, Huang F - Comput Math Methods Med (2014)

Block diagram of the proposed underwater moving object detection method.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4129993&req=5

fig1: Block diagram of the proposed underwater moving object detection method.
Mentions: Therefore, the subblock and the pixel-based operation are mutually compensative. The asymmetric forward feedback mechanism is then applied to jointly combine these two strategies to form a hierarchical background model for object detection. Firstly, intensity features are extracted in the subblock and the difference between the subblocks is taken as the cue for classifying the rough object and background region. The rough object region is extracted afterwards and the background model is updated. Then texture features of every single pixel which belongs to the rough object region are extracted to establish the pixel-based background model. Figure 1 illustrates the process of the proposed underwater moving object detection algorithm.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: College of Computer and Information, Hohai University, Nanjing 210098, China ; College of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China.

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.

Show MeSH
Related in: MedlinePlus