Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1155/2014/609801 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Keywords | Field | DocType |
normal distribution,algorithms,visual perception,computer simulation | Computer vision,Object detection,Normal distribution,Computer science,Gaussian,Artificial intelligence,Pixel,Completeness (statistics),Hierarchical database model,Visual perception,Machine learning,Underwater | Journal |
Volume | ISSN | Citations |
2014 | 1748-670X | 1 |
PageRank | References | Authors |
0.37 | 9 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Shen | 1 | 12 | 6.03 |
| Tanghuai Fan | 2 | 13 | 9.73 |
| Min Tang | 3 | 11 | 1.78 |
| Qian Zhang | 4 | 1 | 0.71 |
| Zhen Sun | 5 | 9 | 4.89 |
| Fengchen Huang | 6 | 28 | 4.21 |