Detection of interest image region based on adaptive radius search

被引：0
|
作者：
机构：
[1] College of Information Science and Technology, Beijing Normal University
来源：
Zhang, L. (libaozhang@163.com) | 2013年 / Science Press卷 / 40期
关键词：
Adaptive radius search; Focus of attention; Image processing; Region of interest;
D O I：
10.3788/CJL201340.0714001
中图分类号：
学科分类号：
摘要：
The regions of an interest image are the parts of priority attention and have more significance. The traditional visual attention model describes the information of regions of interest using fixed-size circles and can't accurately express the outline of the regions of interest. A new automatic detection algorithm of the regions of interest based on adaptive radius search (ARS) is proposed. The new algorithm extracts color, intensity and orientation features of the image to generate a multi-scale saliency map. The global saliency threshold is calculated, which can get the end condition of searching the focus of attention. The adaptive radius search mechanism based on saliency ratio is proposed in the description of regions of interest to acquire the accurate information of regions of interest. The experimental results show that the new algorithm not only can effectively improve the detection precision of regions of interest, but also is more suitable to the features of human visual system. It has important value for the automatic target recognition of regions of interest in the future.
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  • [1] Signoroni A., Lazzaroni F., Leonardi R., Exploitation and extension of the region-of-interest coding functionalities in JPEG2000, IEEE Transactions on Consumer Electronics, 49, 4, pp. 818-823, (2003)
  • [2] Liu L., Fan G., A new JPEG 2000 region of interest image coding method: Partial significant bitpanes shift, IEEE Signal Processing Letters, 10, 2, pp. 35-38, (2003)
  • [3] Itti L., Koch C., Computational modeling of visual attention, Nature Reviews Neuroscience, 2, 3, pp. 194-202, (2001)
  • [4] Le Meur O., Le Callet P., Barba D., Et al., A coherent computational approach to model bottom-up visual attention, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 5, pp. 802-817, (2006)
  • [5] Navalpakkam V., Itti L., Modeling the influence of task on attention, Visual Research, 45, 2, pp. 205-231, (2005)
  • [6] Itti L., Koch C., Niebur E., A model of saliency-based visual attention for rapid scene analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 11, pp. 1254-1259, (1998)
  • [7] Lee D., Itti L., Koch C., Attention activates winner-take-all competition among visual filters, Nature Neuroscience, 2, 4, pp. 375-381, (1999)
  • [8] Zhang L., Wang P., Fast detection of regions of interest in high resolution remote sensing image, Chinese J Lasers, 39, 7, (2012)
  • [9] Xin R., Lei R., Search aid system based on machine vision and its visual attention model for rescue target detection, Second WRI Global Congress on Intelligent Systems, 2, pp. 149-152, (2011)
  • [10] Zhang L., Fast detection of visual saliency regions in remote sensing image based on region growing, Chinese J Lasers, 39, 11, (2012)
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