Small Target Detection Based on Visual Saliency Improved by Spatial Distance

被引：0
作者：
Yang, Linna [1 ]
An, Wei [1 ]
Lin, Zaiping [1 ]
Li, Andong [1 ]
机构：
[1] School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, Hunan
来源：
Guangxue Xuebao/Acta Optica Sinica | 2015年 / 35卷 / 07期
关键词：
Machine vision; Small target detection; Spatial distance; Visual saliency;
D O I：
10.3788/AOS201535.0715004
中图分类号：
学科分类号：
摘要：
In human visual system (HVS), the contrast value occupies the most important part rather than the brightness. The existing infrared small target detection algorithm based on HVS can get a higher signal to noise ratio and detection rate, but it also has shortcomings of higher false alarm rate and easily effected by noise. Considering those shortcomings. An infrared small target detection algorithm based on visual saliency improved by spatial distance is proposed. The gray value of target is weighted by the ratio of the target pixel block and its surrounding blocks, and a saliency map is got. Spatial distance is one of the most important factors in visual attention mechanism and is used in the calculation of the weighted sum of the surrounding blocks. Smaller weight value is distributed to fruther distance according to the weighted space distance. Experimental results show that the proposed algorithm can not only get a lower false alarm rate, but also has robustness for noise. ©, 2015, Chinese Optical Society. All right reserved.
引用
收藏
页数：6
相关论文
共 14 条
  • [1] Desimone R., Duncan J., Neural mechanisms of selective visual attention, Annual Review of Neuroscience, 18, 1, pp. 193-222, (1995)
  • [2] Han J.H., Ma Y., Zhou B., Et al., A robust infrared small target detection algorithm based on human visual system, IEEE Geoscience Remote Sensing Letters, 11, 12, pp. 2168-2172, (2014)
  • [3] Zhang L., Zhang J., A new adaptive fusion method based on saliency analysis for remote sensing images, Chinese J Lasers, 42, 1, (2015)
  • [4] Lin C., He B., Dong S., An indoor object fast detection method based on visual attention mechanism of fusion depth information in RGB image, Chinese J Lasers, 41, 11, (2014)
  • [5] Xu L., Tan X., Infrared small target detection algorithm based on visual attention, Journal of Air Force Early Warning Academy, 27, 6, pp. 397-401, (2013)
  • [6] Zhang J., Shen L., Gao J., Region of interest detection based on visual attention model and evolutionary programming, Journal of technology Electronics & Information, 31, 7, pp. 1646-1652, (2009)
  • [7] Sun J., Chen R., He J., A Modified GBVS Method with Entropy for Extracting Bottom-up Attention Information, pp. 765-770, (2012)
  • [8] Hansen L.K., Karadogan S., Marchegiani L., What to measure next to improve decision making? On top-down task driven feature saliency, Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), pp. 1-7, (2011)
  • [9] Baluch F., Itti L., Mechanisms of top-down attention, Trends in Neurosciences, 34, 4, pp. 210-224, (2011)
  • [10] Koch C., Ullman S., Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry, pp. 115-141, (1987)
12