The detection range of underwater laser imaging technology achieves 4—6 times of detection range of
conventional camera in intervening water medium, which makes it very promising in oceanic research, deep sea
exploration and robotic works. However, the special features in underwater laser images, such as speckle noise and
non-uniform illumination, bring great difficulty for image segmentation. In this paper, a novel saliency motivated
pulse coupled neural network (SM-PCNN) is proposed for underwater laser image segmentation. The pixel saliency
is used as external stimulus of neurons. For improvement of convergence speed to optimal segmentation, a gradient
descent method based on maximum two-dimensional Renyi entropy criterion is utilized to determine the dynamic
threshold. On the basis of region contrast in each iteration step, the real object regions are effectively distinguished,
and the robustness against speckle noise and non-uniform illumination is improved by region selection. The
proposed method is compared with four other state-of-the-art methods which are watershed, fuzzy C-means, meanshift
and normalized cut methods. Experimental results demonstrate the superiority of our proposed method to
allow more accurate segmentation and higher robustness.
WANG Bo* (王 博), WAN Lei (万 磊), LI Ye (李 晔)
. Saliency Motivated Pulse Coupled Neural Network for Underwater Laser Image Segmentation[J]. Journal of Shanghai Jiaotong University(Science), 2016
, 21(3)
: 289
-296
.
DOI: 10.1007/s12204-016-1724-1
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