基于显著性检测和改进投影字典对的盲道分割

现有盲道分割算法是通过提取颜色或纹理特征，采用聚类等方法来进行分割，易受盲道类型和外部环境影响。针对此问题，从盲道整体特征进行考虑，引入学习的方式，提出了一种基于显著性检测和改进投影字典对学习的盲道分割方法。该方法首先利用显著性检测，对盲道区域进行粗定位；然后以图像块作为处理单元，通过所提出的稳健字典对学习算法进行字典学习；接着将粗定位后的图像分块在该字典上进行稀疏重构；最后按照重构误差进行分类，以达到分割的目的。实验结果显示，在盲道分割中，该算法相对于现有算法，无论是准确性，还是普适性都表现更好。

Abstract

The current blind road segmentation algorithms are used to segment by extracting the color or texture feature, and by using the clustering method, which are vulnerable to blind road types and the external environment. To solve this problem, a learning approach is introduced, and a blind road segmentation method is proposed based on saliency detection and improved projection dictionary from the consideration of the global feature of blind road. Firstly, the saliency detection algorithms are used to roughly locate the blind road region. Then the image piece is used as the processing unit, and the dictionary is learned through the robust projective dictionary pair learning proposed. And the coarse image after location is divided into blocks to sparsely reconstruct on the dictionary. Finally, the rough-positioned images are reconstructed on the dictionary, and classified according to the reconstruction error to achieve the purpose of segmentation. Experimental results show that the proposed algorithm performs better than the existing algorithms in terms of accuracy and universality in blind road segmentation.

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王民, 肖磊, 杨放. 基于显著性检测和改进投影字典对的盲道分割[J]. 激光与光电子学进展, 2017, 54(4): 041001. Wang Min, Xiao Lei, Yang Fang. Blind Road Segmentation Based on Saliency Detection and Improved Projective Dictionary Pair[J]. Laser & Optoelectronics Progress, 2017, 54(4): 041001.

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