摘要
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。
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.
出处
《激光与光电子学进展》
CSCD
北大核心
2017年第4期133-140,共8页
Laser & Optoelectronics Progress
基金
住房和城乡建设部科学技术项目计划(2016-R2-045)
陕西省教育厅专项基金(2013JK1081)
陕西省科学技术研究发展计划项目(CXY1122(2))
陕西省自然科学基金青年基金(2013JQ8003)
关键词
图像处理
图像分析
盲道分割
稳健字典对学习
图像块
image processing
image analysis
blind road segmentation
robust dictionary pair learning
image pieces
