计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 197-204.doi: 10.11896/jsjkx.191000054
陈晓文, 刘光帅, 刘望华, 李旭瑞
CHEN Xiao-wen, LIU Guang-shuai, LIU Wang-hua, LI Xu-rui
摘要： 针对原始的局部相位量化(Local Phase QuantizationLPQ)算法对具有模糊不变性的相位特征描述不准确、缺少对图像重要细节信息描述的缺点提出了一种结合高斯拉普拉斯(Laplace of GaussianLoG)边缘检测和增强局部相位量化(Enhanced Local Phase QuantizationELPQ)的模糊图像识别算法记为MrELPQ&MsLoG(Multi-resolution ELPQand Multi-scaleLoG).首先在频域中将图像进行短时傅里叶变换后得到的实部与虚部进行正负量化和幅值量化得到互补的符号特征ELPQ_S和幅值特征ELPQ_M;其次在空间域中利用多尺度高斯拉普拉斯与图像进行卷积得到图像空间域的边缘特征;最后将频域上的符号特征ELPQ_S和幅值特征ELPQ_M与空间域上的边缘特征结合生成最终的特征直方图采用SVM进行识别.在有模糊干扰的Brodatz和KTH-TIPS纹理库中文中提出的ELPQ算法相比原始的LPQ算法有较大的性能提升且空间域和频域结合的MrELPQ&MsLoG算法能进一步提高算法的识别性能;在具有模糊的AR、Extend YaleB人脸库和实际拍摄的铁路扣件库中将MrELPQ&MsLoG算法与目前模糊鲁棒性较好的算法进行对比发现MrELPQ&MsLoG算法保持着较高的识别率.实验结果表明MrELPQ&MsLoG算法对模糊具有较强的鲁棒性且特征提取时间较短具有实时性.
中图分类号:
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