改进的SURF算法在书法笔画匹配识别中的应用

摘要

书法笔画具有丰富的书写人特征,能否正确进行特征向量提取和匹配直接影响识别效果.针对SURF算法检测特征点少、误匹配率高的问题,提出了一种基于Contourlet变换的SURF算法.该算法利用Contourlet变换,在提取特征点前对书法字笔画进行子带分解(LP)和方向性滤波(DFB),得到低频和高频细节分量,采用最小欧氏距离准则(LEDC)对低频细节分量进行相似性计算,高频细节分量进一步分解后选取合适阈值提取高频特征点,然后进行SURF特征点匹配,采用RANSAC算法剔除误匹配点.实验表明,改进的SURF算法不仅能更好地提取笔画特征点,提高抗噪性能,识别率也提高了3％.%Calligraphy strokes have rich writer charateristics.Whether feature vectors can be correctly extracted and matched directly affect the recognition effect.Aiming at the problem that the traditional SURF(Scale Invariant Feature Transform)algorithm has fewer detected feature points and higher false matching rate,a SURF based on Contourlet transform is proposed.The algorithm uses Contourlet transform to do sub-band decomposition and directional filtering of calligraphic strokes before the feature points are extracted,and then obtains the low frequency and high frequency detail components.The minimum Euclidean distance criterion (LEDC) is adopted to calculate the similarity of the low-frequency detail components.After the high frequency detail components are further decomposed,the appropriate thresholds are selected to extract the high frequency feature points.Then,the SURF feature points are matched.The RANSAC algorithm is used to eliminate the false matching points.Experiments show that the improved SURF algorithm can not only extract the feature points of the strokes better,but also improve the anti-noise performance.The recognition rate is improved by 3％.