计算机工程2018，Vol.44Issue(3)：270-274,5.DOI:10.3969/j.issn.1000-3428.2018.03.045
一种局部最佳阈值预测的自适应角点检测方法
An Adaptive Corner Detection Method of Local Optimal Threshold Prediction
摘要
Abstract
In order to solve the problem of large computational load of the corner detection threshold selection method,a new adaptive corner detection method is proposed.Nine basic statistical characteristics that can reflect the gray distribution,contrast and correlation of the images are analyzed.The basic statistical characteristics of 4 848 samples are extracted,and the principal components analysis is used to calculate 4 comprehensive indexes reflecting the different attributes of the images.The multivariate nonlinear local optimal threshold prediction model is established,and the model parameters are optimized and estimated by the training data.The prediction model of the guidance corner detection adaptive threshold selection is obtained.Experimental results show that the introduction of prediction model can improve the quality of detection of significant corners of the image,detection rate of significant corners in complex images is improved by 45％ on average compared with the original detection algorithm,and the average false detection rate is reduced by 81％ on average.关键词
样本图像/统计特征/角点检测/自适应阈值/预测模型Key words
sample image/statistical feature/corner detection/adaptive threshold/prediction model分类
信息技术与安全科学引用本文复制引用
吴腾,张志利,赵军阳,张海峰..一种局部最佳阈值预测的自适应角点检测方法[J].计算机工程,2018,44(3):270-274,5.基金项目
国家自然科学基金(41174162). （41174162）
