摘要
准确、稳定的图像特征点提取对图像拼接、三维重建以及基于特征点的视觉同时定位与建图(SLAM)等计算机视觉应用十分重要。核辐射环境下采集的图像噪点量多、噪声块较大,传统的特征点提取方法存在容易将噪声判定为特征点的问题。基于某辐照厂卡源故障采集的受γ射线影响的图像噪声分布特点,提出一种抗核噪特征点(ANF)提取算法。首先,分析核辐射下图像的每个像素点的红绿蓝(RGB)特性以及灰度特征,获取可能为噪点的像素;然后,通过传统算法提取特征点;最后,采用特征点与可能噪点的欧氏距离大小进行排序筛选特征点,剔除较大可能性为噪点的特征点。标准图像数据集合成的噪声以及真实核辐照环境下拍摄图像的实验表明,ANF提取方法相对于传统的加速分段测试(FAST)和二进制鲁棒尺度不变特征点(BRISK)提取方法具有更好的稳定性,并可以提高特征提取效果和降低匹配错误率。
Accurate and stable image feature extraction is of great significance to computer vision applications such as image stitching, 3D reconstruction, and feature-based visual simultaneous localization and mapping(SLAM). In the nuclear radiation environment, the captured images have the problems such as many noise points, large noise blocks and the noises are easy to identify as features through the traditional feature extraction methods. An against nuclear feature(ANF) extraction algorithm is proposed based on the noise distribution characteristics of the γ rays affected images which is collected by the source blockage failure of an irradiation factory. Firstly, the red, green,blue(RGB) characteristics and grayscale characteristics of each pixel in the image under nuclear radiation are analyzed to obtain the pixels which are suspected as noise points. Then, the features are extracted by the traditional feature extraction algorithm. Finally, the Euclidean distances between the features and the suspected noise points are used to sort and filter the features, and the features which are suspected as noises with high probability are eliminated. The experiments based on the standard image data set combined noises and collected images in the real nuclear radiation environment show that the ANF method is more stable than the traditional features from accelerated segment test(FAST) method and binary robust invariant scalable keypoints(BRISK) method in extracting features,and can improve the effect of feature extraction and reduce the matching error rate.
作者
张文凯
徐锋
李瑾
Zhang Wenkai;Xu Feng;Li Jin(School of Information Engineering,Southwest University of Science and Technology,Mianyang 621010,Sichuan,China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province,Mianyang 621010,Sichuan,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第12期63-71,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61701421)。
关键词
图像处理
核辐射环境
辐射噪声
特征点提取
特征点筛选
image processing
nuclear irradiation environment
radiation noise
feature extraction
feature filter
