特征提取算法在工件识别中的研究  

Research on Feature Extraction Algorithm in Workpiece Recognition

作　　者：黎卫波 韩卫光 

机构地区：[1]中国科学院沈阳计算技术研究所,沈阳110168 [2]中国科学院大学,北京100049

出　　处：《计算机系统应用》2018年第2期236-239,共4页Computer Systems & Applications

摘　　要：特征提取在工件识别中具有重要的意义.运用灰度变换与平滑去噪对获取到的原始图像进行图像预处理.提出改进的工件特征提取方法.SURF算法作为SIFT算法的加速版,不仅能够确保检测到的特征点的稳定性,而且能很大程度地加快特征提取的时间,满足了工件识别过程中实时性的需求.采用改进的SURF算法的特征匹配方法进行工件的识别.实验表明,改进的特征匹配方法对工件识别精确且速度较快.Feature extraction is of great significance in workpiece recognition. In this paper, the image preprocessing is performed on the original image obtained by gray scale transformation and smooth denoising. An improved method of feature extraction is proposed. The SURF algorithm is an accelerated version of the SIFT algorithm, which cannot only ensure the stability of the detected feature points, but can also to a large extent speed up the extraction of the characteristics of the time. It can meet the real-time needs of the workpiece recognition process. The feature recognition method based on the improved SURF algorithm is used to identify the workpiece. Experiments show that the improved feature matching method is accurate for workpiece identification and the speed is good.

关 键 词：灰度变换 平滑去噪 特征提取 识别 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]