基于形状标记和双谱分析的图像形状特征提取

Image Shape Feature Extraction Method Based on Shape Signature and Bispectrum Analysis

针对严重滑动磨粒、疲劳剥块和层状磨粒等磨粒的图像识别问题,提出了基于形状标记和双谱分析的图像形状特征提取方法.首先根据中心距离函数、累积角函数、最远点距离函数和三角形区域表示等4种形状标记方法,将二维磨粒图像转换为一维信号表示;然后对一维信号进行双谱分析,得到形状的归一化双谱;最后在归一化双谱域内,根据双谱积分和双谱矩计算双谱不变量,得到图像的76维形状特征,涵盖了形状的整体特征、角度变化信息、角点信息和轮廓细节信息等.为了有效评价所提方法的有效性,在MPEG-7 CE Shape-1 Part B数据集和Swedish leaf数据集上进行了形状识别能力实验与抗噪声能力实验.实验结果表明,所提方法能够有效提高双谱分析用于形状识别时的识别准确率和抗噪声能力.

Aiming at the image recognition of wear particles,such as sever sliding,fatigue spall,and laminar particles,image shape feature extraction method based on shape signature and bispectrum analysis was put forward.Firstly,according to four shape signature methods which are centroid distance function,cumulative angular function,farthest point distance,and triangle area representation,the two-dimensional wear particle images were converted to onedimensional signal.Secondly,normalized bispectrum was got by carrying out bispectrum analysis on one-dimensional signal.At last,by calculating bispectral invariants according to bispectral integration and bispectral moment on normalized bispectral domain,76-dimensional shape feature was got,which covered whole feature,angle change information,angular point information,and contour detail information of the shape.In order to evaluate the method,shape recognition ability experiment and anti-noise ability experiment were carried on MPEG-7 CE Shape-1 Part B dataset and Swedish leaf dataset.The experiment results demonstrates that the proposed method can enhance the recognition accuracy rate and anti-noise ability of bispectrum analysis.