摘要
在计算机视觉中 ,平面曲线的形状分类和识别具有重要的意义 .在立体足迹的重压面边界围线的形状分类和识别中 ,由于不能严格控制基准面方向的一致性 ,必须采取对透视变换不敏感的描述方法 .从空间旋转不变的要求出发 ,首先从平面闭合曲线得到一维极径序列 ,进而采用该序列的自回归模型来完成特征提取并用于平面曲线的形状分类 .与传统方法相比 ,这种方法提取的特征具有与采样起始点无关、受噪声影响小、计算简单、方便用于分类等优点 .定性分析和实验结果证明了这种方法提取的特征对小角度的透视失真是不敏感的 .该方法应用于立体足迹重压面边界围线的同一性认定效果明显 。
The shape classification and recognition of planar curve plays an important role in computer vision. In the shape analysis of stress surface contour of 3D footprint, the consistency of datum plane cannot be strictly controlled, so a descriptor insensitive to perspective transform is needed. In view of this, a one dimension polar distance sequence is obtained first from the closed planar curve and then the auto regressive model of this sequence is used to extract feature vector. Finally, the feature vectors are used to shape classification and recognition of planar curve. Compared with traditional algorithms, the features extracted using our approach have many advantages, such as invariance to sampling start point, robust to noise, less computation, and convenient to use in shape classification. Qualitative analysis and experimental results prove that the features extracted are insensitive to space rotation of a small angle. Experimental results also show that the new approach works properly well in confirming the identity of region contours of two footprints stress surface, and can be used in recognition and classification of 3D footprint.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2000年第8期942-947,共6页
Journal of Computer Research and Development
基金
河南省自然科学基金资助!(项目编号 SPOOXS0 0 6)
关键词
平面曲线识别
自回归模型
特征提取
计算机视觉
planar curve recognition, auto regressive model, perspective transform, feature extraction, 3D footprint recognition
