...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >A bivariate autoregressive technique for analysis and classification of planar shapes
【24h】

A bivariate autoregressive technique for analysis and classification of planar shapes

机译：用于平面形状分析和分类的双变量自回归技术

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A bivariate autoregressive model is introduced for the analysis and classification of closed planar shapes. The boundary coordinate sequence of a digitized binary image is sampled to produce a polygonal approximation to an object's shape. This circular sample sequence is then represented by a vector autoregressive difference equation which models the individual Cartesian coordinate sequences as well as coordinate interdependencies. Several classification features which are functions or transformations of the estimated coefficient matrices and the associated residual error covariance matrices are developed. These features are shown to be invariant to object transformations such as translation, rotation, and scaling. Laboratory experiments involving object sets representative of industrial shapes are presented. Superior classification results are demonstrated.
机译：引入了双变量自回归模型，用于封闭平面形状的分析和分类。对数字化二进制图像的边界坐标序列进行采样，以生成与物体形状的多边形近似值。然后，此循环样本序列由向量自回归差分方程表示，该方程对单个笛卡尔坐标序列以及坐标相互依赖性进行建模。发展了几种分类特征，它们是估计系数矩阵和相关残差协方差矩阵的函数或变换。这些功能对于对象转换（例如平移，旋转和缩放）是不变的。介绍了涉及代表工业形状的对象集的实验室实验。证明了优越的分类结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号