关于寻找shape特征(feature)的方法总结

来源：互联网发布：2011安全生产事故数据编辑：程序博客网时间：2024/05/16 16:00

计算机视觉中，目前有哪些成熟的匹配定位算法

这个链接涉及到模板匹配的实际问题, 高票答主提到的ESM, 结构特征ASM的, 我都是第一次看到. 即便是我之前在下面提到的ICP算法也没有实际去应用过,计算机视觉的道路任重而道远啊.

computer and machine vision

computer vision algorithms and applications,

加上对应opencv2 cookbook 涉及到的大部分算法

关于寻找shape特征(feature)的方法总结

二维物体形状识别方法比较

Compares two shapes.

C++: double matchShapes(InputArray contour1, InputArray contour2, int method, double parameter)

Python: cv2.matchShapes(contour1, contour2, method, parameter) → retval

C: double cvMatchShapes(const void* object1, const void* object2, int method, double parameter=0 )

Parameters:

object1 – First contour or grayscale image.
object2 – Second contour or grayscale image.
method – Comparison method: CV_CONTOURS_MATCH_I1 , CV_CONTOURS_MATCH_I2 orCV_CONTOURS_MATCH_I3 (see the details below).
parameter – Method-specific parameter (not supported now).

The function compares two shapes. All three implemented methods use the Hu invariants (see HuMoments() ) as follows ( $A$ denotes object1,:math:B denotes object2 ):

method=CV_CONTOURS_MATCH_I1
$I_1(A,B) = \sum _{i=1...7} \left | \frac{1}{m^A_i} - \frac{1}{m^B_i} \right |$
method=CV_CONTOURS_MATCH_I2
$I_2(A,B) = \sum _{i=1...7} \left | m^A_i - m^B_i \right |$
method=CV_CONTOURS_MATCH_I3
$I_3(A,B) = \max _{i=1...7} \frac{ \left| m^A_i - m^B_i \right| }{ \left| m^A_i \right| }$

where

$\begin{array}{l} m^A_i = \mathrm{sign} (h^A_i) \cdot \log{h^A_i} \\ m^B_i = \mathrm{sign} (h^B_i) \cdot \log{h^B_i} \end{array}$

and $h^A_i, h^B_i$ are the Hu moments of $A$ and $B$ , respectively.

阅读全文

0 0