Learning OpenCV, 2nd Edition

the different available fitting methods. In the case of , the cost function will become the

familiar least-squares fitting procedure that is probably st readers from elementary statistics.

The more complex distan useful when m methods are needed (i.e., fitting

u a or

familiar to mo

ce functions are ore robust fitting

methods that handle o tlier d ta points more gracefully). Table 8-2 shows the available forms f and

ST_L2

the associated OpenCV enum values used by cv::fitLine().

Table 8-2: Available distance metrics for the distType parameter in cv::fitLine()

distType

Distance Metric



Least-squares

method

cv::DI

cv::DIST_L1

cv::DIST_L12

cv::DIST_FAIR

cv::DIST_WELSCH

cv::DIST_HUBER

The OpenCV function cv::fitLine() has the following function prototype:

void cv::fitLine(

cv::InputArray points, // Array 2-dimensional points or vector of

cv::OutputArray line, // Vector of Vec4f (2d), or Vec6f (3d)

int distType, // Distance type (Table 8-2)

double param, // Parameter for distance metric (Table 8-2)

double reps, // Radius accuracy parameter

double aeps // Angle accuracy parameter

);

The argument points is mostly what you have come to expect, a representation of a set of points either

a cv::Mat array or an STL vector. One very important difference, however, between

as

c

t,

r

his

cv::fitLine() matically select the optim e for

r

v::fitLine()

al valu

and ...