Learning OpenCV, 2nd Edition

matrix expression, it will be evaluated and a pointer to the results will be assigned in m2. The results will

reside in a newly allocated data area.

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Table 3-17 lists examples of the algebraic operations available. Note that in addition to simple algebra,

there are comparison operators, operators for constructing matrices (such as cv::Mat::eye(), which

we encountered earlier), and higher level operations for computing transposes and inversions. The key idea

here is that you should be able to take the sorts of relatively nontrivial matrix expressions that occur when

doing computer vision and express them on a single line in a clear and concise way.

Table 3-17: Operations available for matrix expressions

Example

Description

m0 + m1, m0 – m1;

Addition or subtraction of matrices

m0 + s; m0 – s; s + m0, s – m1;

Addition or subtraction between a matrix

and a singleton

-m0;

Negation of a matrix

s * m0; m0 * s;

Scaling of a matrix by a singleton

m0.mul( m1 ); m0/m1;

Per element multiplication of m0 and m1,

per-element division of m0 by m1

m0 * m1;

Matrix multiplication of m0 and m1

m0.inv( method );

Matrix inversion of m0 (default value of

method is DECOMP_LU)

m0.t();

Matrix transpose of m0 (no copy is done)

m0>m1; m0>=m1; m0==m1; m0<=m1; m0<m1;

Per element comparison, returns uchar

matrix with elements 0 or 255

m0&m1; m0|m1; m0^m1; ~m0;

m0&s; s&m0; m0|s; s|m0 ...