MATLAB and Simulink. Computer Vision Toolbox™ Reference.

2492 Pages·2021·47.723 MB·English

by  MathWorks, Inc.

Computer Vision Toolbox™
Reference
R2021b
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Computer Vision Toolbox™ Reference
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Revision History
April 2011 Online only Revised for Version 4.0 (Release 2011a)
September 2011 Online only Revised for Version 4.1 (Release 2011b)
March 2012 Online only Revised for Version 5.0 (Release 2012a)
September 2012 Online only Revised for Version 5.1 (Release R2012b)
March 2013 Online only Revised for Version 5.2 (Release R2013a)
September 2013 Online only Revised for Version 5.3 (Release R2013b)
March 2014 Online only Revised for Version 6.0 (Release R2014a)
October 2014 Online only Revised for Version 6.1 (Release R2014b)
March 2015 Online only Revised for Version 6.2 (Release R2015a)
September 2015 Online only Revised for Version 7.0 (Release R2015b)
March 2016 Online only Revised for Version 7.1 (Release R2016a)
September 2016 Online only Revised for Version 7.2 (Release R2016b)
March 2017 Online only Revised for Version 7.3 (Release R2017a)
September 2017 Online only Revised for Version 8.0 (Release R2017b)
March 2018 Online only Revised for Version 8.1 (Release R2018a)
September 2018 Online only Revised for Version 8.2 (Release R2018b)
March 2019 Online only Revised for Version 9.0 (Release R2019a)
September 2019 Online only Revised for Version 9.1 (Release R2019b)
March 2020 Online only Revised for Version 9.2 (Release R2020a)
September 2020 Online only Revised for Version 9.3 (Release R2020b)
March 2021 Online only Revised for Version 10.0 (Release R2021a)
September 2021 Online only Revised for Version 10.1 (Release R2021b)
Contents
Blocks
1
Objects
2
Functions
3
iii

1
Blocks
1
Blocks
2-D Autocorrelation
2-D autocorrelation of input matrix
Library: Computer Vision Toolbox / Statistics
Description
The 2-D Autocorrelation block computes the 2-D autocorrelation of the input vector or matrix.
Ports
Input
Port_1 — Input array
vector | matrix
Input array, specified as a vector or matrix.
Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32 | fixed point
Complex Number Support: Yes
Output
Port_1 — Autocorrelation of input
vector | matrix
Autocorrelation of the input array, returned as a vector or matrix. The data type of the output is the
same as that of the input.
Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32 | fixed point
Complex Number Support: Yes
Parameters
For details on fixed-point block parameters, see “Specify Fixed-Point Attributes for Blocks”.
Lock data type settings against change by the fixed-point tools — Prevent fixed-
point tools from overriding data types
off (default) | on
Select this parameter to prevent the fixed-point tools from overriding the data types you specify in
this block. For more information, see “Lock the Output Data Type Setting” (Fixed-Point Designer).
Block Characteristics
Data Types double | fixed point | integer | single
1-2
2-D Autocorrelation
Multidimensional no
Signals
Variable-Size Signals yes
Algorithms
If the input Ais a vector with dimension M-by-1 then the equation for 1-D discrete autocorrelation is:
where 0≤n≤2M−1 .
The output is an autocorrelation vector of size (2M−1,1).
If the input A is a matrix with dimension M-by-N then the equation for the 2-D discrete
autocorrelation is:
where 0≤i<2M−1 and 0≤ j<2N−1.
The dimension of the output autocorrelation matrix is (2M−1,2N−1).
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.
See Also
Blocks
2-D Correlation | 2-D Histogram | 2-D Mean | 2-D Median | 2-D Standard Deviation | 2-D Variance | 2-
D Maximum | 2-D Minimum
Introduced before R2006a
1-3
1
Blocks
2-D Convolution
Compute 2-D discrete convolution of two input matrices
Library: Computer Vision Toolbox / Filtering
Description
The 2-D Convolution block computes the two-dimensional convolution of two input matrices. Assume
that matrix A has dimensions (Ma, Na) and matrix B has dimensions (Mb, Nb). When the block
calculates the full output size, the equation for the 2-D discrete convolution is:
(Ma−1)(Na−1)
C(i, j)= ∑ ∑ A(m,n)*B(i−m, j−n)
m=0 n=0
where 0≤i<Ma+Mb−1 and 0≤ j<Na+Nb−1.
Ports
Input
I1 — Input matrix
matrix
Input matrix, specified as either a matrix of intensity values or a matrix that represents one plane of
an RGB video stream.
Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 |
fixed point
I2 — Input matrix
matrix
Input matrix, specified as either a matrix of intensity values or a matrix that represents one plane of
an RGB video stream.
Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 |
fixed point
Output
Output — Convolution
matrix
Convolution of the input matrices, returned as a matrix.
1-4
2-D Convolution
Dependencies
• The dimensions of the output are dictated by the Output size parameter.
• If the data type of the input is floating point, the output of the block is also floating point.
• If all(size(I1)<size(I2)), the block returns an error.
Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 |
fixed point
Parameters
Main Tab
Output size — Output size
matrix
Dimensions of the output.
The table describes the block output, given the following input dimensions:
• I1 — (Ma, Na)
• I2 — (Mb, Nb)
Output size Output Output Dimension
Full Full two-dimensional (Ma+Mb-1, Na+Nb-1).
convolution
Same as input port I1 Central part of the convolution
with the same dimensions as the
input at port I1
Valid Only the parts of the (Ma-Mb+1, Na-Nb+1)
convolution that are computed
without the zero-padded edges
of any input.
Normalized output — Normalized output
matrix
Normalize output by dividing the output by sqrt(sum(dot(I1p,I1p))*sum(dot(I2,I2))),
where I1p is the portion of the I1 matrix that aligns with the I2 matrix.
Note When you select the Normalized output check box, the block input cannot be fixed point.
Data Types Tab
For details on the fixed-point block parameters, see “Specify Fixed-Point Attributes for Blocks” .
Block Characteristics
Data Types double | fixed point | integer | single
1-5
1
Blocks
Multidimensional no
Signals
Variable-Size Signals yes
Algorithms
2-D Convolution
In convolution, the value of an output element is computed as a weighted sum of neighboring
elements.
For example, suppose the first input matrix represents an image and is defined as:
I1 = [17 24 1 8 15
23 5 7 14 16
4 6 13 20 22
10 12 19 21 3
11 18 25 2 9]
The second input matrix also represents an image and is defined as:
I2 = [8 1 6
3 5 7
4 9 2]
The following figure shows how to compute the (1,1) output element by using these steps:
1 Rotate the second input matrix, I2, 180 degrees around its center element.
2 Slide the center element of I2 so that it lies on top of the (0,0) element of I1.
3 Multiply each element of the rotated I2 matrix by the element of I1 underneath.
4 Sum the individual products from step 3.
The (1,1) output element is 0⋅2+0⋅9+0⋅4+0⋅7+17⋅5+24⋅3+0⋅6+23⋅1+5⋅8=220.
1-6