Gaussian Pyramid
Perform Gaussian pyramid decomposition
Library
Transforms
visiontransforms
Description
The Gaussian Pyramid block computes Gaussian pyramid reduction or expansion to resize an image. The image reduction process involves lowpass filtering and downsampling the image pixels. The image expansion process involves upsampling the image pixels and lowpass filtering. You can also use this block to build a Laplacian pyramid. For more information, see Examples.
Note
This block supports intensity and color images on its ports.
| Port | Output | Supported Data Types | Complex Values Supported |
|---|---|---|---|
Input | In In |
| No |
Output | In In | Same as Input port | No |
Use the Operation parameter to specify
whether to reduce or expand the input image. If you select Reduce,
the block applies a lowpass filter and then downsamples the input
image. If you select Expand, the block
upsamples and then applies a lowpass filter to the input image.
Use the Pyramid level parameter to specify
the number of times the block upsamples or downsamples each dimension
of the image by a factor of 2. For example, suppose you have a 4-by-4
input image. You set the Operation parameter
to Reduce and the Pyramid level to 1.
The block filters and downsamples the image and outputs a 2-by-2 pixel
output image. If you have an M-by-N input image and you set the Operation parameter
to Reduce, you can calculate the dimensions
of the output image using the following equation:
You must repeat this calculation for each successive pyramid
level. If you have an M-by-N input image and you set the Operation parameter
to Expand, you can calculate the dimensions
of the output image using the following equation:
In the previous equation, l is the scalar
value from 1 to inf that you enter for
the Pyramid level parameter.
Use the Coefficient source parameter to
specify the coefficients of the lowpass filter. If you select Default
separable filter [1/4-a/2 1/4 a 1/4 1/4-a/2], use the a parameter
to define the coefficients in the vector of separable filter coefficients.
If you select Specify via dialog, use the Coefficient
for separable filter parameter to enter a vector of separable
filter coefficients.
Examples
The following example model shows how to construct a Laplacian pyramid:
Open this model by typing
at the MATLAB® command prompt.
Run the model to see the following results.
You can construct a Laplacian pyramid if the dimensions of the input image, R-by-C, satisfy and , where MR, MC, and N are integers. In this example, you have an input matrix that is 256-by-256. If you set MR and MC equal to 63 and N equal to 2, you find that the input image needs to be 253-by-253. So you use a Submatrix block to crop the dimensions of the input image to 253-by-253.
Fixed-Point Data Types
The following diagram shows the data types used in the Gaussian Pyramid block for fixed-point signals:
You can set the coefficients table, product output, accumulator, and output data types in the block mask.
Parameters
- Operation
Specify whether you want to reduce or expand the input image.
- Pyramid level
Specify the number of times the block upsamples or downsamples each dimension of the image by a factor of 2.
- Coefficient source
Determine how to specify the coefficients of the lowpass filter. Your choices are
Default separable filter [1/4-a/2 1/4 a 1/4 1/4-a/2]orSpecify via dialog.- a
Enter a scalar value that defines the coefficients in the default separable filter
[1/4-a/2 1/4 a 1/4 1/4-a/2]. This parameter is visible if, for the Coefficient source parameter, you selectDefault separable filter [1/4-a/2 1/4 a 1/4 1/4-a/2].- Coefficients for separable filter
Enter a vector of separable filter coefficients. This parameter is visible if, for the Coefficient source parameter, you select
Specify via dialog.
- Rounding mode
Select the rounding mode for fixed-point operations.
- Overflow mode
Select the overflow mode for fixed-point operations.
- Coefficients
Choose how to specify the word length and the fraction length of the coefficients:
When you select
Same word length as input, the word length of the coefficients match that of the input to the block. In this mode, the fraction length of the coefficients is automatically set to the binary-point only scaling that provides you with the best precision possible given the value and word length of the coefficients.When you select
Specify word length, you can enter the word length of the coefficients, in bits. The block automatically sets the fraction length to give you the best precision.When you select
Binary point scaling, you can enter the word length and the fraction length of the coefficients, in bits.When you select
Slope and bias scaling, you can enter the word length, in bits, and the slope of the coefficients. The bias of all signals in the Computer Vision Toolbox™ blocks is 0.
- Product output
As shown in the previous figure, the output of the multiplier is placed into the product output data type and scaling. Use this parameter to specify how to designate the product output word and fraction lengths.
When you select
Same as input, these characteristics match those of the input to the block.When you select
Binary point scaling, you can enter the word length and the fraction length of the product output, in bits.When you select
Slope and bias scaling, you can enter the word length, in bits, and the slope of the product output. The bias of all signals in the Computer Vision Toolbox blocks is 0.
- Accumulator
As shown in the previous figure, inputs to the accumulator are cast to the accumulator data type. The output of the adder remains in the accumulator data type as each element of the input is added to it. Use this parameter to specify how to designate the accumulator word and fraction lengths.
When you select
Same as product output, these characteristics match those of the product output.When you select
Same as input, these characteristics match those of the input to the block.When you select
Binary point scaling, you can enter the word length and the fraction length of the accumulator, in bits.When you select
Slope and bias scaling, you can enter the word length, in bits, and the slope of the accumulator. The bias of all signals in the Computer Vision Toolbox blocks is 0.
- Output
Choose how to specify the word length and fraction length of the output of the block:
When you select
Same as input, these characteristics match those of the input to the block.When you select
Binary point scaling, you can enter the word length and the fraction length of the output, in bits.When you select
Slope and bias scaling, you can enter the word length, in bits, and the slope of the output. The bias of all signals in the Computer Vision Toolbox blocks is 0.
- Lock data type settings against change by the fixed-point tools
Select this parameter to prevent the fixed-point tools from overriding the data types you specify on the block mask. For more information, see
fxptdlg(Fixed-Point Designer), a reference page on the Fixed-Point Tool in the Simulink® documentation.
See Also
Computer Vision Toolbox software |