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disparitySGM

Compute disparity map through semi-global matching

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Syntax

disparityMap = disparitySGM(I1,I2)
disparityMap = disparitySGM(I1,I2,Name,Value)

Description

example

disparityMap= disparitySGM(I1,I2)computes disparity map from a pair of rectified stereo imagesI1andI2, by using semi-global matching (SGM) method. To know more about rectifying stereo images, seeImage Rectification.

disparityMap= disparitySGM(I1,I2,Name,Value)specifies options using one or more name-value pair arguments.

Examples

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Open Live Script

Load a rectified stereo pair image.

I1 = imread('rectified_left.png'); I2 = imread('rectified_right.png');

Create the stereo anaglyph of the rectified stereo pair image and display it. You can view the image in 3-D by using red-cyan stereo glasses.

A = stereoAnaglyph(I1,I2); figure imshow(A) title('Red-Cyan composite view of the rectified stereo pair image')

Figure contains an axes object. The axes object with title Red-Cyan composite view of the rectified stereo pair image contains an object of type image.

Convert the rectified input color images to grayscale images.

J1 = rgb2gray(I1); J2 = rgb2gray(I2);

Compute the disparity map through semi-global matching. Specify the range of disparity as [0, 48], and the minimum value of uniqueness as 20.

disparityRange = [0 48]; disparityMap = disparitySGM(J1,J2,'DisparityRange',disparityRange,'UniquenessThreshold',20);

Display the disparity map. Set the display range to the same value as the disparity range.

figure imshow(disparityMap,disparityRange) title('Disparity Map') colormapjetcolorbar

Figure contains an axes object. The axes object with title Disparity Map contains an object of type image.

Input Arguments

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Input image referenced asI1corresponding to camera 1, specified as a 2-D grayscale image or agpuArray(Parallel Computing Toolbox)object. The function uses this image as the reference image for computing the disparity map. The input imagesI1andI2must be real, finite, and nonsparse. Also,I1andI2must be of the same size and same data type.

Data Types:single|double|int16|uint8|uint16

Input image referenced asI2corresponding to camera 2, specified as a 2-D grayscale image or agpuArray(Parallel Computing Toolbox)object. The input imagesI1andI2must be real, finite, and nonsparse.I1andI2must be of the same size and same data type.

Data Types:single|double|int16|uint8|uint16

Name-Value Arguments

Specify optional pairs of arguments asName1=Value1,...,NameN=ValueN, whereNameis the argument name andValueis the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and encloseNamein quotes.

Example:disparitySGM(I1,I2,'DisparityRange',[0 64])

Range of disparity, specified as the comma-separated pair consisting of'DisparityRange'and a two-element vector of the form [MinDisparityMaxDisparity].MinDisparityis the minimum disparity andMaxDisparityis the maximum disparity.

For input images of widthN,MinDisparityandMaxDisparitymust be integers in the range (–N,N). The difference between theMaxDisparityandMinDisparityvalues must be divisible by 8 and must be less than or equal to 128.

The default value for the range of disparity is[0 128]. For more information on choosing the range of disparity, seeChoosing Range of Disparity.

Data Types:integers

Minimum value of uniqueness, specified as the comma-separated pair consisting of'UniquenessThreshold'和一个非negative integer.

The function marks the estimated disparity valueKfor a pixel as unreliable, if:

v<V×(1+0.01×UniquenessThreshold),

whereVis the Hamming distance corresponding to the disparity valueK.vis the smallest Hamming distance value over the whole disparity range, excludingK,K–1, andK+1.

Increasing the value ofUniquenessThresholdresults in disparity values for more pixels being marked as unreliable. To disable the use of uniqueness threshold, set this value to 0.

Output Arguments

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Disparity map for rectified stereo pair image, returned as a 2-D grayscale image or agpuArrayobject. The function returns the disparity map with the same size as input imagesI1andI2. Each value in this output refers to the displacement between conjugate pixels in the stereo pair image. For details about computing the disparity map, seeComputing Disparity Map Using Semi-Global Matching.

Data Types:single

More About

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Image Rectification

The input imagesI1andI2must be rectified before computing the disparity map. The rectification ensures that the corresponding points in the stereo pair image are on the same rows. You can rectify the input stereo pair image by using therectifyStereoImagesfunction. The reference image must be the same for rectification and disparity map computation.

Algorithms

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Choosing Range of Disparity

The range of disparity must be chosen to cover the minimum and the maximum amount of horizontal shift between the corresponding pixels in the rectified stereo pair image. You can determine the approximate horizontal shift values from the stereo anaglyph of the stereo pair image. Compute the stereo anaglyph of the rectified images by using thestereoAnaglyphfunction. Display the stereo anaglyph in theImage Viewerapp. To measure the amount of horizontal shift between the corresponding points in the stereo pair image, selectMeasure Distancefrom theToolsmenu inImage Viewer. Choose the minimum and maximum disparity values for the range of disparity based on this measurement.

For example, this figure displays the stereo anaglyph of a rectified stereo pair image and the horizontal shift values measured between the corresponding points in the stereo pair image. The minimum and maximum shift values are computed as 8 and 31 respectively. Based on these values, the range of disparity can be chosen as [0, 48].

Computing Disparity Map Using Semi-Global Matching

  1. Compute Census transform of the rectified stereo pair image.

  2. Compute Hamming distance between pixels in the census-transformed image to obtain the matching cost matrix.

  3. 从匹配因为计算pixel-wise差距t matrix by using the semi-global matching method given in[1].

  4. Optionally, mark the pixels for unreliability based on theUniquenessThresholdname-value pair. The function sets the disparity values of the unreliable pixels toNaN.

References

[1] Hirschmuller, H. "Accurate and Efficient Stereo Processing by Semi-Global Matching and Mutual Information." InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 807-814. San Diego, CA: IEEE, 2005.

Extended Capabilities

Version History

Introduced in R2019a

See Also

Apps

Functions