Package: vision
Estimate geometric transformation from matching point pairs
Note:
It is recommended that you use the new 
The GeometricTransformEstimator
object estimates
geometric transformation from matching point pairs and returns the
transform in a matrix. Use this object to compute projective, affine,
or nonreflective similarity transformations with robust statistical
methods, such as, RANSAC and Least Median of Squares.
Use the step syntax below with input points, MATCHED_POINTS1
and MATCHED_POINTS2
,
the object, H
, and any optional properties.
TFORM = step(H,MATCHED_POINTS1,
MATCHED_POINTS2)
calculates the transformation matrix, TFORM
. The input arrays, MATCHED_POINTS1
and MATCHED_POINTS2
specify
the locations of matching points in two images. The points in the
arrays are stored as a set of [x_{1} y_{1}; x_{2 }y_{2};
...; x_{N }y_{N }]
coordinates, where N is the number of points. When
you set the Transform
property to Projective
,
the step
method outputs TFORM
as
a 3by3 matrix. Otherwise, the step
method outputs TFORM
as
a 3by2 matrix.
[TFORM,INLIER_INDEX] = step(H,
...)
additionally outputs the logical vector, INLIER_INDEX
,
indicating which points are the inliers.
H = vision.GeometricTransformEstimator
returns
a geometric transform estimation System object, H
.
This object finds the transformation matrix that maps the largest
number of points between two images.
H = vision.GeometricTransformEstimator(
returns
a geometric transform estimation object, Name
,Value
)H
, with
each specified property set to the specified value. You can specify
additional namevalue pair arguments in any order as (Name1
, Value1
,...,NameN
,ValueN
).

Transformation type Specify transformation type as one of Default: 

Whether to exclude outliers from input points Set this property to Default: 

Method to find outliers Specify the method to find outliers as one of Default: 

Algebraic distance threshold for determining inliers Specify a scalar threshold value for determining inliers as
a positive, scalar value. The threshold controls the upper limit used
to find the algebraic distance for the RANSAC method. This property
applies when you set the Default: 

Distance threshold for determining inliers in pixels Specify the upper limit of the algebraic distance that a point
can differ from the projection location of its associating point.
Set this property as a positive, scalar value. This property applies
when you set the Default: 

How to specify number of random samplings Indicate how to specify number of random samplings as one of Default: 

Number of random samplings Specify the number of random samplings as a positive, integer
value. This property applies when you set the Default: 

Probability to find largest group of points Specify as a percentage, the probability to find the largest
group of points that can be mapped by a transformation matrix. This
property applies when you set the Default: 

Maximum number of random samplings Specify the maximum number of random samplings as a positive,
integer value. This property applies when you set the Default: 

Source of inlier percentage Indicate how to specify the threshold to stop random sampling
when a percentage of input point pairs have been found as inliers.
You can set this property to one of Default: 

Percentage of point pairs to be found to stop random sampling Specify the percentage of point pairs that needs to be determined
as inliers, to stop random sampling. This property applies when you
set the Default: 

Whether to refine transformation matrix Set this property to Default: 

Data type of the transformation matrix Specify transformation matrix data type as one of Default: 
clone  Create geometric transform estimator object with same property values 
getNumInputs  Number of expected inputs to step method 
getNumOutputs  Number of outputs from step method 
isLocked  Locked status for input attributes and nontunable properties 
release  Allow property value and input characteristics changes 
step  Calculate transformation matrix mapping largest number of valid points from input arrays 
The success of estimating the correct geometric transformation
depends heavily on the quality of the input point pairs. If you chose
the RANSAC or LMS algorithm, the block will randomly select point
pairs to compute the transformation matrix and will use the transformation
that best fits the input points. There is a chance that all of the
randomly selected point pairs may contain outliers despite repeated
samplings. In this case, the output transformation matrix, TForm
,
is invalid, indicated by a matrix of zeros.
To improve your results, try the following:
Increase the percentage of inliers in the input points. 
Increase the number for random samplings. 
For the RANSAC method, increase the desired confidence. 
For the LMS method, make sure the input points have 50% or more inliers. 
Use features appropriate for the image contents 
Be aware that repeated patterns, for example, windows in office building, will cause false matches when you match the features. This increases the number of outliers. 
Do not use this function if the images have significant parallax.
You can use the estimateFundamentalMatrix function
instead. 
Choose the minimum transformation for your problem. 
If a projective transformation produces the error message,
"A portion of the input image was transformed to the location
at infinity. Only transformation matrices that do not transform any
part of the image to infinity are supported.", it is usually
caused by a transformation matrix and an image that would result in
an output distortion that does not fit physical reality. If the matrix
was an output of the GeometricTransformationEstimator object,
then most likely it could not find enough inliers. 
This object implements the algorithm, inputs, and outputs described on the Estimate Geometric Transformation block reference page. The object properties correspond to the block parameters.