Estimate fundamental matrix from corresponding points in stereo images

`estimateFundamentalMatrix`

example`F = estimateFundamentalMatrix(matchedPoints1,matchedPoints2)`

example`[F,inliersIndex] = estimateFundamentalMatrix(matchedPoints1,matchedPoints2)`

`[F,inliersIndex,status] = estimateFundamentalMatrix(matchedPoints1,matchedPoints2)`

`[F,inliersIndex,status] = estimateFundamentalMatrix(matchedPoints1,matchedPoints2,Name,Value)`

`estimateFundamentalMatrix`

estimates the
fundamental matrix from corresponding points in stereo images. This
function can be configured to use all corresponding points or to exclude
outliers. You can exclude outliers by using a robust estimation technique
such as random-sample consensus (RANSAC). When you use robust estimation,
results may not be identical between runs because of the randomized
nature of the algorithm.

returns
the 3-by-3 fundamental matrix, `F`

= estimateFundamentalMatrix(`matchedPoints1`

,`matchedPoints2`

)`F`

, using the least
median of squares (LMedS) method. The input points can be *M*-by-2
matrices of *M* number of [x y] coordinates, or `SURFPoints`

, `MSERRegions`

, or `cornerPoints`

object.

`[`

returns
logical indices, `F`

,`inliersIndex`

]
= estimateFundamentalMatrix(`matchedPoints1`

,`matchedPoints2`

)`inliersIndex`

, for the inliers
used to compute the fundamental matrix. The `inliersIndex`

output
is an *M*-by-1 vector. The function sets the elements
of the vector to `true`

when the corresponding point
was used to compute the fundamental matrix. The elements are set to `false`

if
they are not used.

`[`

returns
a status code.`F`

,`inliersIndex`

,`status`

]
= estimateFundamentalMatrix(`matchedPoints1`

,`matchedPoints2`

)

`[`

sets
parameters for finding outliers and computing the fundamental matrix `F`

,`inliersIndex`

,`status`

]
= estimateFundamentalMatrix(`matchedPoints1`

,`matchedPoints2`

,`Name,Value`

)`F`

,
with each specified parameter set to the specified value with one
or more comma-separated parameters, specified as name-value pairs.

**Code Generation Support:**

Compile-time
constant input: `Method`

, `OutputClass`

, `DistanceType`

,
and `ReportRuntimeError`

.

Supports MATLAB^{®} Function
block: Yes.

Code Generation Support, Usage Notes, and Limitations

[1] Hartley, R., A. Zisserman, *Multiple
View Geometry in Computer Vision*, Cambridge University
Press, 2003.

[2] Rousseeuw, P., A. Leroy, *Robust
Regression and Outlier Detection*, John Wiley & Sons,
1987.

[3] Torr, P. H. S., and A. Zisserman, *MLESAC:
A New Robust Estimator with Application to Estimating Image Geometry*,
Computer Vision and Image Understanding, 2000.

`detectFASTFeatures`

| `detectHarrisFeatures`

| `detectMinEigenFeatures`

| `detectMSERFeatures`

| `detectSURFFeatures`

| `epipolarline`

| `estimateUncalibratedRectification`

| `extractFeatures`

| `matchFeatures`

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