- shi_tomasi - Shi - Tomasi corner detector.
- harris - Harris corner detector.
- noble - Noble's variant of the Harris corner detector.
- coherence - Compute image coherence from structure tensor
- structuretensor - Compute structure tensor values over an image
- hessianfeatures - Computes determiant of hessian features in an image.
- fastradial - Loy and Zelinski's fast radial feature detector.
Shi - Tomasi corner detector
Usage: cimg = shi_tomasi(img, sigma=1)
(cimg, r, c) = shi_tomasi(img, sigma=1; args...)
Arguments:
img - Image to be processed.
::Array{T,2} or ::AbstractImage{T,2}
sigma - Standard deviation of Gaussian summation window.
Typical values to use might be 1-3. Default is 1.
Keyword arguments:
args... - A sequence of keyword arguments for performing non-maximal suppression.
These are: (see nonmaxsuppts() for more details)
radius - Radius of region considered in non-maximal
suppression. Typical values to use might
be 1-3 pixels. Default (and minimum value) is 1.
thresh - Threshold, only features with value greater than
threshold are returned. Default is 0.
N - Maximum number of features to return. In this case the
N strongest features with value above 'thresh' are
returned. Default is Inf.
subpixel - If set to true features are localised to subpixel
precision. Default is false.
img - Optional image data. If an image is supplied the
detected corners are overlayed on this image. This can
be useful for parameter tuning. Default is [].
Returns:
cimg - Corner strength image.
r - Row coordinates of corner points.
c - Column coordinates of corner points.
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"] so that it is consistent with the coordinates returned in r, c
With no keyword arguments supplied only 'cimg' is returned as a raw corner strength image. You may then want to look at the values within 'cimg' to determine the appropriate threshold value to use.
The Shi - Tomasi measure returns the minimum eigenvalue of the structure tensor. This represents the ideal that the Harris and Noble detectors attempt to approximate. Back in 1988 Harris wanted to avoid the computational cost of taking a square root when computing features. This is no longer relevant today!
See also: harris(), noble(), hessianfeatures(), structuretensor(), nonmaxsuppts(), derivative5()
Harris corner detector.
Usage: cimg = harris(img, sigma=1; k=0.04)
(cimg, r, c) = harris(img, sigma=1; k=0.04, args...)
Arguments:
img - Image to be processed.
::Array{T,2} or ::AbstractImage{T,2}
sigma - Standard deviation of Gaussian summation window.
Typical values to use might be 1-3. Default is 1.
Keyword arguments:
k - The scaling factor to apply to the trace of the
structure tensor. Defaults to the traditional value
of 0.04
args... - A sequence of keyword arguments for performing non-maximal suppression.
These are: (see nonmaxsuppts() for more details)
radius - Radius of region considered in non-maximal
suppression. Typical values to use might
be 1-3 pixels. Default (and minimum value) is 1.
thresh - Threshold, only features with value greater than
threshold are returned. Default is 0.
N - Maximum number of features to return. In this case the
N strongest features with value above 'thresh' are
returned. Default is Inf.
subpixel - If set to true features are localised to subpixel
precision. Default is false.
img - Optional image data. If an image is supplied the
detected corners are overlayed on this image. This can
be useful for parameter tuning. Default is [].
Returns:
cimg - Corner strength image.
r - Row coordinates of corner points.
c - Column coordinates of corner points.
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"] so that it is consistent with the coordinates returned in r, c
With none of the optional nonmaximal keyword arguments specified only
cimg is returned as a raw corner strength image. You may then want
to look at the values within cimg to determine the appropriate
threshold value to use. Note that the Harris corner strength varies
with the intensity gradient raised to the 4th power. Small changes in
input image contrast result in huge changes in the appropriate
threshold.
The Harris measure is det(M) - k*trace^2(M), where k is a parameter you have to set (traditionally k = 0.04) and M is the structure tensor. Use Noble's measure if you wish to avoid the need to set a parameter k. However the Shi-Tomasi measure is probably what you really want.
See also: noble(), shi_tomasi(), hessianfeatures(), nonmaxsuppts(), derivative5()
Noble's variant of the Harris corner detector.
Usage: cimg = noble(img, sigma=1)
(cimg, r, c) = noble(img, sigma=1; args...)
Arguments:
img - Image to be processed.
::Array{T,2} or ::AbstractImage{T,2}
sigma - Standard deviation of Gaussian summation window.
Typical values to use might be 1-3. Default is 1.
Keyword arguments:
args... - A sequence of keyword arguments for performing non-maximal suppression.
These are: (see nonmaxsuppts() for more details)
radius - Radius of region considered in non-maximal
suppression. Typical values to use might
be 1-3 pixels. Default (and minimum value) is 1.
thresh - Threshold, only features with value greater than
threshold are returned. Default is 0.
N - Maximum number of features to return. In this case the
N strongest features with value above 'thresh' are
returned. Default is Inf.
subpixel - If set to true features are localised to subpixel
precision. Default is false.
img - Optional image data. If an image is supplied the
detected corners are overlayed on this image. This can
be useful for parameter tuning. Default is [].
Returns:
cimg - Corner strength image.
r - Row coordinates of corner points.
c - Column coordinates of corner points.
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"] so that it is consistent with the coordinates returned in r, c
With none of the optional keyword arguments specified only cimg is
returned as a raw corner strength image. You may then want to look at
the values within 'cimg' to determine the appropriate threshold value
to use.
Noble's variation of the Harris operator avoids the need to set the
parameter k. However the Shi-Tomasi measure is probably what you
really want.
See also: harris(), shi_tomasi(), hessianfeatures(), nonmaxsuppts(), structuretensor(), derivative5()
Compute image coherence from structure tensor
Usage: cimg = coherence(img, sigma=1)
Arguments:
img - Image to be processed.
::Array{T,2} or ::AbstractImage{T,2}
sigma - Standard deviation of Gaussian summation window.
Typical values to use might be 1-3. Default is 1.
Returns:
cimg - Coherence image.
Coherence is defined as the difference between the eigenvalues of the structure tensor divided by the sum of the eigenvalues, all squared.
((L1-L2)./(L1+L2)).^2
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"].
A small value indicates that the eigenvalues are similar in magnitude and hence the local structure has a strong 2D component.
See also: structuretensor()
Compute structure tensor values over an image
Usage: (Ix2, Iy2, Ixy) = structuretensor(img, sigma=1)
Arguments:
img - Image to be processed ::Array{T,2}
sigma - Standard deviation of Gaussian summation window. Typical
values to use might be 1-3. Default is 1.
Returns:
Ix2 - 2nd moment of gradients in x
Iy2 - 2nd moment of gradients in y
Ixy - 2nd moment of gradients in xy
See also: shi_tomasi(), harris(), noble(), coherence(), derivative5()
Computes determiant of hessian features in an image.
Usage: hdet = hessianfeatures(img, sigma)
Arguments:
img - Greyscale image to be processed.
::Array{T,2} or ::AbstractImage{T,2}
sigma - Defines smoothing scale.
Returns: hdet - Matrix of determinants of Hessian
The local maxima of hdet tend to mark the centres of dark or light blobs. However, the point that gets localised can be dependent on scale. If the blobs you wish to detect are large you will need to use a value of sigma that is comparable in magnitude.
The local minima of hdet is useful for marking the intersection points of a camera calibration checkerboard pattern. These saddle features seem to be more stable under scale variations.
For example to get the 100 strongest saddle features in image img use:
> hdet = hessianfeatures(img, 1) # sigma = 1
> (r, c) = nonmaxsuppts(-hdet, N=100)
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"].
See also: harris(), noble(), shi_tomasi(), derivative5(), nonmaxsuppts()
Loy and Zelinski's fast radial feature detector.
Usage: (S, So) = fastradial(img, radii, alpha, beta)
Arguments:
img - Image to be analysed
::Array{T,2} or ::AbstractImage{T,2}
radii - Array of integer radius values to be processed
suggested radii might be [1 3 5]
alpha - Radial strictness parameter.
1 - slack, accepts features with bilateral symmetry.
2 - a reasonable compromise.
3 - strict, only accepts radial symmetry.
... and you can go higher
beta - Gradient threshold. Gradients below this threshold do
not contribute to symmetry measure, defaults to 0.
Returns S - Symmetry map. Bright points with high symmetry are
marked with large positive values. Dark points of
high symmetry marked with large -ve values.
So - Symmetry map based on orientation only.
To localize points use nonmaxsuppts() on S, -S or abs(S) depending on what you are seeking to find.
A good algorithm for detecting eyes in face images.
If the input is an AbstractImage cimg will be an AbstractImage with property spatialorder set to ["y","x"].
Reference:
Loy, G. Zelinsky, A. Fast radial symmetry for detecting points of
interest. IEEE PAMI, Vol. 25, No. 8, August 2003. pp 959-973.