`optical_flow_mg`

— Compute the optical flow between two images.

**optical_flow_mg**(*ImageT1*, *ImageT2* : *VectorField* : *Algorithm*, *SmoothingSigma*, *IntegrationSigma*, *FlowSmoothness*, *GradientConstancy*, *MGParamName*, *MGParamValue* : )

`optical_flow_mg`

computes the optical flow between two
images. The optical flow represents information about the movement
between two consecutive images of a monocular image sequence. The
movement in the images can be caused by objects that move in the
world or by a movement of the camera (or both) between the
acquisition of the two images. The projection of these 3D movements
into the 2D image plane is called the optical flow.

The two consecutive images of the image sequence are passed in
* ImageT1* and

`ImageT2`

`VectorField`

`VectorField`

`ImageT1`

`ImageT2`

`ImageT2`

`ImageT1`

`VectorField`

The parameter * Algorithm* allows the selection of three
different algorithms for computing the optical flow. All three
algorithms are implemented by using multigrid solvers to ensure an
efficient solution of the underlying partial differential equations.

For * Algorithm* =

For * Algorithm* =

For * Algorithm* =

In all three algorithms, the input images can first be smoothed by a
Gaussian filter with a standard deviation of * SmoothingSigma*
(see

`derivate_gauss`

).
All three approaches are variational approaches that compute the
optical flow as the minimizer of a suitable energy functional. In
general, the energy functionals have the following form:
where w=(u,v,1) is the optical flow vector
field to be determined (with a time step of 1 in the third
coordinate). The image sequence is regarded as a continuous
function f(x), where
x=(r,c,t) and (r,c) denotes the position
and t the time. Furthermore,
denotes the data term,
while denotes the
smoothness term, and is a regularization
parameter that determines the smoothness of the solution. The
regularization parameter is passed in
* FlowSmoothness*. While the data term encodes assumptions
about the constancy of the object features in consecutive images,
e.g., the constancy of the gray values or the constancy of the first
spatial derivative of the gray values, the smoothness term encodes
assumptions about the (piecewise) smoothness of the solution, i.e.,
the smoothness of the vector field to be determined.

The **FDRIG algorithm** is based on the minimization of an
energy functional that contains the following assumptions:

*Constancy of the gray values*: It is assumed that
corresponding pixels in consecutive images of an image sequence have
the same gray value, i.e., that f(r+u,c+v,t+1) = f(r,c,t). This
can be written more compactly as f(x+w) = f(x) using vector notation.

*Constancy of the spatial gray value derivatives*: It is
assumed that corresponding pixels in consecutive images of an image
sequence additionally have have the same spatial gray value
derivatives, i.e, that
also holds, where
. This can be written more compactly as
. In contrast to the gray value
constancy, the gradient constancy has the advantage that it is
invariant to additive global illumination changes.

*Large displacements*: It is assumed that large
displacements, i.e., displacements larger than one pixel, occur.
Under this assumption, it makes sense to consciously abstain from
using the linearization of the constancy assumptions in the model
that is typically proposed in the literature.

*Statistical robustness in the data term*: To reduce the
influence of outliers, i.e., points that violate the constancy
assumptions, they are penalized in a statistically robust manner,
i.e., the customary non-robust square penalization
is replaced by a
linear penalization via
, where is a
fixed regularization constant.

*Preservation of discontinuities in the flow field I*: The
solution is assumed to be piecewise smooth. While the actual
smoothness is achieved by penalizing the first derivatives of the
flow ,
the use of a statistically robust (linear) penalty function
with
provides the desired preservation of edges in the movement in the
flow field to be determined. This type of smoothness term is called
flow-driven and isotropic.

Taking into account all of the above assumptions, the energy functional of the FDRIG algorithm can be written as

Here, is the regularization parameter passed in
* FlowSmoothness*, while is the gradient
constancy weight passed in

`GradientConstancy`

The **DDRAW algorithm** is based on the minimization of an
energy functional that contains the following assumptions:

*Constancy of the gray values*: It is assumed that
corresponding pixels in consecutive images of an image sequence have
the same gray value, i.e., that f(x+w) = f(x).

*Large displacements*: It is assumed that large
displacements, i.e., displacements larger than one pixel, occur.
Under this assumption, it makes sense to consciously abstain from
using the linearization of the constancy assumptions in the model
that is typically proposed in the literature.

*Statistical robustness in the data term*: To reduce the
influence of outliers, i.e., points that violate the constancy
assumptions, they are penalized in a statistically robust manner,
i.e., the customary non-robust square penalization
is replaced by a
linear penalization via
, where is a
fixed regularization constant.

*Preservation of discontinuities in the flow field II*: The
solution is assumed to be piecewise smooth. In contrast to the
FDRIG algorithm, which allows discontinuities everywhere, the DDRAW
algorithm only allows discontinuities at the edges in the original
image. Here, the local smoothness is controlled in such a way that
the flow field is sharp across image edges, while it is smooth along
the image edges. This type of smoothness term is called data-driven
and anisotropic.

All assumptions of the DDRAW algorithm can be combined into the following energy functional:

where is a normalized projection matrix orthogonal to , for which

holds. This matrix ensures that the smoothness of the flow field is only assumed along the image edges. In contrast, no assumption is made with respect to the smoothness across the image edges, resulting in the fact that discontinuities in the solution may occur across the image edges. In this respect, serves as a regularization parameter that prevents the projection matrix from becoming singular. In contrast to the FDRIG algorithm, there is only one model parameter for the DDRAW algorithm: the regularization parameter . As mentioned above, is described in more detail below.

As for the two approaches described above, the **CLG algorithm**
uses certain assumptions:

*Constancy of the gray values*: It is assumed that
corresponding pixels in consecutive images of an image sequence have
the same gray value, i.e., that f(x+w) = f(x).

*Small displacements*: In contrast to the two approaches
above, it is assumed that only small displacements can occur, i.e.,
displacements in the order of a few pixels. This facilitates a
linearization of the constancy assumptions in the model, and leads
to the approximation , i.e., should hold. Here, denotes the gradient in the spatial as well as the
temporal domain.

*Local constancy of the solution*: Furthermore, it is assumed
that the flow field to be computed is locally constant. This
facilitates the integration of the image data in the data term over
the respective neighborhood of each pixel. This, in turn, increases
the robustness of the algorithm against noise. Mathematically, this
can be achieved by reformulating the quadratic data term as
. By performing a local
Gaussian-weighted integration over a neighborhood specified by the
(passed in * IntegrationSigma*), the following
data term is obtained: . Here, denotes a convolution of the 3x3 matrix
with a Gaussian filter with a standard
deviation of (see

`derivate_gauss`

).
*General smoothness of the flow field*: Finally, the solution
is assumed to be smooth everywhere in the image. This particular
type of smoothness term is called homogeneous.

All of the above assumptions can be combined into the following
energy functional:
The corresponding model parameters are the regularization parameter
as well as the integration scale
(passed in * IntegrationSigma*), which determines the size of
the neighborhood over which to integrate the data term. These two
parameters are described in more detail below.

To compute the optical flow vector field for two consecutive images of an image sequence with the FDRIG, DDRAW, or CLG algorithm, the solution that best fulfills the assumptions of the respective algorithm must be determined. From a mathematical point of view, this means that a minimization of the above energy functionals should be performed. For the FDRIG and DDRAW algorithms, so called coarse-to-fine warping strategies play an important role in this minimization, because they enable the calculation of large displacements. Thus, they are a suitable means to handle the omission of the linearization of the constancy assumptions numerically in these two approaches.

To calculate large displacements, coarse-to-fine warping strategies use two concepts that are closely interlocked: The successive refinement of the problem (coarse-to-fine) and the successive compensation of the current image pair by already computed displacements (warping). Algorithmically, such coarse-to-fine warping strategies can be described as follows:

1. First, both images of the current image pair are zoomed down to a very coarse resolution level.

2. Then, the optical flow vector field is computed on this coarse resolution.

3. The vector field is required on the next resolution level: It is applied there to the second image of the image sequence, i.e., the problem on the finer resolution level is compensated by the already computed optical flow field. This step is also known as warping.

4. The modified problem (difference problem) is now solved on the finer resolution level, i.e., the optical flow vector field is computed there.

5. The steps 3-4 are repeated until the finest resolution level is reached.

6. The final result is computed by adding up the vector fields from all resolution levels.

This incremental computation of the optical flow vector field has the following advantage: While the coarse-to-fine strategy ensures that the displacements on the finest resolution level are very small, the warping strategy ensures that the displacements remain small for the incremental displacements (optical flow vector fields of the difference problems). Since small displacements can be computed much more accurately than larger displacements, the accuracy of the results typically increases significantly by using such a coarse-to-fine warping strategy. However, instead of having to solve a single correspondence problem, an entire hierarchy of these problems must now be solved. For the CLG algorithm, such a coarse-to-fine warping strategy is unnecessary since the model already assumes small displacements.

The maximum number of resolution levels (warping levels), the resolution ratio between two consecutive resolution levels, as well as the finest resolution level can be specified for the FDRIG as well as the DDRAW algorithm. Details can be found below.

The minimization of functionals is mathematically very closely related to the minimization of functions: Like the fact that the zero crossing of the first derivative is a necessary condition for the minimum of a function, the fulfillment of the so called Euler-Lagrange equations is a necessary condition for the minimizing function of a functional (the minimizing function corresponds to the desired optical flow vector field in this case). The Euler-Lagrange equations are partial differential equations. By discretizing these Euler-Lagrange equations using finite differences, large sparse nonlinear equation systems result for the FDRIG and DDRAW algorithms. Because coarse-to-fine warping strategies are used, such an equation system must be solved for each resolution level, i.e., for each warping level. For the CLG algorithm, a single sparse linear equation system must be solved.

To ensure that the above nonlinear equation systems can be solved efficiently, the FDRIG and DDRAW use bidirectional multigrid methods. From a numerical point of view, these strategies are among the fastest methods for solving large linear and nonlinear equation systems. In contrast to conventional non-hierarchical iterative methods, e.g., the different linear and nonlinear Gauss-Seidel variants, the multigrid methods have the advantage that corrections to the solution can be determined efficiently on coarser resolution levels. This, in turn, leads to a significantly faster convergence. The basic idea of multigrid methods additionally consists of hierarchically computing these correction steps, i.e., the computation of the error on a coarser resolution level itself uses the same strategy and efficiently computes its error (i.e., the error of the error) by correction steps on an even coarser resolution level. Depending on whether one or two error correction steps are performed per cycle, a so called V or W cycle is obtained. The corresponding strategies for stepping through the resolution hierarchy are as follows for two to four resolution levels: Here, iterations on the original problem are denoted by large markers, while small markers denote iterations on error correction problems.

Algorithmically, a correction cycle can be described as follows:

1. In the first step, several (few) iterations using an interactive linear or nonlinear basic solver are performed (e.g., a variant of the Gauss-Seidel solver). This step is called pre-relaxation step.

2. In the second step, the current error is computed to correct the current solution (the solution after step 1). For efficiency reasons, the error is calculated on a coarser resolution level. This step, which can be performed iteratively several times, is called coarse grid correction step.

3. In a final step, again several (few) iterations using the interactive linear or nonlinear basic solver of step 1 are performed. This step is called post-relaxation step.

In addition, the solution can be initialized in a hierarchical manner. Starting from a very coarse variant of the original (non)linear equation system, the solution is successively refined. To do so, interpolated solutions of coarser variants of the equation system are used as the initialization of the next finer variant. On each resolution level itself, the V or W cycles described above are used to efficiently solve the (non)linear equation system on that resolution level. The corresponding multigrid methods are called full multigrid methods in the literature. The full multigrid algorithm can be visualized as follows: This example represents a full multigrid algorithm that uses two W correction cycles per resolution level of the hierarchical initialization. The interpolation steps of the solution from one resolution level to the next are denoted by i and the two W correction cycles by and . Iterations on the original problem are denoted by large markers, while small markers denote iterations on error correction problems.

In the multigrid implementation of the FDRIG, DDRAW, and CLG algorithm, the following parameters can be set: whether a hierarchical initialization is performed; the number of coarse grid correction steps; the maximum number of correction levels (resolution levels); the number of pre-relaxation steps; the number of post-relaxation steps. These parameters are described in more detail below.

The basic solver for the FDRIG algorithm is a point-coupled fixed-point variant of the linear Gauss-Seidel algorithm. The basic solver for the DDRAW algorithm is an alternating line-coupled fixed-point variant of the same type. The number of fixed-point steps can be specified for both algorithms with a further parameter. The basic solver for the CLG algorithm is a point-coupled linear Gauss-Seidel algorithm. The transfer of the data between the different resolution levels is performed by area-based interpolation and area-based averaging, respectively.

After the algorithms have been described, the effects of the individual parameters are discussed in the following.

The input images, along with their domains (regions of interest) are
passed in * ImageT1* and

`ImageT2`

`VectorField`

`ImageT1`

`ImageT2`

`VectorField`

`ImageT1`

`ImageT2`

* SmoothingSigma* specifies the standard deviation of the
Gaussian kernel that is used to smooth both input images. The
larger the value of

`SmoothingSigma`

`SmoothingSigma`

`SmoothingSigma`

* IntegrationSigma* specifies the standard deviation
of the Gaussian kernel that is
used for the local integration of the neighborhood information of
the data term. This parameter is used only in the CLG algorithm and
has no effect on the other two algorithms. Usually,

`IntegrationSigma`

`IntegrationSigma`

* FlowSmoothness* specifies the weight of
the smoothness term with respect to the data term. The larger the
value of

`FlowSmoothness`

`FlowSmoothness`

`FlowSmoothness`

* GradientConstancy* specifies the weight
of the gradient constancy with respect to the gray value constancy.
This parameter is used only in the FDRIG algorithm. For the other
two algorithms, it does not influence the results. For byte images
with a gray value range of [0,255], a value of

`GradientConstancy`

`GradientConstancy`

`FlowSmoothness`

The parameters of the multigrid solver and for the coarse-to-fine
warping strategy can be specified with the generic parameters
* MGParamName* and

`MGParamValue`

`MGParamName`

`MGParamValue`

`MGParamName`

`MGParamValue`

`MGParamName`

`MGParamValue`

* MGParamName* =

* MGParamName* =

* MGParamName* =

The three parameters that specify the coarse-to-fine warping strategy are only used in the FDRIG and DDRAW algorithms. They are ignored for the CLG algorithm.

* MGParamName* =

* MGParamName* =

* MGParamName* =

* MGParamName* =

* MGParamName* =

* MGParamName* =

Like when increasing the number of correction cycles, increasing the number of pre- and post-relaxation steps increases the computation time asymptotically linearly. However, no additional restriction and prolongation operations (zooming down and up of the error correction images) are performed. Consequently, a moderate increase in the number of relaxation steps only leads to a slight increase in the computation times.

* MGParamName* =

* MGParamName* =

As described above, usually it is sufficient to use one of the
default parameter sets for the parameters described above by using
* MGParamName* =

`MGParamValue`

`MGParamName`

`MGParamValue`

`MGParamName`

`MGParamValue`

The default parameter sets use the following values for the above parameters:

*'default_parameters'* = *'very_accurate'*:
*'warp_zoom_factor'* = 0.5, *'warp_levels'* = 0,
*'warp_last_level'* = 1, *'mg_solver'* =
*'full_multigrid'*, *'mg_cycle_type'* =
*'w'*, *'mg_levels'* = 0, *'mg_cycles'* =
1, *'mg_pre_relax'* = 2, *'mg_post_relax'* = 2,
*'mg_inner_iter'* = 1.

*'default_parameters'* = *'accurate'*:
*'warp_zoom_factor'* = 0.5, *'warp_levels'* = 0,
*'warp_last_level'* = 1, *'mg_solver'* =
*'multigrid'*, *'mg_cycle_type'* = *'v'*,
*'mg_levels'* = 0, *'mg_cycles'* = 1,
*'mg_pre_relax'* = 1, *'mg_post_relax'* = 1,
*'mg_inner_iter'* = 1.

*'default_parameters'* = *'fast_accurate'*:
*'warp_zoom_factor'* = 0.5, *'warp_levels'* = 0,
*'warp_last_level'* = 2, *'mg_solver'* =
*'full_multigrid'*, *'mg_cycle_type'* =
*'w'*, *'mg_levels'* = 0, *'mg_cycles'* =
1, *'mg_pre_relax'* = 2, *'mg_post_relax'* = 2,
*'mg_inner_iter'* = 1.

*'default_parameters'* = *'fast'*:
*'warp_zoom_factor'* = 0.5, *'warp_levels'* = 0,
*'warp_last_level'* = 2, *'mg_solver'* =
*'multigrid'*, *'mg_cycle_type'* = *'v'*,
*'mg_levels'* = 0, *'mg_cycles'* = 1,
*'mg_pre_relax'* = 1, *'mg_post_relax'* = 1,
*'mg_inner_iter'* = 1.

It should be noted that for the CLG algorithm the two modes
*'fast_accurate'* and *'fast'* are identical to the
modes *'very_accurate'* and *'accurate'* since the CLG
algorithm does not use a coarse-to-fine warping strategy.

- Multithreading type: reentrant (runs in parallel with non-exclusive operators).
- Multithreading scope: global (may be called from any thread).
- Automatically parallelized on tuple level.
- Automatically parallelized on internal data level.

`ImageT1`

`→`

object (byte / uint2 / real)
Input image 1.

`ImageT2`

`→`

object (byte / uint2 / real)
Input image 2.

`VectorField`

`→`

object (vector_field)
Optical flow.

`Algorithm`

`→`

(string)
Algorithm for computing the optical flow.

Default: 'fdrig'

List of values: 'clg' , 'ddraw' , 'fdrig'

`SmoothingSigma`

`→`

(real)
Standard deviation for initial Gaussian smoothing.

Default: 0.8

Suggested values: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0

Restriction:
`SmoothingSigma >= 0.0`

`IntegrationSigma`

`→`

(real)
Standard deviation of the integration filter.

Default: 1.0

Suggested values: 0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0

Restriction:
`IntegrationSigma >= 0.0`

`FlowSmoothness`

`→`

(real)
Weight of the smoothing term relative to the data term.

Default: 20.0

Suggested values: 10.0, 20.0, 30.0, 50.0, 70.0, 100.0, 200.0, 300.0, 500.0, 700.0, 1000.0, 1500.0, 2000.0

Restriction:
`FlowSmoothness >= 0.0`

`GradientConstancy`

`→`

(real)
Weight of the gradient constancy relative to the gray value constancy.

Default: 5.0

Suggested values: 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 70.0, 100.0

Restriction:
`GradientConstancy >= 0.0`

`MGParamName`

`→`

(string)
Parameter name(s) for the multigrid algorithm.

Default: 'default_parameters'

List of values: 'default_parameters' , 'mg_cycle_type' , 'mg_cycles' , 'mg_inner_iter' , 'mg_levels' , 'mg_post_relax' , 'mg_pre_relax' , 'mg_solver' , 'parallel_solver' , 'warp_last_level' , 'warp_levels' , 'warp_zoom_factor'

`MGParamValue`

`→`

(string / real / integer)
Parameter value(s) for the multigrid algorithm.

Default: 'accurate'

Suggested values: 'very_accurate' , 'accurate' , 'fast_accurate' , 'fast' , 'multigrid' , 'full_multigrid' , 'v' , 'w' , 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 'true' , 'false'

grab_image (ImageT1, AcqHandle) while (true) grab_image (ImageT2, AcqHandle) optical_flow_mg (ImageT1, ImageT2, VectorField, 'fdrig', 0.8, 1, 10, \ 5, 'default_parameters', 'accurate') threshold (VectorField, Region, 1, 10000) copy_obj (ImageT2, ImageT1, 1, 1) endwhile

If the parameter values are correct, the operator
`optical_flow_mg`

returns the value `2` (
`H_MSG_TRUE`)
. If the input is
empty (no input images are available) the behavior can be set via
`set_system('no_object_result',<Result>)`

. If necessary, an
exception is raised.

`threshold`

,
`vector_field_length`

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert: High accuracy
optical flow estimation based on a theory for warping. In T. Pajdla
and J. Matas, editors, Computer Vision - ECCV 2004, volume 3024 of
Lecture Notes in Computer Science, pages 25--36. Springer, Berlin,
2004.

A. Bruhn, J. Weickert, C. Feddern, T. Kohlberger, and C. Schnörr:
Variational optical flow computation in real-time. IEEE
Transactions on Image Processing, 14(5):608-615, May 2005.

H.-H. Nagel and W. Enkelmann: An investigation of smoothness
constraints for the estimation of displacement vector fields from
image sequences. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 8(5):565-593, September 1986.

Ulrich Trottenberg, Cornelis Oosterlee, Anton Schüller: Multigrid.
Academic Press, Inc., San Diego, 2000.

Foundation