Fundamentals of Digital Image Correlation (DIC)
Every material test relies on sensors to gather accurate data about
the phenomenon under study. The most common quantities of interest in
material testing are stresses and strains. In order to measure these,
sensors record the force and deformation during the test. While force
sensing is often as simple as using a load cell, deformation sensing
can be much less straightforward. Traditionally, deformation was
measured using strain gauges and extensometers. However, these tools
come with significant drawbacks and limitations. Recent advances in
optical measurement techniques provide another way to monitor strain:
Digital Image Correlation (DIC). This article will cover the basics of
DIC, including DIC principles, equipment, algorithms and output.
Principles of Digital Image Correlation (DIC)
Digital Image Correlation is an optical measurement technique. It
uses cameras to sense and record movement happening in the real 3D
world. Fundamentally, a camera is a device that collects information
from light. The exact pattern of light reaching a camera sensor is
what produces images, which are essentially packets of stored
information. Each image tells us what the world looked like within
the camera field of view at the time that the image was taken. The
key to DIC (as well as other optical measurement techniques) is to
realize that the apparent position of objects in a camera image can
be used to measure the actual position of those objects in the real
This is highly intuitive for large-scale, low-precision
measurements. For example, if you see a photograph where two
people appear to be standing and facing each other, you will
probably assume that they were, in fact, standing and facing each
other when the photo was taken. But why stop with such vague
information? Depending on the characteristics of the camera, you
might also be able to estimate the distance between the tips of
their noses to a precision of 0.01 mm.
Similarly, camera images can be used to accurately estimate the
position of tiny features on the surface of a material. Multiple
images can be used to estimate the change in position of those
tiny features over time. In other words, images can be used to
measure material deformation, making it possible to use them as
DIC is the method used to accomplish this. It uses a computer
algorithm which can be broken down into three steps: (1) identify
key points, (2) track the position of key points in a series of
many sequential images, and (3) interpret the motion of the key
points within the images to measure real motion in the physical
Digital Image Correlation (DIC) Equipment and Setup
Since DIC is a type of strain sensing, it requires a testing
apparatus to gather information about. Like a traditional
extensometer, DIC systems can be seen as an accessory for a material
test; all the other testing equipment is still required, including
testing machines, force sensors, and computer processing equipment.
The exact configuration varies depending on the type of test. DIC
introduces the additional limitation that the surface of the
material sample must be visible, but for most types of testing this
is easily achieved.
To understand DIC, it is helpful to think in terms of
input-process-output. DIC algorithms require certain information
as input. The algorithms process the input information in a
certain way, which creates the desired information (strain, etc.)
as output. The main input required by DIC is a series of
These images must be taken sequentially, and they must be taken
from the same location relative to the test sample. Therefore, DIC
requires at least one static camera which can record images of the
surface of interest in a defined measurement sequence (known time
intervals). Though DIC can technically be performed with only a
single camera, additional pieces of equipment are often used to
make obtaining results easier and more accurate.
Camera accessories are one example of this. Most DIC applications
require the use of camera mounting equipment and additional
lighting. Various types of lenses may be used to achieve the best
field of view and distortion properties; these particulars are
beyond the scope of this article.
DIC at FADI-AMT
Another common DIC practice is to use two cameras instead of one.
DIC with one camera is called 2D-DIC, while DIC with two cameras
is known as 3D-DIC (also called Stereo-DIC). With 3D-DIC, paired
(synchronized) cameras record images of the same surface. We can
then compare image pairs taken at the same time to obtain more
information than what can be gleaned from a single camera.
2D-DIC and 3D-DIC each have their advantages. 2D-DIC is less
costly, easier to set up, and easier to use, but requires the
camera to be perpendicular to a planar deforming surface to give
3D-DIC is sometimes more accurate but is more expensive and often
requires lengthy calibration procedures. One major advantage of
3D-DIC is its ability to measure changes in depth. While a single
camera is unable to detect the distance from the camera to any
point of interest, 3D-DIC provides depth perception by comparing
two images captured from different positions.
For material deformation scenarios where the field of deformation
is limited within a flat plane (such as the uniaxial tension
test), the difference between 2D-DIC and 3D-DIC is typically
DIC at FADI-AMT
One more consideration is crucial for DIC measurements: it is
important to ensure that the material sample surface has
distinctive features that can be identified and tracked in
multiple images. A flat, uniformly colored surface would provide
few identifiable features. To prevent this, testing engineers
apply a black and white speckle pattern (also called a stochastic
pattern) to material samples.
This can be achieved quickly and easily by spray-painting the
material surface to a solid white, then applying a top layer of
black paint with a thin mist. The resulting speckle is
high-contrast and non-uniform, making it easy to detect and
identify any point on the surface by tracking a distinctive region
of the paint pattern.
Digital Image Correlation (DIC) Algorithms
Once a series of images has been taken, the first step of DIC is
to identify key points. To do this, DIC software defines a “mesh,”
which is a set of arbitrarily defined points on the material
surface in the image. Convention and convenience dictate that
meshes are regular, which means that the chosen points are evenly
Once these mesh points are chosen for one image, we need a means
to recognize the same points in other images (in other words, to
track them). To do this, we take a small square region around each
point. These regions are called subsets. Each mesh point has its
own subset, with the mesh point at the center.
Because the sample was painted with a speckle pattern, the
appearance of each subset is highly unique. Thus, we can identify
the location of each subset in each image in the series, and from
it we can find the location of the corresponding mesh point
This process is not as straightforward as it might appear because
the material surface (and thus the paint pattern we use to
identify the subset) is deforming during a test. This causes the
subset to appear stretched in later images, which makes it more
difficult to identify.
The heart of DIC algorithms is a mathematical construct called the
correlation criterion (CC). The correlation criterion is a formula
that looks at the difference in appearance between two subsets and
quantifies it with a single number. Comparing two identical subsets
gives a correlation criterion of zero, while comparing very
different subsets gives a large correlation criterion.
DIC algorithms track mesh points by taking the original subset and
looking for the corresponding subset in subsequent images. The
algorithm essentially uses a guess-and-check method, taking a subset
in the current image (the guess) and comparing it with the original
using the correlation criterion (the check). It selects the new
subset with the lowest correlation criterion. This method can
identify corresponding subsets even when the material is greatly
So far, the algorithm has tracked the movement of a set of key
points. However, these mesh point locations still do not represent
strain. The final step in DIC processing is to translate point
locations in images (measured in pixels) to point locations in the
real world (with real distance units). This process is very
different for 2D- and 3D-DIC.
2D-DIC uses a simple scale to convert image pixels into physical
distances. By taking an image of an object with known length (such
as a ruler), the known length is measured in pixels. We can then
take any pixel distance in the image and multiply it by the ratio of
real length units to pixels. This process assumes that the surface
of the material sample is exactly perpendicular to the axis of the
camera, which is never the case in the real world. This inevitably
creates some measurement error.
3D-DIC does not require a perpendicular camera setup, but the
process of converting image distances to real distances is more
complicated. 3D-DIC must be calibrated before use; this involves
capturing images of calibration panels or other well-defined
scaling objects in a range of positions.
Various mathematical calibration processes can then be used to
determine the relative orientation of the cameras. With this
information, it is possible to calculate the real-world 3D
location of any pair of corresponding points in a pair of images
from the two cameras.
This process is mathematically and computationally intensive
compared to the scaling of 2D-DIC, but it is less prone to errors
and provides depth information.
Digital Image Correlation (DIC) Output
Once the DIC algorithm has calculated the real-world displacement of
a set of points on the material surface, determining the strain is
straightforward. As with any other application, the strain between
two points on the material surface is equal to the change in
distance between them divided by the original distance. Thus, for
any pair of mesh points, DIC can determine the strain between them
at any point in the test.
So far, we have been talking about DIC using discrete points (the
mesh). However, DIC is not limited to measuring deformation/strain
only at these points! In fact, DIC can estimate the deformation at
any point on the material surface, even if it falls between mesh
points. This is done by a process called interpolation, which takes
the discrete mesh of displacement values and estimates the
displacement everywhere between them. This creates a continuous
deformation map of the entire surface, allowing us to calculate
strain between any two points on the sample.
Thus, the final product of DIC is a real-time, continuous strain map
over the entire surface of a material testing sample. This provides
a wealth of valuable information about the response of a material to
loading. Though DIC is a little more complicated than using a
traditional strain sensor, the benefits it provides make it a
worthwhile addition to many material tests.
Advantages of Digital Image Correlation (DIC)
Prior to the introduction of DIC, material deformation was usually
measured mechanically. This can be done by attaching a device such
as a strain gauge or an extensometer to a sample. As the sample
deforms, the measuring device stretches with it, producing an
electric signal proportional to the amount of stretching. This
signal represents the magnitude of the material deformation over the
area where the sensor is attached.
While mechanical strain sensors can be very accurate, they come with
important limitations. Mechanical strain sensors are best for
measuring strain along a single axis. This is sometimes permissible
when the test corresponds to uniaxial conditions, but strain gauges
are inadequate for biaxial testing such as bulge and FLC because
they cannot measure strain along multiple axes in the same small
Another hinderance of strain gauges and extensometers is the fact
that they only work locally. A strain gauge can only measure
deformation happening directly underneath its attached surface. An
extensometer can only measure strain that happens between its two
clamping arms. This means that tests which utilize a large material
sample may require mechanical sensors that are large and heavy, or
else multiple sensors attached over the surface of interest. This
can be cost-prohibitive and can interfere with the test in other
Finally, mechanical sensors are subject to physical limitations.
They necessarily take up physical space near the sample, and this
means they can get in the way of other parts of the testing
apparatus. Additionally, some material testing sample geometries are
unsuitable for sensors that clip on or adhere to their surfaces.
All these restrictions are bypassed by DIC. DIC can measure strain
along multiple axes at once, it can measure strain over the entire
sample, and it is not subject to the same physical limitations as
mechanical strain sensors.