Introduction
The problems of tracking and registration both deal with multiple
images and are important to a wide variety of applications. It can
also be important to combine the processes of tracking or registration
and segmentation so that information about more than position and
velocity can be obtained.
In automated segmentation and tracking of objects in image sequences, the
objects can be either user specified (by the operator pointing and clicking
at the beginning of the sequence), or automatically specified (by
custom-developed algorithms which define the objects to be tracked).
Examples of custom tracking applications are tracking cells in
turbulent flow and machine control. The
tracking of user specified objects, using our generic tracking software
Vedda, is illustrated in a fish tracking
example. Our registration algorithms are applied to 2D gels.
Custom tracking and segmentation algorithms
The images in this example illustrate a complex problem --
tracking and segmenting a large number of objects in close proximity
that are not moving in a consistent manner. The objects in these
images are blood platelet cells stained with fluorescent dye that are pumped
through a thin chamber. By tracking and
segmenting all platelets we are able to get information about size,
brightness, velocity and position which can be used to characterize
what are obviously complex scenes.
The left hand side shows the input images while the right shows
the segmentation and tracking results. Each successfully tracked object
is labelled with a number, and each number is maintained as long as
the object is successfully tracked.
Click on the image to retrieve the complete sequence (8MB).
A second application is in control of machines for automation of
complex tasks. In this example a video signal from an ultrasound
sensor was processed (using a specially designed detection and tracking procedure)
in order to provide control information to a
robotic saw. This problem is complex for a number of reasons
- Ultrasound images are inherently noisy.
- Processing needed to be fast (at least 10 frames per second) to provide control signals for the saw to move at sufficient speed.
- The objects (cattle carcasses) vary significantly so it is desirable to minimize the amount of prior (hardcoded) knowledge used by the system.
The sequence below shows the results from a single run, with the
straight line indicating the position to which the saw is being
guided. The system is attempting to guide the saw down a bone that is
indicated by the darker region in the ultrasound image.
Click on the images to download the sequence.
 General tracking and segmentation
- using Vedda
The previous examples illustrate a custom tracking and
segmentation system. On some occasions it is useful to be able to
track and segment in a semi-interactive fashion using our generic tracking
package, Vedda. This avoids the
need to customize the procedure for locating the objects initially. The user
indicates objects that need to be
tracked and the Vedda software then tracks and segments the objects
automatically. The results are position and shape information. This has wide
application as it is not strongly dependent on models describing the shape
or motion of the objects being tracked.
The objects in these examples are larval fish. Information of the type
produced by the tracking and segmentation process allows behaviour of
the fish to be analysed. In this particular example the information
was used to develop an
understanding of the swimming ability of larval fish to improve
management of the Great Barrier Reef. The shape information was
used to detect feeding behaviour.
Click on the image to retrieve the complete sequence (1.5MB).
Registration
Registration is a process that aims to align two or more images,
rather than identify corresponding objects between
images. Registration is commonly used in medical imagery to compare
different patients, or the same patient before and after treatment. It is also closely related to the sort of processing that is necessary in stereo reconstruction.
In some situations the ultimate aim is to identify corresponding
objects in images, but the objects themselves are not obviously
similar. In these situations a registration step can make the problem
more tractable. An interesting example is registration of
two
dimensional electrophoresis gels.
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