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Conclusions

The Hough transform has attracted significant research interest over decades due to its noise immunity, ability to handle occlusion, and expandability. Many variations have been developed to detect different shapes from lines to irregular shapes. Research is expected to continue developing versions that can recognize more complex objects. While originally applied to binary images, work is increasingly being done on gray and color images to preserve more information content. The Hough transform has been used in many fields like traffic, biometrics, object recognition, tracking, medicine, industry and more, and parallel processing may enable more time-critical applications. The evolution of the transform is expected to continue given its successful history.

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50 views4 pages

Conclusions

The Hough transform has attracted significant research interest over decades due to its noise immunity, ability to handle occlusion, and expandability. Many variations have been developed to detect different shapes from lines to irregular shapes. Research is expected to continue developing versions that can recognize more complex objects. While originally applied to binary images, work is increasingly being done on gray and color images to preserve more information content. The Hough transform has been used in many fields like traffic, biometrics, object recognition, tracking, medicine, industry and more, and parallel processing may enable more time-critical applications. The evolution of the transform is expected to continue given its successful history.

Uploaded by

over related2
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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6.

Conclusions
The Hough transform has attracted a
lot of research efforts
over the decades. The main
motivations behind such interest
are the noise immunity, the ability
to deal with occlusion,
and the expandability of the transform.
Many variations of it
have evolved. They cover a whole
spectrum of shape
detection from lines to irregular
shapes. New variations are
expected to appear moving the
transform closer towards the
recognition of more complex objects.
Mostly, the transform
and its variants have been applied
to binary images.
However, this is changing; we have
shown some work done
directly on gray and color images.
We expect that more
work will be done on color images
maintaining most of the
information content. This will be
promoted with the
persistent research efforts done for
more memory-efficient
and speedy implementations.
Nowadays, numerous
applications have made use of the
Hough transform in many
fields such as traffic, biometrics,
object recognition and
tracking, medical applications,
industrial and commercial
applications, and there is room for
unconventional ones. For
future, we expect that the parallel
processing especially on
GPUs will help more time-critical
applications to emerge.
The evolution of the transform will
keep going. As the
transform has had a fruitful history, it
has a good chance of a
bright future for decades to come.
Preface Automatic object recognition has become an established discipline inside image analysis.
Moments and moment invariants play a very important role as features in invariant recognition.

6. Conclusions
The Hough transform has attracted a lot of research efforts over the decades. The main motivations
behind such interest are the noise immunity, the ability to deal with occlusion, and the expandability of
the transform. Many variations of it have evolved. They cover a whole spectrum of shape detection from
lines to irregular shapes. New variations are expected to appear moving the transform closer towards
the recognition of more complex objects. Mostly, the transform and its variants have been applied to
binary images. However, this is changing; we have shown some work done directly on gray and color
images. We expect that more work will be done on color images maintaining most of the information
content. This will be promoted with the persistent research efforts done for more memory-efficient and
speedy implementations. Nowadays, numerous applications have made use of the Hough transform in
many fields such as traffic, biometrics, object recognition and tracking, medical applications, industrial
and commercial applications, and there is room for unconventional ones. For future, we expect that the
parallel processing especially on GPUs will help more time-critical applications to emerge. The evolution
of the transform will keep going. As the transform has had a fruitful history, it has a good chance of a
bright future for decades to come.

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