Authors :
Chetan Bagade; Dr. Nikita Kulkarni; Dr. Vajid Khan
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2p8e9vxd
DOI :
https://doi.org/10.5281/zenodo.8049693
Abstract :
The purpose of image segmentation methods is
to create analytically relevant subsets of an input image.
Segmentation is typically driven by the input information
and a precondition on the search area; the latter is useful
when the images are damaged or contain artifacts due to
limitations in the image collection technique. It is possible
for image segmentation techniques to make use of prior
knowledge in order to deliver outcomes that are more
accurate and credible. The method known as event-based
imaging makes it possible to recognize occurrences in a
way that is both efficient and helpful by using the
medium of pictures. This is a very sophisticated system
that requires the cognitive categorization of the
components in the picture as well as the proper
recognition of the event. For the purpose of event-based
imaging, there have already a great number of studies
and investigations conducted, all of which have been
created with this particular objective in mind. However,
it has come to everyone's attention that the bulk of the
prevalent researches are unable to independently conduct
the event identification with a significant degree of
accuracy. Therefore, to provide a solution to this problem
this research devises an effective methodology that
utilizes Image normalization, image segmentation and
Channel Boosted Convolutional Neural Networks to
achieve event recognition.
Keywords :
Event based imaging, Image normalization, Image segmentation, Channel Boosted Convolutional Neural networks, Decision Tree.
The purpose of image segmentation methods is
to create analytically relevant subsets of an input image.
Segmentation is typically driven by the input information
and a precondition on the search area; the latter is useful
when the images are damaged or contain artifacts due to
limitations in the image collection technique. It is possible
for image segmentation techniques to make use of prior
knowledge in order to deliver outcomes that are more
accurate and credible. The method known as event-based
imaging makes it possible to recognize occurrences in a
way that is both efficient and helpful by using the
medium of pictures. This is a very sophisticated system
that requires the cognitive categorization of the
components in the picture as well as the proper
recognition of the event. For the purpose of event-based
imaging, there have already a great number of studies
and investigations conducted, all of which have been
created with this particular objective in mind. However,
it has come to everyone's attention that the bulk of the
prevalent researches are unable to independently conduct
the event identification with a significant degree of
accuracy. Therefore, to provide a solution to this problem
this research devises an effective methodology that
utilizes Image normalization, image segmentation and
Channel Boosted Convolutional Neural Networks to
achieve event recognition.
Keywords :
Event based imaging, Image normalization, Image segmentation, Channel Boosted Convolutional Neural networks, Decision Tree.
