Surfaces of industrial parts need to be specified based on their utility and application environment. Since the quality of surface influences the suitability of components for a specific application, more attention had been given by researchers to measure the surface quality accurately. Current techniques of quantifying surface quality use profilometers, coordinate measuring machines and some optical techniques. With the advent of automation, surface characterization needs to be totally computerized so that the task of inspection (of surfaces) is greatly simplified and free from human error. In this research paper a methodology is presented that uses a computer vision system to characterize the nature of the surface. Computerized optical microscope will be used to acquire the images of the surface and the same images will be fed into MATLAB software for further investigations. The advantages of using a vision system over other techniques will be adequately discussed.
In this pandemic, it is getting more and more difficult
to keep a track of people who are wearing masks regularly or not.
It cannot solely depend on human efforts to take care of this task
and therefore there is a need to develop software that can
automatically detect whether any given person is wearing a mask
or not. Face Detection has evolved as a really popular problem in
image processing and computer vision. Many new algorithms are
being devised using convolutional architectures to form the
algorithm as accurately as possible. These convolutional
architectures have made it possible to extract even the pixel
details. Training is performed through Fully Convolutional
Neural Networks to semantically segment out the faces present in
that image. Feature detection and feature extraction techniques
help us identify whether a person is wearing a mask or not. The
face mask detector will use a dataset of morphed masked images.
Therefore, the created model will be accurate and it will also be
computationally efficient and easily deployable in embedded
systems since the MobileNetV2 architecture will be incorporated
(Raspberry Pi, Google Coral, etc.). This framework can also be
used in real-time applications that, due to the outbreak of
Covid-19, require face-mask detection for safety purposes. This
project can be merged with embedded application systems at
airports, train stations, workplaces, schools, and public places to
ensure compliance with the guidelines for public safety. The
above topic is very prominent in recent times as the identification
process will not only help us classify individuals but also will
reduce the workforce required to do the same exponentially.
In this paper an effective algorithm for noise removal in an image is obtained by using PCA (principal component analysis) with LPG (Local Pixel Grouping
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