Machine Learning for Image processing applications
Under this topic, the need of image processing and how machine learning can help in realizing those benefits quickly were discussed. Both machine learning under the AI discipline and image processing are highly in demand and their uses are constantly being researched to utilize the capabilities of the two. The image processing can either be analogue or digital, but with a large number of images and less time in hand, it becomes tedious to perform it successfully. Through machine learning, the same results can be developed, in fact better results, at a much faster rate.
Few Points Which Were Discussed In The Thesis Developed
a) Introduction to the research topic and its effective benefits in saving time as well as efforts.
b) Explaining the use of machine learning in image visualization, classification, sharpening, restoration, instance segmentation, object measurement and pattern recognition.
c) The scope and increasing need of image processing in the Healthcare Industry, Defense, Automobile Industry and Agriculture for the advancements in these areas.
d) Usage of deep neural networks, recognizing hand gestures and deep learning in image recognition, retrieval and segmentation.
e) Data Analysis of the benefits of image processing to experts from both academic and corporate perspectives.
f) The scope and limitations of the technological advancements in this area and questions which need to be addressed.
Tools We Use For Image Processing
OpenCV
VGG Image Annotator
TensorFlow
MATLAB Image Processing Toolbox
PyTorch
Microsoft Computer Vision
Google Colaboratory (Colab)
Google Cloud Vision
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