Machine Learning/AI for Augmented Reality

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Technology Roadmap Sections and Deliverables

2AIAR - Computer-Aided Detection Leveraging Machine Learning and Augmented Reality

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Roadmap Overview The working principle and architecture of Machine Learning/AI for Augmented Reality is depicted in the below.

Section 1.JPG

This technology uses Computer Aided Detection (CAD) with Faster R-CNN deep learning model, leveraging Augmented Reality (AR) for a unique 3D experience. This experience increases the accuracy of interpretation and therefore, proper actions versus what is available today, which is the simple CAD using high resolution image processing. Faster R-CNN9 is based on a convolutional neural network with additional components for detecting, localizing and classifying objects in an image. Faster R-CNN has a branch of convolutional layers, called Region Proposal Network (RPN), on top of the last convolutional layer of the original network, which is trained to detect and localize objects on the image, regardless of the class of the object. The differentiation of this model is how it optimizes both the object detection and classifier part of the model at the same time.

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