COURSE OVERVIEW

This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence Digital Image Processing. It covers the fundamentals of digital image processing, basics of images and pixels and image processing with OpenCV; the image enhancement techniques, morphological image processing and image segmentation basics; the features in image processing, edge detection and contour analysis, feature descriptors and keypoint detection; and the image classification using AI, deep learning for image processing and simple image classifier with TensorFlow/Keras. 

 

Further, the course will also discuss the advanced image segmentation techniques, object detection using AI and convolutional neural networks (CNNs) in image processing; the image augmentation and data preprocessing and image denoising techniques; implementing image processing pipelines for AI applications; the advanced object recognition, image segmentation with deep learning and facial recognition and emotion detection; and the generative adversarial networks (GANs), super-resolution and image enhancement and deep learning for image-to-image translation. 

During this interactive course, participants will learn the AI in medical image processing, AI in autonomous vehicles and surveillance, image processing in robotics and cloud-based AI image processing; the models for mobile and embedded devices and TensorFlow Lite and OpenCV for edge AI; deploying AI models as APIs using Flask/Django; and integrating AI image processing into mobile applications.