• Basic Concept
• Image Filtering Techniques

Module2: Human Visual System

Module3: Feature Detection and Matching Techniques

• Edge Detection
• Corner Detection
• Discriminative Interest Point Detectors
• Interview Questions and Preparation

Module4:Camera Models and 3D Computer Vision

• Camera Calibration
• Stereo Vision
• Generating 3D images from 2D views (exciting, isn’t it?)
• Interview Questions and Preparation

Module5: Machine Learning Fundamentals

• Supervised Learning
• Feature selection and learning
• Classification Techniques
• Generative and Discriminative Models (GANs)
• Semi-supervised Learning
• Unsupervised Learning
• Self-Supervised Learning
• Interview Questions and Preparation

Module6: Deep Learning 

• Introduction to Convolutional Neural Network
• CNN Artchitecture:  DenseNET, Res NET, Fully Convolutional Network
• Interview Questions and Preparation

Module7: Object Detection

• RCNN, Fast-RCNN, Faster RCNN, Mask RCNN
• YOLO, SSD for Object Detection
• Feature Pyramid Networks
• EfficientDet
• Interview Questions and Preparation

Module8:Image Segmentation

• UNET
• Fast FCN
• Gated SCNN
• DeepLab
• Interview Questions and Preparation

 Module9:Object Tracking

• Single and Multiple Object Tracking using deep learning

Module10: Medical Image Diagnosis

• Tissue, bone and muscle segmentation in X-ray, CT and MRI
• Automatic cancer detection in liver, pancreas, brain, kidney, lung and other organ
• Radiologists in the loop systems

Module11Action Recognition

• Review of state-of-the-art action recognition systems

Module12: Applications in Retail and E-Commerce

• Image search, retrieval and recommendation systems

Module13:Autonomous Driving

Module14: Exciting Applications in Astronomy and other fields

Module15:Building a Computer Vision Startup

Module16:Review and Final project