COMPUTER VISION COURSE CURRICULUM AND SYLLABUS
Here’s our detailed Computer Vision Course Curriculum and syllabus. We will cover all these topics and more in Computer Vision Courses Syllabus and outline:
- Introduction to Computer Vision
- Basic Concepts
- Image Filtering Techniques
- Human Visual System
- Feature Detection and Matching Techniques
- Edge Detection
- Corner Detection
- Discriminative Interest Point Detectors
- Interview Questions and Preparation
- Camera Models and 3D Computer Vision
- Camera Calibration
- Stereo Vision
- Generating 3D images from 2D views (exciting, isn’t it?)
- Interview Questions and Preparation
- 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
- Supervised Learning
- Deep Learning
- Introduction to Convolutional Neural Network
- CNN Artchitecture: DenseNET, ResNET, Fully Convolutional Networks
- Interview Questions and Preparation
- Object Detection
- RCNN, Fast-RCNN, Faster RCNN, Mask RCNN
- YOLO, SSD for Object Detection
- Feature Pyramid Networks
- EfficientDet
- Interview Questions and Preparation
- Image Segmentation
- UNET
- Fast FCN
- Gated SCNN
- DeepLab
- Interview Questions and Preparation
- Object Tracking
- Single and Multiple Object Tracking using deep learning
- 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 organs
- Radiologists in the loop systems
- Action Recognition
- Review of state-of-the-art action recognition systems
- Applications in Retail and E-Commerce
- Image search, retrieval and recommendation systems
- Autonomous Driving
- Exciting Applications in Astronomy and other fields
- Building a Computer Vision Startup
- Review and Final project