Computer Vision & Image Processing
Learn Computer Vision & Image Processing with practical skills in OpenCV, image processing, CNNs, object detection, segmentation, and real-world AI applications.
Course Overview
Course Modules Module 1: Image Processing Fundamentals Digital Image Basics Color Spaces Image Transformations Filtering & Convolution Edge Detection OpenCV Library Module 2: Classical Computer Vision Feature Detection (SIFT, SURF, ORB) Object Detection Basics Template Matching Contour Analysis Optical Flow Camera Calibration Module 3: Deep Learning for Computer Vision CNN Architecture Image Classification Transfer Learning ResNet & EfficientNet Data Augmentation Fine-tuning Models Module 4: Object Detection & Segmentation YOLO Architecture Faster R-CNN Semantic Segmentation Instance Segmentation Mask R-CNN Detection Metrics Module 5: Advanced CV Applications Face Recognition Pose Estimation OCR & Document Analysis Video Analytics Medical Imaging Generative Models (GANs) Module 6: Capstone Project Problem Definition Dataset Preparation Model Development Training & Evaluation Optimization Deployment API Capstone Project Build and deploy a computer vision application such as an object detection system or image classification API.
Learning Outcomes
Implement image processing techniques using OpenCV.
Build CNN models for image classification and detection.
Apply object detection with YOLO and Faster R-CNN.
Develop segmentation and recognition systems.
Deploy computer vision models in production.
Prerequisites
Participants should have: Python programming proficiency Foundational deep learning knowledge Linear algebra and calculus basics
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