Course Breakdown

Module 1: Getting Started with Computer Vision

• Get a solid foundation in the world of computer vision and image processing.
• Learn how digital images are structured and how they’re represented in the computer.
• Discover basic image processing techniques like scaling, rotating, and transforming images.
• Practical exercise: Load and display images in Python using libraries like OpenCV.

Module 2: Image Enhancement and Preprocessing

• Learn how to improve the quality of images by adjusting contrast, eliminating noise, and applying filters.
• Understand the importance of preprocessing for machine learning applications.
• Experiment with edge detection methods such as Sobel and Canny filters.
• Practical exercise: Enhance and apply filters to sample images to prepare them for analysis.

Module 3: Extracting Features and Detecting Objects

• Master feature extraction techniques like corner detection, SIFT, and HOG to identify important aspects of images.
• Explore methods for detecting objects, from simple template matching to more sophisticated deep learning models like R-CNN.
• Practical exercise: Detect objects in images using feature extraction and deep learning techniques.

Module 4: Deep Learning for Image Classification

• Discover how Convolutional Neural Networks (CNNs) are used to classify images and perform object recognition.
• Train your own CNN models using popular frameworks like TensorFlow and Keras.
• Learn how to apply transfer learning to leverage pre-trained models for custom tasks.
• Practical exercise: Build and train your own image classification model using real-world data.

Module 5: Advanced Techniques in Computer Vision

• Explore cutting-edge techniques like semantic segmentation, where each pixel in an image is labeled with a class.
• Learn about instance segmentation and how to differentiate individual objects in a scene.
• Apply Optical Character Recognition (OCR) for text extraction from images.
• Practical exercise: Use a pre-trained deep learning model to segment and classify objects in an image.

Module 6: Real-World Applications: From Surveillance to Healthcare

• Understand the role of computer vision in medical imaging, from diagnosing diseases to analyzing X-rays and MRIs.
• Explore the use of computer vision in surveillance, facial recognition, and security systems.
• Learn how motion tracking and video analysis can be used for robotics and autonomous vehicles.
• Practical exercise: Apply object tracking techniques in live video streams using OpenCV.

Module 7: Optimizing and Deploying Your Vision Models

• Understand how to optimize computer vision models for real-time applications and efficient performance.
• Learn about deploying models on edge devices, such as mobile phones or embedded systems.
• Explore cloud-based deployment strategies for scalable computer vision solutions.
• Practical exercise: Deploy a computer vision model for real-time object detection on an embedded device.

Final Project: Bringing It All Together

• Design and develop a computer vision solution for a real-world problem, like building a facial recognition system or automating medical image analysis.
• Integrate what you’ve learned into a functional system that can be applied in your own projects or industry.
• Present your project to the class, showcasing your approach, models used, and the impact it can make.