Program Structure

Module 1: Introduction to Computer Vision and Image Processing

• Understanding the role of computer vision in AI and automation.
• Overview of image and video data types and formats (e.g., grayscale, RGB, binary).
• Introduction to OpenCV: setting up the environment and basic image operations.
• Hands-on exercise: Loading, displaying, and manipulating images using OpenCV.

Module 2: Image Processing Fundamentals

• Image transformations: resizing, rotation, and flipping images.
• Filtering and smoothing techniques: Gaussian blur, sharpening, and edge detection (Sobel, Canny).
• Histogram equalization and contrast adjustment.
• Hands-on exercise: Applying image filters to enhance image features and reduce noise.

Module 3: Feature Extraction and Object Detection

• Understanding image features: edges, corners, and blobs.
• Techniques for feature extraction: Harris Corner Detection, HOG (Histogram of Oriented Gradients).
• Object detection using classical methods (e.g., Haar cascades) and deep learning-based methods (e.g., YOLO, SSD, Faster R-CNN).
• Hands-on exercise: Implementing object detection in images and video streams.

Module 4: Image Segmentation

• Understanding image segmentation: splitting an image into meaningful regions.
• Thresholding techniques: simple thresholding, adaptive thresholding.
• Region-based segmentation methods: Watershed algorithm, K-means clustering for segmentation.
• Hands-on exercise: Segmenting objects in an image using different segmentation techniques.

Module 5: Machine Learning in Computer Vision

• Using machine learning algorithms for image classification and pattern recognition.
• Feature extraction methods for classification tasks: SIFT, SURF, and ORB.
• Training classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forests, etc.
• Hands-on exercise: Implementing image classification models using Scikit-learn and TensorFlow.

Module 6: Deep Learning for Computer Vision

• Introduction to deep learning and Convolutional Neural Networks (CNNs).
• Building CNNs using TensorFlow and Keras for image classification and feature extraction.
• Transfer learning with pre-trained models (e.g., VGG16, ResNet, Inception) for faster and more accurate results.
• Hands-on exercise: Implementing CNN-based models for image recognition tasks.

Module 7: Video Processing and Tracking

• Techniques for processing video streams: background subtraction, motion detection, and object tracking.
• Tracking objects in real-time using algorithms like Kalman filter, Mean-Shift, and Optical Flow.
• Implementing face detection and tracking systems for video analysis.
• Hands-on exercise: Building a simple real-time object tracking system using OpenCV.

Module 8: Advanced Computer Vision Applications

• Deep learning in advanced vision tasks: image captioning, semantic segmentation, and image generation.
• Generative Adversarial Networks (GANs) for image generation and enhancement.
• Medical image analysis using computer vision: segmentation and classification of medical images.
• Hands-on exercise: Applying deep learning models for advanced computer vision tasks.

Module 9: Deployment of Computer Vision Models

• Deploying computer vision models to production environments (e.g., mobile apps, cloud-based solutions).
• Optimizing model performance: model compression, quantization, and edge deployment.
• Integrating computer vision models into real-time applications (e.g., surveillance, autonomous vehicles, industrial automation).
• Hands-on exercise: Deploying a computer vision model to a cloud environment or edge device.

Final Project

• Design and implement a complete computer vision system for a real-world problem (e.g., facial recognition, object detection, medical image analysis).
• Apply deep learning or traditional computer vision techniques for image classification or segmentation.
• Example projects: Building a face recognition system, detecting defective products on a production line, or classifying medical images.