Course Syllabus
Introduction to Computer Vision and Python
Overview of computer vision principles
Introduction to Python programming for computer vision
Setting up Python development environment
Applications of computer vision in various industries
Basics of digital image processing and representation
Image Processing Techniques
Image enhancement: Histogram equalization, contrast stretching
Filtering operations: Gaussian filter, median filter, convolution
Edge detection: Sobel, Prewitt, Canny edge detectors
Feature detection and extraction (e.g., Harris corner detection, SIFT, SURF)
Feature matching and descriptor techniques
Feature-based image alignment and stitching
Object Detection and Recognition
Introduction to object detection and recognition
Traditional methods for object detection (e.g., Viola-Jones algorithm)
Modern approaches: Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)
Image Processing with OpenCV
Basics of image representation and manipulation
Image enhancement techniques
Filtering and convolution operations
Feature Extraction and Descriptors
Feature detection algorithms (e.g., Harris corner detection)
Feature descriptors (e.g., SIFT, SURF)
Feature matching techniques
Object Detection with OpenCV
Introduction to object detection
Haar cascades for object detection
Template matching and sliding window techniques
Deep Learning for Computer Vision
Introduction to deep learning concepts
Convolutional Neural Networks (CNNs) for image classification
Transfer learning with pre-trained CNNs
Convolutional Neural Networks with TensorFlow
Introduction to TensorFlow and Keras
Building and training CNN models with TensorFlow
Evaluation and testing of CNN models
Image Segmentation
Introduction to image segmentation techniques
Thresholding and contour detection
Watershed algorithm for image segmentation
Advanced Topics in Computer Vision
Optical Character Recognition (OCR) with Tesseract
Face detection and recognition
Hand gesture recognition using computer vision techniques
