Executive Development Programme in Image Processing with Python and OpenCV

This course is designed for mid-to-senior level professionals in data science, computer vision, and related fields. It aims to enhance your skills in leveraging Python and OpenCV for efficient image processing. Participants will gain hands-on experience in applying OpenCV functions to real-world problems, understanding image analysis techniques, and developing custom image processing solutions.

By the end of the program, attendees will be proficient in using Python and OpenCV libraries to analyze and manipulate images, ready to implement advanced image processing tasks in their projects or organizations.

1. 1. Introduction to Image Processing: Learners will explore the basics of image processing, including types of images, color models, and key terminologies. They will gain practical skills in loading, saving, and displaying images using Python libraries.
2. 2. Image Preprocessing Techniques: This module covers essential preprocessing steps such as resizing, cropping, and converting images to grayscale or binary formats. Learners will practice these techniques using OpenCV and Python, enhancing image quality for further processing.
3. 3. Image Filtering and Enhancement: Learners will study various filters and techniques to enhance image quality and extract meaningful features. Practical skills include applying Gaussian, median, and bilateral filters to reduce noise and sharpen images.
4. 4. Feature Detection and Extraction: This module introduces learners to feature detection algorithms like Harris corner detection and SIFT. They will learn to identify and extract key features from images, essential for tasks like image matching and recognition.
5. 5. Object Detection and Tracking: Learners will delve into techniques for detecting and tracking objects within images and video sequences. Practical skills include using OpenCV’s object detection methods and training custom models with deep learning frameworks.
6. 6. Image Segmentation: This module covers segmentation techniques to partition an image into multiple segments or regions. Learners will apply thresholding, clustering, and contour detection methods to segment images accurately.
7. 7. Machine Learning in Image Processing: Learners will explore how machine learning algorithms can be applied to image processing tasks. Practical skills include training and evaluating models for image classification, object recognition, and segmentation using Python libraries like scikit-learn and TensorFlow.
8. 8. Deep Learning for Image Processing: This module introduces deep learning techniques for advanced image processing tasks. Learners will practice building and training convolutional neural networks (CNNs) for image classification, detection, and segmentation.
9. 9. Image Augmentation and Data Preparation: Learners will learn how to augment and prepare image data for training deep learning models. Practical skills include techniques like random cropping, flipping, and adding noise to enhance model robustness.
10. 10. Project: Building a Comprehensive Image Processing Pipeline: In this final module, learners will apply all the skills and knowledge gained throughout the programme by developing a comprehensive image processing pipeline. They will design, implement, and present a project that integrates multiple techniques learned in the programme.