WSQ Applied Python Programming (SF) (Synchronous and Asynchronous E-Learning)
About This Course
The module "Applied Python Programming" equips learners with essential knowledge and skills in Python programming for computer vision applications. Through the instructional units covered in this module, learners will gain a comprehensive understanding of computer vision concepts and techniques, as well as the ability to apply them effectively using the OpenCV library.
Learners will start with an introduction to computer vision and OpenCV, learning the fundamentals of image processing and analysis. They will acquire knowledge in various image processing techniques, including noise reduction, image enhancement, and feature extraction. Learners will also explore advanced computer vision techniques such as image segmentation and object recognition, further expanding their capabilities in solving complex problems.
The module delves into object detection and tracking, teaching learners how to identify and track objects in images or video streams. They will learn about feature detection and matching, enabling them to identify distinctive features and match them across multiple images. Additionally, learners will gain expertise in image filtering and transformation, equipping them with the skills to manipulate and transform images effectively.
With image analysis with Azure, learners delve into cutting-edge techniques for processing and extracting insights from images using Microsoft Azure's powerful tools. The instructional units cover image classification with Azure, empowering participants to categorize and label images efficiently. Additionally, the module explores the realm of Optical Character Recognition (OCR), providing learners with the skills to extract text information from images.
Upon completing this module, learners will have the knowledge and skills necessary to leverage Python programming for computer vision applications.
What You'll Learn
This course comprises of following Instructional Units
1. Introduction to Computer Vision and OpenCV
2. Image Processing Techniques using OpenCV.
3. Object Detection and Tracking
4. Feature Detection and Matching
5. Image analysis with Azure
6. Image classification with Azure
7. Optical Character Recognition
Knowledge outcomes
Explain the fundamental concepts of computer vision and image processing using OpenCV.
Describe various image processing techniques including noise reduction, image enhancement, and feature extraction.
Summarize advanced computer vision techniques like image segmentation and object recognition.
Identify key methods in object detection and tracking, including feature detection and matching.
Outline the process of image filtering and transformation for effective manipulation of visual data.
Skills Outcomes
Apply Python programming to solve problems in computer vision, utilizing OpenCV for image processing.
Develop skills in noise reduction and image enhancement to improve visual quality in computer vision projects.
Implement object detection and tracking algorithms to identify and monitor objects in images or videos.
Perform feature detection and matching for recognizing distinctive features across multiple images.
Create and execute a real-world computer vision project, integrating learned techniques for practical application.
Entry Requirements
Academic Qualification - Minimum O Level credit in Maths or Minimum one credit in Nitech in STEM
Experience - 2 years experience in Programming or Data analytics