Computer Vision Theory and Projects in Python for Beginners
Explore the world of Computer Vision and learn to master it using Python, OpenCV, Tensor Flow, and others
Development,Data Science,Computer Vision
Lectures -320
Duration -26.5 hours
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Course Description
The high-quality content of the Mastering Computer Vision from the Absolute Beginning Using Python course presents you with a great opportunity to learn and become an expert. You will learn the core concepts of the CV field. This course will also help you understand the digital imaging process and identify the key application areas of CV. The course is easy to understand, descriptive, comprehensive, practical with live coding, and rich with state-of-the-art and updated knowledge of this field.
Although this course is a compilation of all the basic concepts of CV, you are encouraged to step up and experience more than what you learn. Your understanding of every concept is tested at the end of each section. The homework assignments/tasks/activities/quizzes along with solutions will assess your learning. Several of these activities are focused on coding so that you are ready to run with implementations.
The two hands-on projects in the last section—Change Detection in CCTV Cameras (Real-Time) and Smart DVRs (Real-Time)—make up the most important learning element of this course. They will help you sharpen your practical skills. Successful completion of these two projects will help you enrich your portfolio and kick-start your career in the CV field.
By the end of the course, you will have a strong understanding of Computer Vision concepts and will be ready to apply them in your future projects.
Code bundles are available here:
https://github.com/PacktPublishing/Computer-Vision-Theory-and-Projects-in-Python-for-Beginners
Audience :
This course is useful for data scientists, machine learning experts, and learners who are absolute beginners and know nothing about Computer Vision, and for people who want to learn Computer Vision with real data along with its implementation in realistic projects.
Goals
What will you learn in this course:
- Learn the concept of colored and black and white images with practice.
- Know the theory and implementation of panoramic images.
- Learn image filtering with implementation in Python.
- Implement any project from scratch that requires Computer Vision knowledge.
- Apply edge detection, shape detection, and corner detection.Â
- Develop a project to make a very intelligent and efficient DVR using Python.
Prerequisites
What are the prerequisites for this course?
- No prior experience required.
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Course and Instructor
6 Lectures
- Introduction to the Course 02:02 02:02
- Introduction to Instructor 03:58 03:58
- About AI Sciences 01:30 01:30
- Course Outline (Optional) 12:31 12:31
- Computer Vision Applications 08:35 08:35
- Final Project 03:42 03:42
Introduction to Images
22 Lectures
2D Scaling Transformations
37 Lectures
2D Geometric Transformations
40 Lectures
Geometric Transformation Estimation (Panorama)
28 Lectures
Binary Morphology
23 Lectures
Image Filtering
10 Lectures
Canny Edge Detector
36 Lectures
Shape Detection
26 Lectures
Shape Detection Hough Transform
15 Lectures
Corner Detection
20 Lectures
Automatic Panorama SIFT
5 Lectures
Object Detection
9 Lectures
YOLO Object Detector
12 Lectures
Motion
3 Lectures
Object Tracking
6 Lectures
3D Reconstruction
7 Lectures
Smart CCTV Project
15 Lectures
Instructor Details
Packt Publishing
Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals.
Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools.
As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.
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