Introduction to Computer Vision
Extend your knowledge of Computer Vision through this free online course and apply it to work with real-time applications. It gives you fundamental and advanced-level knowledge to work with OpenCV for various AI tasks.
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What you learn in Introduction to Computer Vision ?
About this Course
This Computer Vision course is designed to ensure that you gain a thorough knowledge of image processing and how the OpenCV library is inculcated practically with Python to function in Artificial Intelligence and Machine Learning tasks. This course helps you understand the basics, such as sampling the data, digitizing images, and compressing or quantizing them. It will throw insights into different methods to work with pictures, including identification, classification, detection, and other processes in Computer Vision. Later, you will learn various Computer Vision applications to understand What Computer vision is. You will learn about Transfer Learning in the latter part of this course.
After this self-paced beginner-level guide to Computer Vision, you can continue learning AI ML by registering for the Artificial Intelligence courses with millions of keen aspirants across the globe!
Course Outline
Computer Vision is one of the essential components of AI. This section explains how a computer system visualizes and understands images and videos. It also explains how every pattern is segmented and recognized by the machine and discusses different tasks employed in the process.
In this chapter, you will understand computer vision, architecture, why, and how it is practiced. With examples, you will also gain knowledge about when and where the technology sees its application.
This chapter discusses pixel intensity histograms, their philosophy, features, and process. It also demonstrates the convolution in different layers with its function and image filters. You will learn to obtain the result for an image using the convolution function.
This chapter explains different tasks in computer vision, which are-- classification, classification with localization, object detection, and instance segmentation to help you work with different applications of computer vision.
This chapter equips you with a thorough understanding of digital images, pixels, and amplitude quantization. It discusses the functions of converting analog spaces into digital images and sampling, and it then explains digital images and neighborhood concepts involved in computer vision with examples.
With this course, you get
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Free lifetime access
Learn anytime, anywhere
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0.5 Hours
of self-paced video lectures
Frequently Asked Questions
What are the prerequisites to learning this Computer Vision course?
Before you learn this course, you must have basic knowledge of working with Keras and a good understanding of Artificial Intelligence and Machine Learning.
How long does it take to complete this free Computer Vision course?
Introduction to Computer Vision is half an hour-long course. You can, however, learn from the course at your convenience since it is self-paced.
Will I have lifetime access to this free Computer Vision online course?
Yes. You will have lifetime access to this online Computer Vision course once you enroll.
What are my next learning options after this Computer Vision course?
After you have completed this course, you can register for the AIML online courses and master them all under a single roof.
Why is it essential to learn Computer Vision?
Computer vision is the study of simulating some of the complexity of the human visual system so that computers can recognize and analyze objects in images and videos in a way comparable to how the human brain does it. It is utilized in the security sector for facial identification and pattern detection, in the corporate sector, medical and automated vehicles. Applications of computer vision are extended in police departments and other law enforcement agencies to scan urban areas, examine the behavior of large groups, find suspicious activities, and spot possible threats before they happen. Facial recognition software heavily relies on computer vision, which enables computers to match images of people's faces to their identities. Computer vision algorithms find facial characteristics in photos and contrast them with face profile databases.
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