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Free Image Segmentation Course

Image Segmentation Techniques

Learn image segmentation with edge detection, thresholding, and region-based methods. Join our free Image Segmentation course to gain practical skills fo r extracting structures and applying them in computer vision.

Instructor:

Dr. Vibhav Singh
Beginner

Level

2.25 Hrs

Learning hours

62

Learners

Enroll free with email

Skills you will learn

Image segmentation basics
Gradient edge detection
Sobel/Prewitt/Roberts operators
Thresholding
Segmentation algorithms
Region growing/splitting/merging

About this Course

This free image Segmentation training course covers the fundamental techniques used in computer vision to divide images into meaningful regions. You will learn how gradient operators such as Sobel, Prewitt, and Roberts detect edges, along with core edge detection and thresholding methods for separating objects from the background. The course also introduces segmentation algorithms, including clustering, edge-based, and region-based approaches, with a focus on region growing, splitting, and merging techniques. By the end, you will have the skills to apply image segmentation methods to real-world images and build a foundation for advanced computer vision tasks.

Why upskill with us?

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Course Outline

Introduction to Image Segmentation

In this module, you’ll explore the fundamentals of image segmentation, its purpose in computer vision, and how it helps in dividing an image into meaningful regions.

Gradient Operators

In this module, you’ll learn how gradient-based operators like Sobel, Prewitt, and Roberts are used to detect edges by measuring changes in pixel intensity.

Basics of Edge Detection

In this module, you’ll examine core edge detection techniques for identifying object boundaries within images.

Basics of Thresholding

In this module, you’ll understand how thresholding separates objects from the background using pixel intensity values.

Segmentation Algorithms

In this module, you’ll discover various segmentation approaches, including clustering, edge-based, and region-based techniques.

Region Based Segmentation

In this module, you’ll learn how region growing, splitting, and merging methods are applied to segment connected areas in images.

Image Segmentation Techniques

2.25 Hrs

Learning hours

Beginner

Level

62

Learners

Enroll free with email

Trusted by 10 Million+ Learners globally

star

4.8
star

4.89
star

4.94
star

4.7

Our course instructor

instructor img

Dr. Vibhav Singh

Assistant Professor

Artificial Intelligence Expert

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62 Learners
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1 Courses

Frequently Asked Questions

Who should take this image segmentation free course?

This course is ideal for beginners, students, and professionals interested in computer vision, image processing, or AI who want to understand and apply image segmentation techniques.

What will I learn in this free image Segmentation course?

You’ll learn image segmentation for free, starting with the basics, gradient edge detection, and thresholding. The course also covers segmentation algorithms and region-based methods like growing, splitting, and merging.

Do I need prior experience before starting this free image Segmentation training course?

No advanced experience is required. Basic knowledge of images, pixels, and digital image processing concepts can be helpful, but is not mandatory.

What is the purpose of learning image segmentation?

Image segmentation is a core step in computer vision that enables object detection, medical imaging, autonomous driving, and many AI applications. Learning gives you practical, in-demand skills.

What modules are included in the course?

The course includes:

  • Introduction to Image Segmentation
  • Gradient Operators
  • Basics of Edge Detection
  • Basics of Thresholding
  • Segmentation Algorithms
  • Region-Based Segmentation


What skills will I gain after completing the free online image Segmentation course?

You’ll gain skills in image preprocessing, edge detection with Sobel/Prewitt/Roberts operators, thresholding, segmentation algorithms, and applying region-based methods to real-world images.

How does this course apply in real-world scenarios?

Once you complete the course, you will be in a position to implement segmentation methods for tasks such as object recognition, medical imaging, and other computer vision tasks.

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