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Image Segmentation Using scikit-image

In this project, we’ll implement different image processing tasks using scikit-image. We’ll learn and apply two different methods of image segmentation and various methods of noise removal in images.

Image Segmentation Using scikit-image

You will learn to:

Apply image processing with scikit-image.

Apply thresholding for image segmentation.

Detect license plates using image segmentation.

Plot data using dedicated Python libraries.


Image Processing

Image Segmentation

Noise Removal


Hands-on experience with Python

Understanding of image processing techniques






Project Description

Image processing is a domain of computer science in which different operations and transformations are implemented on an image to extract useful information or produce a desirable change. Numerous image processing techniques have been developed for different processes. Image processing finds widespread applications in areas like biomedical imaging and face detection.

The scikit-image library provides image processing functionalities. It contains predefined algorithms for different image processing functions like image restoration and color conversion and contains several other functions to facilitate image processing.

In this project, we’ll use scikit-image to perform the following operations on an image:

  • Use image segmentation to separate an object based on its color.

  • Use filters to remove noise from an image.

  • Use image segmentation to detect license plates from images of vehicles.

Project Tasks



Task 0: Get Started

Task 1: Import Libraries


Image Segmentation Using Color Slicing

Task 2: Load the Image

Task 3: Convert the Image to HSV

Task 4: Perform Image Segmentation through Thresholding

Task 5: Improve the Segmentation Iteratively


Number Plate Detection Using Image Segmentation

Task 6: Load the Image and Convert to Grayscale

Task 7: Smoothen the Image

Task 8: Sharpen the Image

Task 9: Label the Image Segments

Task 10: Identify the License Plate