Image Processing and Segmentation Techniques

Introduction

This repository explores various image processing techniques, focusing on image segmentation using clustering methods. We delve into concepts such as Human Stereopsis, Binocular Fusion, and different clustering techniques like K-means, DBScan, and Agglomerative clustering. Additionally, we implement short boundary detection and background subtraction.

Contents

Introduction to Image Processing Techniques

Image Segmentation Using K-means Clustering

Enhanced Image Processing: Grayscale, Boundary Detection, and Background Subtraction

1. Introduction to Image Processing Techniques

In this section, we provide an overview of key concepts in image processing, including:

Segmentation by Clustering:

Techniques like K-means, DBScan, and Agglomerative clustering.

Applications:

Implementing short boundary detection and background subtraction.

Image Segmentation by Clustering Pixels:

Detailed exploration of clustering methods to segment images effectively.

2. Image Segmentation Using K-means Clustering

This section demonstrates how to perform image segmentation using K-means clustering. Image segmentation is the process of partitioning an image into multiple segments to simplify or change the representation of an image into something more meaningful.

Steps Followed:

Image Loading:

Load the image from a specified path and convert it to RGB color space for better processing.

Reshape Image:

Reshape the image from a 3D array (height, width, color channels) to a 2D array of pixels. This makes it suitable for applying K-means clustering.

Apply K-means Clustering:

Define the number of clusters (K) and apply K-means clustering to group similar pixels together.

Reshape Segmented Image:

Reshape the clustered pixels back to the original image dimensions to view the segmented image.

Display Images:

Use matplotlib to display the original and segmented images side-by-side for comparison.

3. Enhanced Image Processing: Grayscale, Boundary Detection, and Background Subtraction

Building upon the previous code, this section introduces grayscale conversion, boundary detection, and background subtraction with a blue background.

Steps Followed:

Image Loading and Display:

Load the image from a specified path, verify it's loaded correctly, and display it using matplotlib.

Grayscale Conversion:

Convert the RGB image to a grayscale image to simplify the boundary detection process.

K-means Clustering for Segmentation:

Similar to the previous code, apply K-means clustering to segment the image into different clusters.

Boundary Detection:

Use the Canny edge detection algorithm on the grayscale image to detect edges and display the result.

Background Subtraction:

Apply a mask to subtract the background, setting it to blue while keeping the foreground (greenery) intact.

Display Processed Images:

Display the original image, grayscale image, segmented image, edge detection result, and the final image with background subtraction using matplotlib.

Acknowledgements

Special thanks to Dr. Agughasi Victor Ikechukwu for their guidance and support.