Generating Panoramic Image by Image Stitching Based on ORB Feature Detection Technique

Abstract

Generating panoramic images through image stitching is a crucial task in computer vision, enabling the creation of immersive and wide-field views from a series of overlapping images. This project focuses on implementing an image stitching algorithm based on the ORB (Oriented FAST and Rotated BRIEF) feature detection technique to seamlessly merge multiple images into a panoramic view. The proposed approach leverages the efficiency and robustness of ORB feature detection to identify keypoints in each input image. These keypoints serve as distinctive landmarks that facilitate accurate matching between overlapping regions of adjacent images. By employing geometric transformation methods and robust matching algorithms, the detected keypoints are used to estimate the homography transformation between pairs of images. This homography transformation aligns the images into a common coordinate system, enabling seamless blending and integration of overlapping regions. The implementation is performed using the OpenCV library in Python, providing a flexible and accessible platform for developing the image stitching algorithm. Experimental results demonstrate the effectiveness of the proposed approach in generating high-quality panoramic images with minimal computational overhead. Overall, this project presents a novel image stitching solution based on the ORB feature detection technique, offering a robust and efficient method for generating panoramic images. The integration of ORB feature detection with geometric transformation and blending techniques contributes to the advancement of image stitching algorithms, opening up possibilities for various applications in fields such as photography, virtual reality, and autonomous navigation.