As a part of a remarkable initiative, I spearheaded the development of Pupils Mosaic, a proficient image processing tool. The objective was to create seamless panoramic mosaics from overlapping eye pupil images. This is crucial in the medical field as these composite images provide a more comprehensive view to physicians for accurate diagnosis. The tool leverages Python and exploits the OpenCV library to facilitate the process of image stitching.
The procedure of creating mosaics involves several vital steps:
- Loading and identifying features in the images using the SIFT (Scale-Invariant Feature Transform) feature detector.
- Matching the detected features between the images with the FLANN (Fast Library for Approximate Nearest Neighbors) matcher.
- Estimating the homography matrices between the images using the RANSAC (Random Sample Consensus) algorithm for accurate image alignment.
- Warping the images to align them to a common coordinate system using the estimated homography matrices.
- Blending the warped images together using a weighted average method to produce a seamless panoramic image or mosaic.
With Pupils Mosaic, users have an intuitive interface that allows them to select input images, adjust parameters, and display the resulting mosaic. It is a powerful tool that provides high-quality panoramas and significantly enhances image processing efficiency.
Key Achievements:
- Directed the development of Pupils Mosaic, an advanced image processing tool creating seamless panoramic images from overlapping eye pupil images, significantly aiding physicians in accurate diagnoses.
- Utilized crucial algorithms including SIFT for feature detection, FLANN for feature matching, and RANSAC for estimating homography matrices, ensuring precise image alignment.
- Successfully created a tool that provides a user-friendly interface for selecting input images, adjusting parameters, and showcasing the output mosaic.
Particulars: Linux (Ubuntu), Python, OpenCV Library.