Introduction
Image stitching is a technique used to combine multiple images into a panoramic view. The goal of this assignment is to implement robust homography estimation to stitch pairs of images together. By accurately aligning and blending the images, we can create seamless panoramas that capture a wider field of view.
Problem Statement
The problem at hand involves stitching pairs of images together to create a panoramic view. This requires accurately estimating the homography between the images, which represents the transformation needed to align the images correctly. By implementing a robust homography estimation algorithm, we can overcome challenges such as differences in viewpoint, scale, and rotation between the images.
Dataset
For this assignment, we provide a pair of images that need to be stitched together. These images can be downloaded in high-resolution versions for processing and analysis.
Task
To tackle the image stitching problem, we will follow a step-by-step approach, leveraging various techniques and algorithms:
Load and Preprocess Images: We will load both images and convert them to an appropriate format (double or float) while also converting them to grayscale. This step ensures uniformity in data representation and simplifies subsequent processing.
Feature Detection: We will employ the Harris corner detection algorithm to identify feature points in both images. These feature points serve as reference points for establishing correspondences between the images.
Descriptor Extraction: Local neighborhoods will be extracted around each keypoint in both images. We will create descriptors by "flattening" the pixel values within these neighborhoods to one-dimensional vectors. Experimentation with different neighborhood sizes will be performed to identify the most effective choice.
Descriptor Matching: Distances between descriptors in one image and descriptors in the other image will be computed. This step involves comparing the descriptors using Euclidean distance or other alternatives like normalized correlation or SIFT descriptors. The goal is to establish putative matches based on pairwise descriptor distances.
RANSAC and Homography Estimation: The RANSAC algorithm will be implemented to estimate the homography matrix that maps one image onto the other. We will iterate through the RANSAC process, initializing the homography using four matches, and selecting the transformation with the most inliers. The average residual for the inliers will be calculated, representing the squared distance between the transformed coordinates and the actual matching points. The locations of inlier matches will be displayed in both images.
Image Warping: Using the estimated transformation, one image will be warped onto the other. This step involves applying the appropriate transformation functions (such as maketform and imtransform in MATLAB or similar functions in Python) to align the images correctly.
Panorama Composition: A new image will be created, capable of holding the complete panorama. The two images will be composited into this new image by averaging the pixel values where the images overlap. Color panoramas can be created by applying the compositing step to each color channel separately.
Extra Credit: For additional credit, we can extend the homography estimation to work with multiple images, as well as experiment with challenging image pairs or sequences. This involves registering multiple images together to create a composite panoramic view.
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