Article
ROBUST ALIGNMENT FOR PANORAMIC IMAGE STITCHING VIA AN EXACT RANK CONSTRAINT
This project introduces an automated method for stitching panoramic images by leveraging machine learning and computer vision technologies. Traditional methods often require manually sorted images, which can be time-consuming and error-prone. The proposed system eliminates this dependency by automatically identifying and grouping related images based on visual similarity. Feature detection is performed using the Scale- Invariant Feature Transform (SIFT) algorithm, which extracts robust keypoints unaffected by scale, rotation, or lighting variations. These keypoints are matched between image pairs using nearest-neighbor techniques, and the Random Sample Consensus (RANSAC) algorithm is applied to compute accurate homography matrices while filtering out outliers.Once the transformation matrices are obtained, the images are geometrically aligned and warped into a common coordinate space to form a cohesive panorama. Image blending techniques such as multi-band blending or feathering are employed to ensure smooth transitions across image boundaries, minimizing visible seams and exposure differences. The system also integrates quality assessment metrics to evaluate the alignment accuracy and overall visual coherence of the stitched panorama. To enhance scalability, the approach supports batch processing of large image datasets and can adapt to unordered or variably illuminated inputs. Potential applications of this system include automated mapping, virtual tours, surveillance systems, and content generation in augmented reality environments.
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