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Geometric Computer Vision Part 2: Image Stitching in Python
- The article discusses implementing image stitching in Python using libraries like numpy, openCV, and Matplotlib, without using ready-to-use functions from OpenCV.
- Helper functions for normalizing image intensity, image convolution, plotting images, and coordinate conversions are defined to facilitate the stitching algorithm.
- Homography matrices are calculated to map points from one image to another, and bilinear interpolation is used for approximating image intensities after transformation.
- The stitching algorithm takes two normalized images, a stitching axis, and produces a stitched image, considering potential issues like invalid pixels post-homography.
- Post-processing steps involve eliminating blank pixels, adjusting brightness based on mean intensity differences, and using Gaussian blurring to blend image seams.
- The article encourages readers to try image stitching on their own images and concludes with a discussion on future topics in geometric computer vision.
- The content is inspired and credited to Prof. Dr. Michael Bourke at Monash University for teaching fundamental concepts in Computer Vision.
- The process involves various steps like intensity normalization, convolution, homography calculation, bilinear interpolation, and handling invalid pixels post transformation.
- Blurring techniques, brightness adjustment, and post-processing help improve the final stitched image quality and appearance.
- The article provides a detailed overview of the image stitching process in Python, focusing on manual implementation and key concepts in geometric computer vision.
- Readers are encouraged to experiment with their own images and explore further applications in computer vision and image processing.
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