Description: Stitching in the context of image processing refers to the process of combining multiple images to create a cohesive panorama. This method is fundamental in creating large images or capturing scenes that cannot be encompassed in a single shot. Stitching involves the precise alignment of individual images, ensuring that edges overlap smoothly and that colors and textures integrate naturally. This process can be done manually, but today, advanced algorithms are used to enable automatic stitching, facilitating the creation of high-quality panoramas. Stitching is applied in various applications beyond photography, including map creation, 3D visualizations, and augmented reality, where accurate representation of an environment is required. The quality of the final result depends on several factors, including the quality of the original images, the stitching technique used, and the software employed. In summary, stitching is an essential technique in image processing that allows for the creation of wide and detailed visual representations from multiple sources.
History: The concept of image stitching began to develop in the 1980s, although its roots can be traced back to early experiments in panoramic photography. By the late 1990s, more sophisticated algorithms were introduced that allowed for automatic image alignment, such as the Harris corner detection algorithm. In 2001, the work of Matthew Brown and David Lowe in ‘Automatic Panoramic Image Stitching using Invariant Features’ marked an important milestone, as they presented a feature-based approach that significantly improved stitching quality. Since then, image stitching has evolved with technological advancements, integrating into image editing software and various applications.
Uses: Image stitching is used in various applications, including panoramic photography, where multiple images are combined to capture a wide landscape. It is also employed in the creation of topographic maps and in the visualization of environments in 3D, such as in architecture and interior design. Additionally, it is fundamental in augmented reality and in the creation of multimedia content, where an accurate and broad representation of a space is required. In the medical field, stitching is used to combine magnetic resonance imaging or computed tomography images, providing a more comprehensive view of the patient’s anatomy.
Examples: An example of image stitching is the creation of panoramas in photography applications where users can import multiple images and the software automatically aligns and combines them. Another case is the use of stitching in Google Street View, which employs this technique to create panoramic street views from multiple images captured by its vehicles. In the medical field, there are applications that combine tomography images to provide a more detailed view of specific areas of the body.
