ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Kevin Kristop Morales Barranco
Universidad de Guadalajara
Jorge Gálvez
Universidad de Guadalajara
0000-0001-6595-8605
Fernando Wario
Universidad de Guadalajara
Acerca de
Image stitching is a fundamental image processing technique that combines multiple overlapping images into panoramic images while minimizing transitions and distortions. This paper explores the evolution of image stitching from its manual beginnings to contemporary automated systems driven by advances in AI. Traditional methods, including direct and feature-based approaches, are discussed, highlighting their strengths and limitations. Recent advancements in AI, particularly in deep learning techniques, have significantly improved the accuracy and robustness of image stitching in complex scenarios. The AutoStitch algorithm is also examined for its comprehensive approach to feature detection, homography estimation, and blending. Despite these advancements, challenges in handling large datasets and poor capture conditions remain. This paper concludes by discussing the potential of emerging techniques to further transform applications in photography, mapping, augmented reality, and beyond.
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