Resumen: Topological image analysis is a powerful tool for understanding the structure and topology of images, being persistent homology one of its most popular methods. However, persistent homology requires a chain of inclusions of topological spaces, which can be challenging for digital images. In this article, we explore the use of zigzag persistence, a recent variant of traditional persistence, for digital image processing. To this end, new algorithms are developed to build a simplicial complex associated to a digital image and to compute the relationships between homology classes of a sequence of binary images via zigzag persistence. Additionally, we provide a simple software to use them. We demonstrate its effectiveness by applying it to a real-world problem of analyzing honey bee sperm videos. Idioma: Inglés DOI: 10.1016/j.patrec.2024.06.010 Año: 2024 Publicado en: PATTERN RECOGNITION LETTERS 184 (2024), 111-118 ISSN: 0167-8655 Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-112673RB-I00 Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-116641GB-I00 Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/A07_23R Tipo y forma: Article (Published version) Área (Departamento): Área Producción Animal (Dpto. Produc.Animal Cienc.Ali.)