Accelerated border tracking in binary images with GPUs
[EN] This work presents an optimized algorithm for contour detection and extraction (i.e., border tracking) in binary images, aiming to improve performance in computer vision scenarios that require real-time processing. The approach divides the image into rectangular blocks, processing each block in...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::0604f8ad67d8e9c596c4c7b67cbf0660 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234626 |
| Access Level: | acceso abierto |
| Palabra clave: | Border tracking Binary images GPU CUDA |
| Sumario: | [EN] This work presents an optimized algorithm for contour detection and extraction (i.e., border tracking) in binary images, aiming to improve performance in computer vision scenarios that require real-time processing. The approach divides the image into rectangular blocks, processing each block in parallel to extract "triads" (structures representing three interconnected and ordered points). Subsequently, the triads are connected both within each block and between adjacent blocks to form complete, closed contours. The algorithm is composed of three steps, each implemented as CUDA kernels. The main objective of the proposed algorithm is to avoid costly data transfers between the CPU and GPU, while maintaining performance at a level similar to that of the CPU. This objective is, particularly, beneficial when the algorithm is part of industrial workflows with high efficiency requirements. |
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