Kapitel/Beitrag aus dem Buch: Längle T. & Heizmann M. 2024. Forum Bildverarbeitung 2024.
Exploring the potential of quantum hardware for enhancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum- inspired methods compared to classical models for crack segmentation. Using annotated gray-scale image patches of concrete samples, we benchmark a classical mean Gaussian mixture technique, a quantum-inspired fermionbased method, Q-Seg a quantum annealing-based method, and a U-Net deep learning architecture. Our results indicate that quantum- inspired and quantum methods offer a promising alternative for image segmentation, particularly for complex crack patterns, and could be applied in near-future applications.
Srinivasan, A et al. 2024. Benefiting from quantum? A comparative study of Q-Seg, quantum-inspired techniques, and U-Net for crack segmentation. In: Längle T. & Heizmann M (eds.), Forum Bildverarbeitung 2024. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000174496-10
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Veröffentlicht am 21. November 2024