Počet záznamů: 1
Tuning of grayscale computer vision systems
- 1.
SYSNO ASEP 0560958 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Tuning of grayscale computer vision systems Tvůrce(i) Škrabánek, P. (CZ)
Martínková, Natália (UBO-W) RID, ORCID, SAICelkový počet autorů 2 Číslo článku 102286 Zdroj.dok. Displays. - : Elsevier - ISSN 0141-9382
Roč. 74, September (2022)Poč.str. 9 s. Jazyk dok. eng - angličtina Země vyd. NL - Nizozemsko Klíč. slova Computer vision ; Parameter optimization ; Performance evaluation ; WECIA graph ; Weighted means grayscale conversion Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-20286S GA ČR - Grantová agentura ČR Způsob publikování Omezený přístup Institucionální podpora UBO-W - RVO:68081766 UT WOS 000848013000002 EID SCOPUS 85136309947 DOI 10.1016/j.displa.2022.102286 Anotace Computer vision systems perform based on their design and parameter setting. In computer vision systems that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of the systems in terms of both results quality and computational costs. Appropriate setting of the weights for the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the system and subsystem to small changes in the distribution of data in a color space of the processed images. We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies. The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer vision system with the robustness of its performance against variable input data. Pracoviště Ústav biologie obratlovců Kontakt Hana Slabáková, slabakova@ivb.cz, Tel.: 543 422 524 Rok sběru 2023 Elektronická adresa https://www.sciencedirect.com/science/article/pii/S0141938222001044?via%3Dihub
Počet záznamů: 1