Tuning of grayscale computer vision systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081766%3A_____%2F22%3A00560958" target="_blank" >RIV/68081766:_____/22:00560958 - isvavai.cz</a>

Alternative codes found

RIV/00216305:26210/22:PU145434 RIV/00216224:14310/22:00126692

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0141938222001044?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0141938222001044?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.displa.2022.102286" target="_blank" >10.1016/j.displa.2022.102286</a>

Result language

angličtina

Original language name

Tuning of grayscale computer vision systems

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Displays

ISSN

0141-9382

e-ISSN

1872-7387

Volume of the periodical

74

Issue of the periodical within the volume

September

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

9

Pages from-to

102286

UT code for WoS article

000848013000002

EID of the result in the Scopus database

2-s2.0-85136309947