Tuning of grayscale computer vision systems

ŠKRABÁNEK, Pavel and Natália MARTÍNKOVÁ. Tuning of grayscale computer vision systems. DISPLAYS. ELSEVIER, 2022, vol. 74, September 2022, p. 1-9. ISSN 0141-9382. Available from: https://dx.doi.org/10.1016/j.displa.2022.102286.

Basic information

• Original name: Tuning of grayscale computer vision systems
• Authors: ŠKRABÁNEK, Pavel and Natália MARTÍNKOVÁ (703 Slovakia, guarantor, belonging to the institution).
• Edition: DISPLAYS, ELSEVIER, 2022, 0141-9382.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10306 Optics
• Country of publisher: Netherlands
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 4.300
• RIV identification code: RIV/00216224:14310/22:00126692
• Organization unit: Faculty of Science
• Doi: http://dx.doi.org/10.1016/j.displa.2022.102286
• UT WoS: 000848013000002
• Keywords in English: Computer vision; Parameter optimization; Performance evaluation; WECIA graph; Weighted means grayscale conversion
• Tags: rivok
• Tags: International impact, Reviewed

Abstract

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.