Template selection technique on object recognition

S. Volkova

doi:10.1117/12.2669292

5 January 2023 Template selection technique on object recognition

S. Volkova

Proceedings Volume 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022); 125640V (2023) https://doi.org/10.1117/12.2669292
Event: Computer Applications for Management and Sustainable Development of Production and Industry (CMSD2022), 2022, Dushanbe, Tajikistan

Abstract

In object recognition systems based on template matching by calculating the distance metric between preassigned templates and given object, the template selection can severely affect the recognition quality. This article presents a novel method for image selection in object recognition systems, which can improve the identification quality. Its uniqueness lies in the fact that the images, what is the best for further recognition, were selected automatically in a way that provides the best quality. The technique is based on a SOM (Self-Organizing Map) and k-mean clustering. In this article, we describe the selection procedure and show the result of its usage in the system.

Citation Download Citation

S. Volkova "Template selection technique on object recognition", Proc. SPIE 12564, 2nd International Conference on Computer Applications for Management and Sustainable Development of Production and Industry (CMSD-II-2022), 125640V (5 January 2023); https://doi.org/10.1117/12.2669292

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available