{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:42:04Z","timestamp":1760143324435,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T00:00:00Z","timestamp":1760054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Polish Ministry of Science and Higher Education"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper introduces Contextual Object Grouping (COG), a specific computer vision framework that enables automatic interpretation of technical security diagrams through dynamic legend learning for intelligent sensing applications. Unlike traditional object detection approaches that rely on post-processing heuristics to establish relationships between the detected elements, COG embeds contextual understanding directly into the detection process by treating spatially and functionally related objects as unified semantic entities. We demonstrate this approach in the context of Cyber-Physical Security Systems (CPPS) assessment, where the same symbol may represent different security devices across different designers and projects. Our proof-of-concept implementation using YOLOv8 achieves robust detection of legend components (mAP50 \u2248 0.99, mAP50\u201395 \u2248 0.81) and successfully establishes symbol\u2013label relationships for automated security asset identification. The framework introduces a new ontological class\u2014the contextual COG class that bridges atomic object detection and semantic interpretation, enabling intelligent sensing systems to perceive context rather than infer it through post-processing reasoning. This proof-of-concept appears to validate the COG hypothesis and suggests new research directions for structured visual understanding in smart sensing environments, with applications potentially extending to building automation and cyber-physical security assessment.<\/jats:p>","DOI":"10.3390\/a18100642","type":"journal-article","created":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:47:08Z","timestamp":1760104028000},"page":"642","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contextual Object Grouping (COG): A Specialized Framework for Dynamic Symbol Interpretation in Technical Security Diagrams"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4523-1227","authenticated-orcid":false,"given":"Jan","family":"Kapusta","sequence":"first","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Krakow, 30-059 Krakow, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8543-0995","authenticated-orcid":false,"given":"Waldemar","family":"Bauer","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Krakow, 30-059 Krakow, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3313-581X","authenticated-orcid":false,"given":"Jerzy","family":"Baranowski","sequence":"additional","affiliation":[{"name":"Department of Automatic Control and Robotics, AGH University of Krakow, 30-059 Krakow, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,10]]},"reference":[{"key":"ref_1","first-page":"151171","article-title":"Cyber-physical power system (CPPS): A review on modeling, simulation, and analysis with cyber security applications","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. 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