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C.C.J. van Engelenburg
C.C.J. van Engelenburg
Profile
Architects communicate their designs through various visual abstractions of the physical space; including orthographic drawings, photos, and 3D models. Semantic similarity learning for architectural drawings is a PhD project of Casper van Engelenburg that started in October 2021, focusing on understanding visual patterns in floorplan image data. He develops deep contrastive learning frameworks that enable us to learn low-dimensional, task-agnostic representations of architectural drawings. This research line builds a foundation for large quantitative analysis of archival and linked visual data. Besides theoretical work, his aim is to connect it to the practice by enhancing Architectural-specific search engines.
Expertise
Publications
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2024
AI-supported approach for human-building interaction implemented at furniture scale
H.H. Bier / A.J. Hidding / S. Brancart / Alessandra Luna-Navarro / S. Khademi / C.C.J. van Engelenburg
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2024
Advancing Applications for Artificial-Intelligence-Supported Ambient Control in the Built Environment
Henriette Bier / Arwin Hidding / Seyran Khademi / Casper van Engelenburg / Hamed Alavi / Sailin Zhong
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2024
Real-World Applications of Artificial Intelligence in Architecture
H.H. Bier / A.J. Hidding / S. Khademi / C.C.J. van Engelenburg / J.M. Prendergast / L. Peternel
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2023
SSIG
A Visually-Guided Graph Edit Distance for Floor Plan Similarity
Casper van Engelenburg / Seyran Khademi / Jan van Gemert -
2022
Computer Vision and Human–Robot Collaboration Supported Design-to-Robotic-Assembly
Henriette Bier / Seyran Khademi / Casper van Engelenburg / J. Micah Prendergast / Luka Peternel
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