C.C.J. van Engelenburg
C.C.J. van Engelenburg
Profiel
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
Publicaties
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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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2016-12-14
Mechanical Trapping of DNA in a Double-Nanopore System
Sergii Pud / Shu Han Chao / Maxim Belkin / Daniel Verschueren / Teun Huijben / Casper Van Engelenburg / Cees Dekker / Aleksei Aksimentiev
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