Title:	AI-based Density Recognition
Authors:	Müller, Simone Kolb, Daniel Müller, Müller Kranzlmüller, Dieter
Citation:	WSCG 2024: full papers proceedings: 32. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 227-236.
Issue Date:	2024
Publisher:	Václav Skala - UNION Agency
Document type:	konferenční příspěvek conferenceObject
URI:	http://hdl.handle.net/11025/57394
ISSN:	2464–4625 (online) 2464–4617 (print)
Keywords:	umělá inteligence;počítačové vidění;rozpoznávání hustoty
Keywords in different language:	artificial intelligence;AI;computer vision;density recognition
Abstract in different language:	Learning-based analysis of images is commonly used in the fields of mobility and robotics for safe environmental motion and interaction. This requires not only object recognition but also the assignment of certain properties to them. With the help of this information, causally related actions can be adapted to different circumstances. Such logical interactions can be optimized by recognizing object-assigned properties. Density as a physical property offers the possibility to recognize how heavy an object is, which material it is made of, which forces are at work, and consequently which influence it has on its environment. Our approach introduces an AI-based concept for assigning physical properties to objects through the use of associated images. Based on synthesized data, we derive specific patterns from 2D images using a neural network to extract further information such as volume, material, or density. Accordingly, we discuss the possibilities of property-based feature extraction to improve causally related logics.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2024: Full Papers Proceedings

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