Autoencoder-based image processing framework for object appearance modifications

The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framew...

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Veröffentlicht in:	Neural computing & applications Jg. 33; H. 4; S. 1079 - 1090
Hauptverfasser:	Ślot, Krzysztof, Kapusta, Paweł, Kucharski, Jacek
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.02.2021 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Aspect ratio Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Datasets Handwriting Image processing Image Processing and Computer Vision Image reconstruction Original Article Principal components analysis Probability and Statistics in Computer Science Representations Convolutional autoencoders Supervised principal component analysis Visual object editing Machine learning
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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