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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Bibliographic Details
Published in:	Neural computing & applications Vol. 33; no. 4; pp. 1079 - 1090
Main Authors:	Ślot, Krzysztof, Kapusta, Paweł, Kucharski, Jacek
Format:	Journal Article
Language:	English
Published:	London Springer London 01.02.2021 Springer Nature B.V
Subjects:	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 Access:	Get full text
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