Abstract
The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.
Highlights
We propose a deep learning-based framework to classify the forms of Method fine arts, such as paintings and mosaics
This paper presents a framework for geometric form analysis based on images extracted from a close-range photogrammetric model of an artifact and deep learning (CNN) algorithm
From the digital model of the mosaic, an orthophoto was obtained, which the photogrammetric software generated by rectifying the photos used in photogrammetry
Summary
The application of signal and image processing techniques for the analysis and restoration of artworks was a very uncommon practice
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