From Digital Imaging to Computer Image Analysis of Fine Art

Stork, David G.

doi:10.1007/978-3-642-11577-6_1

Cited by 3 publications

References 20 publications

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“…Various methods of automatic image segmentation are used in the literature aiming at identifying regions in an image and labeling them as different classes. The main applications are pattern recognition for classifying paintings [19][20][21][22][23] or the authentication of fine arts (e.g., of paintings) [24]. These image segmentation methods include the following: The thresholding methods transform a grey-scale image into a binary image, where the algorithm evaluates the differences among neighboring pixels to find object boundaries [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

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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.

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