Automatic extraction of brushstroke orientation from paintings

Abstract

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Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works

“…In this respect, results achieved by the method reported in this paper are compared with those obtained by applying the algorithm in [9], confirming the above behavior both by a qualitative and quantitative point of view. With respect to approaches based on global feature extraction, in this paper, the methodology presented in [10] for comparing automatic measures about patches' prevailing orientation with measures gathered by human subjects is extended to assess the performances of the proposed technique in terms of individual brushstrokes orientation, length, and width, showing a good agreement between automatically and manually obtained results.…”

“…In particular, numerical results in terms of brushstroke orientation, length, and width have been first extracted and compared with visual observations by nonexpert human subjects by extending the metrics and the methodology adopted in [10]. Then, the same metrics have been exploited again on data used in [9] by comparing brushstrokes obtained by the method presented in this paper both with brushstrokes extracted by the technique proposed in the above work as well as with brushstrokes annotated manually by art experts identified by the same authors.…”

“…Global features extraction is addressed in [10], where image processing methods encompassing band-pass filtering and multi-level thresholding are used to automatically determine the prevailing orientation of image patches extracted from van Gogh's works. Automatic results are compared with human observations as well as with the outcomes of other computer-based techniques.…”

Detection of forgery in paintings using supervised learning

“…This approach was further extended to the faces of a Perugino painting thought to be painted by several hands; the three faces that clustered in this analysis agreed with the three that art experts think were painted by the same hand. Meanwhile, Berezhnoy and Postma [4,5] found methods for color and texture analysis by complementary colors of VG and specifying the spatial distribution of brushstrokes at different directions by circular filtering. Li and Wang [6] have used texture features obtained by training a 2D Hidden Markov Model on local wavelet coefficients combined with features obtained from detecting and segmenting individual brushstrokes from the images, such as length and average curvature of individual strokes.…”

“…This assumes that an artist's brushwork is characterized by signature features (caused, e.g., by the artist's habitual physical movements) which might be found by machine learning methods and used as an additional piece of evidence to rule upon authenticity. Indeed, early attempts in this area have already found considerable success [2,3,4,5,6]. …”