Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible fea
- PDF / 4,816,763 Bytes
- 17 Pages / 595 x 794 pts Page_size
- 54 Downloads / 149 Views
RE SE A RCH
Open Access
Computer-assisted analysis of painting brushstrokes: digital image processing for unsupervised extraction of visible features from van Gogh’s works Fabrizio Lamberti, Andrea Sanna and Gianluca Paravati* Abstract The automatic extraction of objective features from paintings, like brushstroke distribution, orientation, and shape, could be particularly useful for different artwork analyses and management tasks. In fact, these features contribute to provide a unique signature of the artists’ style and can be effectively used for artist identification and classification, artwork examination and retrieval, etc. In this paper, an automatic technique for unsupervised extraction of individual brushstrokes from digital reproductions of van Gogh’s paintings is presented. Through the iterative application of segmentation, characterization, and validation steps, valid brushstrokes complying with specific area and shape constraints are identified. On the extracted brushstrokes, several representative features can then be calculated, like orientation, length, and width. The accuracy of the devised method is evaluated by comparing numerical results obtained on a dataset of digital reproductions of van Gogh’s oil-on-canvas works with observations made by human subjects and with another recent approach for automatic brushstroke analysis. Experimental tests showed that the devised methodology produces results that are rather close to those obtained by human subjects and, for some of the metrics considered, can provide improved performances with respect to alternative techniques. Keywords: Painting analysis; Brushstroke extraction; Visual judgements; Image processing; Unsupervised techniques; Automatic systems
1 Introduction Analysis of paintings by art historians for appreciation, attribution, and conservation purposes has been supported by science and technology since the early eighteenth century [1]. As a matter of example, chemical tests have been exploited to assess pigment composition and compare paints used by different artists and in different periods, whereas dendrochronology and radiocarbon dating have been used for examining wooden panels and paint layers as well as for supporting the estimation of painting age. In more recent years, image processing techniques (encompassing ultraviolet fluorescence, infrared reflectography, stereo microscopy, x-radiography, etc.) have *Correspondence: [email protected] Dip. di Automatica ed Informatica, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino I-10129, Italy
been proposed as further tools supporting the job of art experts and conservation specialists, e.g., to identify restoring interventions, to reveal underdrawings beneath the paint surface, and to provide insights into artists’ techniques and intentions [2,3]. Despite their potential, the application of such methods, which rely on lowlevel features of paintings under analysis, has not gained yet a widespread adoption. Furthermore, the devised approaches do not, in general, allow res
Data Loading...
