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Application of deep neural networks to automated analysis of hatching lines for artist identification
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Ziaee, Shahrzad. Application of deep neural networks to automated analysis of hatching lines for artist identification. Retrieved from https://doi.org/doi:10.7282/t3-6661-y606

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TitleApplication of deep neural networks to automated analysis of hatching lines for artist identification
NameZiaee, Shahrzad (author); Elgammal, Ahmed (chair); Kulikowski, Casimir (member); Boularias, Abdeslam (member); Shamir, Lior (member); Rutgers University; School of Graduate Studies
Date Created2023
Other Date2022-10 (degree)
SubjectArtificial intelligence, Computer science, Art authentication, Artificial intelligence, Computer vision, Deep learning, Machine learning
Extent224 pages : illustrations
DescriptionAutomated analysis of art has gained interest in the computer science research community over the past decade, resulting in the onset of computer vision solutions for authentication and attribution of art. Attribution and authentication are two of the most critical tasks in the domain of art. Several studies have approached these tasks with an emphasis on analyzing paintings in the visual spectrum, however, with a limited scope to a specific artist, limited datasets, or without rigorous evaluations of the robustness of such approaches. Contrary to paintings, the application of computer vision methodologies for authentication and attribution of drawings and prints has been only sparsely explored.
In this dissertation, we introduce a novel approach for attribution and authentication of drawings and sketches at the visual spectrum by automated analysis of hatching lines. Hatching is a prevalent technique in drawing and printmaking, often used to introduce toning, shading, or illusion of light and volume. Since artists tend to add hatching lines spontaneously, we hypothesize that these areas of drawings could carry unique physical and unconscious characteristics of the artist, which can be used to identify the artist. We further hypothesize that such unique characteristics can be quantified by a computational model that will enable automatic attribution and authentication.
We investigated the application of deep convolutional neural networks [1] for detecting and localizing hatching lines in drawings, sketches, and prints and identifying artists based on their hatching characteristics. We conducted several experiments on different sets of drawings and prints by different artists from different eras, using different techniques and with various degrees of complexity. We concluded that we can indeed identify artists based solely on their hatchings with an accuracy of 90-100% in most cases. In addition, we extensively evaluated the robustness of our approach in hatching detection.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-6661-y606
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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