FindAPhD Weekly PhD Newsletter | JOIN NOW FindAPhD Weekly PhD Newsletter | JOIN NOW

An automated approach for identification, authentication and classification of historical artworks and manuscripts using multimodal imaging techniques


   School of Science & Technology

This project is no longer listed on FindAPhD.com and may not be available.

Click here to search FindAPhD.com for PhD studentship opportunities
  Dr G Shahtahmassebi, Prof H Liang  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The 18th/19th century Japanese artist Hokusai is famous for his iconic colour woodblock print The Great Wave. Hokusai was an extremely prolific artist and much of his surviving work is preserved in the collections of museums, galleries and libraries across the world. Hokusai is well-known for re-using similar (but not identical) designs over time in his art which includes sketches, woodblock prints, illustrated woodblock printed books and paintings.

To help with authentication but also to better understand Hokusai’s work, scholars task themselves with finding similar images among Hokusai’s work. Manual investigation of the images of the woodblock prints has been fruitful also in identifying the time sequence of the prints as the woodblocks age. These questions and many other similar research questions in art history could be answered by both non-invasive advanced imaging of the artworks and an automated image classification method. The Imaging and Sensing for Archaeology, Art history and Conservation (ISAAC) Lab will provide mobile lab facility to apply a range of suitable non-invasive advanced imaging techniques (e.g. spectral imaging of a variety of modalities) in situ.

To avoid the need of manual analysis or labelling, this project aims to develop an automated classification method that is suitable for both printed works, drawings and manuscripts to classify the sketches or brushstrokes or scribal hand, authenticate seal stamps and signatures. The proposed method will adopt, modify and extend available classification methods and deep learning algorithms for analysing such images, and extract further information from the imaging data collected from large numbers of printed artworks to draw conclusions such as artist, artist’s style, classification of objects, authenticity, etc that are of interest to art historians.

Supervisory team

Director of Studies: Dr Golnaz Shahtahmassebi

Co-Supervisor: Professor Haida Liang

Entry requirements

For the eligibility criteria, please visit our how to apply page.

Fees and funding

This project is fully-funded by the Cultural Heritage Research Peak Studentship Scheme.

How to apply

The application deadline is Friday 18 February 2022.

We are looking for motivated, engaged individuals to join our doctoral community. If you are interested in applying for one of the proposed Studentship projects, follow the apply button to access our application portal: please note, you will need to use the ‘NTU Doctoral Application 21/22’ form.

As you are applying for a project, your application should clearly outline which of the projects advertised you wish to apply in Summary of Proposed Research Topic. In Research Proposal and Personal Statement, please give up to 1,500 word statement of why you are interested in the project you are applying for and how you would engage with the research proposed. Think about the outline and research aims for the project and how you would approach them, as well as showing your understanding of the field and how the project will contribute to or challenge existing research. Your statement should focus on the framework of the project, to give the panel a clear idea of your understanding of the research project/topic. You will also need to include a bibliography or reference list for any work you cite.

Your skills, experience, motivation for pursuing doctoral study, and interest in the field should be included as part of your 500 word Previous Experience and Personal Statement.

Please note that only applications to the advertised projects will be accepted as part of this funding call; do not use your application to propose your own research project.

Please see our application guide for prospective candidates. You can also find a step-by-step guide and make an application on our how to apply page.

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs