2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDIMA-ELI-1.7
Paper Title HYPERSPECTRAL IMAGE SEGMENTATION FOR PAINT ANALYSIS
Authors Nathan Magro, Alexandra Bonnici, Stefania Cristina, University of Malta, Malta
SessionIMA-ELI-1: Imaging and Media Applications + Electronic Imaging
LocationArea F
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Electronic Imaging: Color and multispectral imaging
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Hyperspectral imaging (HSI) is used in analysis of paintings to obtain features hidden to the human eye by selecting specific wavelengths. Superpixel segmentation can be applied to HSI for feature extraction. A superpixel algorithm processes an image in a way in which the result includes an unnecessary amount of over-segmentation. In this work, we use over-segmentation and propose Spectral Similarity Merging (SSM), a region growing algorithm based on homogeneous spectral properties with the aim to reduce over-segmentation without compromising under-segmentation. The algorithm focuses on the similarity of the spectral shapes rather than intensity. Results show an average of 45% reduction in over-segmentation and an average of 53% improvement on the F-score on existing superpixel segmentation algorithms.