1. Review Dec, 2001 Workpackage 4 Image Analysis Algorithms Progress Update Dec. 2001 Kirk Martinez, Paul Lewis, David Duplaw, Fazly Abbas, Faizal Fauzi, Mike Westmacott, Marc Chiaverini Intelligence, Agents and Multimedia Research Group Department of Electronics and Computer Science University of Southampton UK

2. Review Dec, 2001 Overview Grey level Histogram Texture matching and texture segmentation Query by Low Quality Images MNS colour clustering craquelure detection Query by Sketch

3. Review Dec, 2001 Progress on Texture Segmentation and Classification Texture in image processing is concerned with repeating patterns Work on texture is currently concentrating on wavelets Wavelet transforms analyse the image according to scale and frequency Transforms can use different decomposition strategies and different base wavelet functions (cf Fourier which uses sines and cosines only)

4. Review Dec, 2001 Segmentation for Texture Indexing Idea is to divide the image into major regions of homogeneous texture Then store representation of each significant texture so that images containing similar textures can be retrieved eg we have an image of a textile. We may wish to ask, “are there other images containing a similar textile pattern?” Texture may also be a useful contributing key for style classification

5. Review Dec, 2001 Query by Low Quality Images eg Faxes Modified the standard wavelet retrieval to use all but the lowest frequency coefficient Using a set of 19 faxes we evaluated retrieval by fax using a database of 150 images including the originals for the 19 fax images.

6. Review Dec, 2001 Using Daubechies Wavelets RankingPWTModified PWT Top 539 6-1053 11-2042 21-3013 31-4010 Other52

7. Review Dec, 2001 Fax Queries and Database Image

8. Review Dec, 2001

10. MNS- Multi-Nodal Signature Uses colour pair patches as key for matching Original version only used presence of a colour pairs and no real scope for indexing Now exploring use of quantised colour pairs, an indexing strategy and use of frequency of occurrence within an image and inverse of document frequency as weightings.

11. Review Dec, 2001 Query By Sketch

12. Review Dec, 2001 Colour Space Custering

13. Review Dec, 2001 Identifying a cluster

14. Review Dec, 2001 Labelling an image with pigment

15. Review Dec, 2001 Crack Detection Original image Vertical + horizontal detection diagonal detection Detected cracks

16. Review Dec, 2001 cracks: another example Next stage is to classify them