1	Workpackage 4 Image Analysis Algorithms Progress Update Sept. 2001 Kirk Martinez, Paul Lewis, Fazly Abbas, Faizal Fauzi, Mike Westmacott, Marc Chiaverini Intelligence, Agents and Multimedia Research Group Department of Electronics and Computer Science University of 天发娱乐棋牌_天发娱乐APP-官网\|下载 UK
2	Overview
3	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	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	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	Using Daubechies Wavelets
7	Fax Queries and Database Image
8	?
9	?
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	Query By Sketch No work yet but could use paint package to create sketch and feed into M-CCV or MNS algorithms
12	Colour Space Custering
13	Identifying a cluster
14	Labelling an image with pigment
15	Crack Detection
16	cracks: another example Next stage is to classify them!