1. 21 / 06 / 2000Segmentation of Sea-bed Images.1 Josepha UNIA Ecole Centrale de Lyon

2. 21 / 06 / 2000Segmentation of Sea-bed Images.2 Summary Introduction Sensing Image segmentation Vector quantization Preliminary results Perspectives

3. 21 / 06 / 2000Segmentation of Sea-bed Images.3 Introduction Provide the information necessary for a safe and sustainable exploitation of underwater resources. sensingDevelop and implement mathematical models, and sensing and guidance techniques Marine application : Mapping of living/dead Maerl.

4. 21 / 06 / 2000Segmentation of Sea-bed Images.4 Sensing (WP3, subtask 3.1) Design signal processing algorithms for : –on-line extraction of features for platform guidance Segmentation of video images –video mosaicing –using contour information to correct (effects of) positioning errors

5. 21 / 06 / 2000Segmentation of Sea-bed Images.5 Image segmentation ROV Control Image Segmentation Contour-extraction

6. 21 / 06 / 2000Segmentation of Sea-bed Images.6 Dictionary What size for window (vector) ? Vector quantization What size for dictionary ? What words ? What resemblance measure ?

7. 21 / 06 / 2000Segmentation of Sea-bed Images.7 Algorithm Outline Dictionary learning Initialize dictionary Initialize dictionary (centroid of all vectors - windows) Split until desired dictionary size is reached PARTITIONING : make best attribution of each vector to current dictionary CORRECT DICTIONARY to get minimal distortion (for current partioning)

8. 21 / 06 / 2000Segmentation of Sea-bed Images.8 Preliminary results (1) Dic Window 241632 30 x 30 3 x 3 10 x 10

9. 21 / 06 / 2000Segmentation of Sea-bed Images.9 Preliminary results (2) Frame 01Frame 02Frame 03Frame 04 F_out 02F_out 01F_out 03F_out 04 2 CODEWORDS - WINDOW 10x10

10. 21 / 06 / 2000Segmentation of Sea-bed Images.10 Preliminary results (3) Frame 01Frame 02Frame 03Frame 04 F_out 02F_out 01F_out 03F_out 04 16 CODEWORDS - WINDOW 10x10

11. 21 / 06 / 2000Segmentation of Sea-bed Images.11 Lower resolution image Window 10x10 & 2 clusters What resolution ?

12. 21 / 06 / 2000Segmentation of Sea-bed Images.12 Learning representation of classes (coverage with enough vectors) rotation invariance Perspectives Choice of distance Euclidean distance good for compression Distance more sensitive to texture variation Automatic tuning of the algorithm’s parameters distance size of windows size of dictionary. Adaptive adjustment (tracking of classes’ characteristics)