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

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

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

4. Segmentation of Sea-bed Images.
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
21 / 06 / 2000
Segmentation of Sea-bed Images.

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

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

7. Segmentation of Sea-bed Images.
Algorithm Outline
Dictionary learning
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)
21 / 06 / 2000
Segmentation of Sea-bed Images.

8. Preliminary results (1)
Dic 2 4 16 32
Window
3 x 3
10 x 10
30 x 30
21 / 06 / 2000
Segmentation of Sea-bed Images.

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

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

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

12. Segmentation of Sea-bed Images.
Perspectives
Learning
representation of classes (coverage with enough vectors)
rotation invariance
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)
21 / 06 / 2000
Segmentation of Sea-bed Images.