21 / 06 / 2000 Segmentation of Sea-bed Images. 1

Segmentation of Sea-bed Images. Segmentation of Sea-bed Images.

JosephaUNIA

Ecole Centrale de Lyon

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SummarySummary

• Introduction

• Sensing

• Image segmentation

• Vector quantization

• Preliminary results

• Perspectives

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IntroductionIntroduction

• Provide the information necessary for a safe and sustainable exploitation of underwater resources.

• Develop and implement mathematical models, and sensingsensing and guidance techniques

• Marine application : Mapping of living/dead Maerl.

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Sensing (WP3, subtask 3.1)Sensing (WP3, subtask 3.1)Design signal processing algorithms for :

– on-line extraction of features for platform guidanceSegmentation of video imagesSegmentation of video images

– video mosaicing

– using contour information to correct (effects of) positioning errors

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Image segmentationImage segmentation

ROV Control

Image

SegmentationSegmentation Contour-extraction

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DictionaryDictionary• What size for window (vector) ?

Vector quantizationVector quantization

•What size for dictionary ?

•What words ?

•What resemblance measure ?

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Algorithm OutlineAlgorithm Outline

Dictionary learning

• Initialize dictionaryInitialize dictionary (centroid of all vectors - windows)

• Split until desired dictionary size is reachedSplit 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)

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Preliminary results (1)Preliminary results (1)Dic

Window2 4 16 32

30 x 30

3 x 3

10 x 10

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Preliminary results (2)Preliminary results (2)

Frame 01 Frame 02 Frame 03 Frame 04

F_out 02F_out 01 F_out 03 F_out 04

2 CODEWORDS - WINDOW 10x102 CODEWORDS - WINDOW 10x10

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Preliminary results (3)Preliminary results (3)

Frame 01 Frame 02 Frame 03 Frame 04

F_out 02F_out 01 F_out 03 F_out 04

16 CODEWORDS - WINDOW 10x1016 CODEWORDS - WINDOW 10x10

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Lower resolution imageLower resolution image

Window 10x10 & 2 clusters

What resolution ?What resolution ?

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• Learning• representation of classes (coverage with enough vectors)• rotation invariance

PerspectivesPerspectives

•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)