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Theses

Shape recognition for image scene analysis

Maria Kulikova 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : This thesis includes two main parts. In the first part we address the problem of tree crown classification into species using shape features, without, or in combination with, those of radiometry and texture, to demonstrate that shape information improves classification performance. For this purpose, we first study the shapes of tree crowns extracted from very high resolution colour aerial infra-red images. For our study, we choose a methodology based on the shape analysis of closed continuous curves on shape spaces using geodesic paths under the bending metric with the angle-function curve representation, and the elastic metric with the square root q-function representation. A necessary preliminary step to classification is extraction of the tree crowns. In the second part, we address thus the problem of extraction of multiple objects with complex, arbitrary shape from remote sensing images of very high resolution. We develop a model based on marked point processes. Its originality lies in its use of arbitrarily-shaped objects as opposed to parametric shape objects, e.g. ellipses or rectangles. The shapes considered are obtained by local minimisation of an active contour energy with weak and then strong shape prior knowledge included. The objects in the final (optimal) configuration are then selected from amongst these candidates by a multiple birth-and-death dynamics embedded in an annealing scheme. The approach is validated on very high resolution images of forest provided by the Swedish University of Agriculture.
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Maria Kulikova. Shape recognition for image scene analysis. Human-Computer Interaction [cs.HC]. Université Nice Sophia Antipolis, 2009. English. ⟨tel-00477661v2⟩

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