
Spatio-temporal feature descriptors are of great
importance for characterizing the local changes of 3D deformable
shapes. In this study, we propose a method for characterizing
3D shapes from point clouds and we show a direct application
on a study of organ temporal deformations. As an example,
we characterize the behavior of the bladder during forced
respiratory motion with a reduced number of 3D surface points:
first, a set of geodesically equidistant points representing the
vertices of quadrilateral mesh for the organ surface are tracked
throughout a long dynamic MRI sequence using a large deformation diffeomorphic metric mapping (LDDMM) framework.
Second, a novel 3D shape descriptor invariant to translation,
scale and rotation is proposed for characterizing the temporal
organ deformations by employing an Eulerian Partial Differential
Equations (PDEs) methodology. We demonstrate the robustness
of our feature on both synthetic 3D shapes and realistic dynamic
Magnetic Resonance Imaging (MRI) data sequences portraying
the bladder deformation during a forced breathing exercise.
Promising results are obtained, showing that the proposed feature
may be useful for several computer vision applications such as
medical imaging, aerodynamics and robotics.

A new geodesic-based feature for characterization of 3D shapes

**Session Chair**: prof. Cristiano Saltori

PS T5.8: Image, Signals and Audio processing

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
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Karim Makki

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A new geodesic-based feature for characterization of 3D shapes

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