Abstract book (pdf) - ICPR 2010

13:50-14:10, Paper WeBT3.2
Gait Learning-Based Regenerative Model: A Level Set Approach
Al-Huseiny, Muayed Sattar, Univ. of Southampton
Mahmoodi, Sasan, Univ. of Southampton
Nixon, Mark, Univ. of Southampton
We propose a learning method for gait synthesis from a sequence of shapes(frames) with the ability to extrapolate to novel
data. It involves the application of PCA, first to reduce the data dimensionality to certain features, and second to model corresponding
features derived from the training gait cycles as a Gaussian distribution. This approach transforms a non Gaussian
shape deformation problem into a Gaussian one by considering features of entire gait cycles as vectors in a Gaussian space.
We show that these features which we formulate as continuous functions can be modeled by PCA. We also use this model
to in-between (generate intermediate unknown) shapes in the training cycle. Furthermore, this paper demonstrates that the
derived features can be used in the identification of pedestrians.
14:10-14:30, Paper WeBT3.3
Scale-Space Spectral Representation of Shape
Bates, Jonathan, Florida State Univ.
Liu, Xiuwen, Florida State Univ.
Mio, Washington, Florida State Univ.
We construct a scale space of shape of closed Riemannian manifolds, equipped with metrics derived from spectral representations
and the Hausdorff distance. The representation depends only on the intrinsic geometry of the manifolds, making it
robust to pose and articulation. The computation of shape distance involves an optimization problem over the 2^p-element
group of all p-bit strings, which is approached with Markov chain Monte Carlo techniques. The methods are applied to cluster
surfaces in 3D space.
14:30-14:50, Paper WeBT3.4
Learning Metrics for Shape Classification and Discrimination
Fan, Yu, Florida State Univ.
Houle, David, Florida State Unversity
Mio, Washington, Florida State Univ.
We propose a family of shape metrics that generalize the classical Procrustes distance by attributing weights to general linear
combinations of landmarks. We develop an algorithm to learn a metric that is optimally suited to a given shape classification
problem. Shape discrimination experiments are carried out with phantom data, as well as landmark data representing the
shape of the wing of different species of fruit flies.
14:50-15:10, Paper WeBT3.5
Non-Parametric 3D Shape Warping
Hillenbrand, Ulrich, German Aerospace Center (DLR)
A method is presented for non-rigid alignment of a source shape to a target shape through estimating and interpolating pointwise
correspondences between their surfaces given as point clouds. The resulting mapping can be non-smooth and non-isometric,
relate shapes across large variations, and find partial matches. It does not require a parametric model or a prior of
deformations. Results are shown for some objects from the Princeton Shape Benchmark and a range scan.
WeBT4 Dolmabahçe Hall B
Image Denoising Regular Session
Session chair: Skodras, A. (Hellenic Open Univ.)
13:30-13:50, Paper WeBT4.1
Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces
Bouboulis, Pantelis, Univ. of Athens
Slavakis, Konstantinos, Univ. of Peloponnese
Theodoridis, Sergios, Univ. of Athens
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