Abstract book (pdf) - ICPR 2010

cannot be convincingly synthesised using neither simple tiling nor using purely stochastic models. However these textures
are ubiquitous in many man-made environments and also in some natural scenes. Thus they are required for their realistic
appearance visualisation. The principle of the presented BTF-NR synthesis and editing method is to automatically separate
periodic and random components from one or more input textures. Each of these components is subsequently independently
modelled using its corresponding optimal method. The regular texture part is modelled using our roller method, while the
random part is synthesised from its estimated exceptionally efficient Markov random field based representation. Both independently
enlarged texture components from the original measured textures representing one (enlargement) or several
(editing) materials are combined in the resulting synthetic near-regular texture.
10:00-10:20, Paper WeAT3.4
Detecting Vorticity in Optical Flows of Fluids
Doshi, Ashish, Univ. of Surrey
Bors, Adrian, Univ. of York
In this paper we apply the diffusion framework to dense optical flow estimation. Local image information is represented
by matrices of gradients between paired locations. Diffusion distances are modelled as sums of eigenvectors weighted by
their eigenvalues extracted following the eigen decomposion of these matrices. Local optical flow is estimated by correlating
diffusion distances characterizing features from different frames. A feature confidence factor is defined based on
the local correlation efficiency when compared to that of its neighbourhood. High confidence optical flow estimates are
propagated to areas of lower confidence.
10:20-10:40, Paper WeAT3.5
Modeling Facial Skin Motion Properties in Video and its Application to Matching Faces across Expressions
Manohar, Vasant, Raytheon BBN Tech.
Shreve, Matthew, Univ. of South Florida
Goldgof, Dmitry, Univ. of South Florida
Sarkar, Sudeep, Univ. of South Florida
In this paper, we propose a method to model the material constants (Young’s modulus) of the skin in subregions of the
face from the motion observed in multiple facial expressions and present its relevance to an image analysis task such as
face verification. On a public database consisting of 40 subjects undergoing some set of facial motions associated with
anger, disgust, fear, happy, sad, and surprise expressions, we present an expression invariant strategy to matching faces
using the Young’s modulus of the skin. Results show that it is indeed possible to match faces across expressions using the
material properties of their skin.
WeAT4 Topkapı Hall A
Kernel Methods Regular Session
Session chair: Aksoy, Selim (Bilkent Univ.)
09:00-09:20, Paper WeAT4.1
AdaMKL: A Novel Biconvex Multiple Kernel Learning Approach
Zhang, Ziming, Simon Fraser Univ.
Li, Ze-Nian, Simon Fraser Univ.
Drew, Mark S.
In this paper, we propose a novel large-margin based approach for multiple kernel learning (MKL) using biconvex optimization,
called Adaptive Multiple Kernel Learning (AdaMKL). To learn the weights for support vectors and the kernel
coefficients, AdaMKL minimizes the objective function alternately by learning one component while fixing the other at a
time, and in this way only one convex formulation needs to be solved. We also propose a family of biconvex objective
functions with an arbitrary Lp-norm (p>=1) of kernel coefficients. As our experiments show, AdaMKL performs comparably
with state-of-the-art convex optimization based MKL approaches, but its learning is much simpler and faster.
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