61,418 research outputs found
Disconnected Skeleton: Shape at its Absolute Scale
We present a new skeletal representation along with a matching framework to
address the deformable shape recognition problem. The disconnectedness arises
as a result of excessive regularization that we use to describe a shape at an
attainably coarse scale. Our motivation is to rely on the stable properties of
the shape instead of inaccurately measured secondary details. The new
representation does not suffer from the common instability problems of
traditional connected skeletons, and the matching process gives quite
successful results on a diverse database of 2D shapes. An important difference
of our approach from the conventional use of the skeleton is that we replace
the local coordinate frame with a global Euclidean frame supported by
additional mechanisms to handle articulations and local boundary deformations.
As a result, we can produce descriptions that are sensitive to any combination
of changes in scale, position, orientation and articulation, as well as
invariant ones.Comment: The work excluding {\S}V and {\S}VI has first appeared in 2005 ICCV:
Aslan, C., Tari, S.: An Axis-Based Representation for Recognition. In
ICCV(2005) 1339- 1346.; Aslan, C., : Disconnected Skeletons for Shape
Recognition. Masters thesis, Department of Computer Engineering, Middle East
Technical University, May 200
Adaptive Quantizers for Estimation
In this paper, adaptive estimation based on noisy quantized observations is
studied. A low complexity adaptive algorithm using a quantizer with adjustable
input gain and offset is presented. Three possible scalar models for the
parameter to be estimated are considered: constant, Wiener process and Wiener
process with deterministic drift. After showing that the algorithm is
asymptotically unbiased for estimating a constant, it is shown, in the three
cases, that the asymptotic mean squared error depends on the Fisher information
for the quantized measurements. It is also shown that the loss of performance
due to quantization depends approximately on the ratio of the Fisher
information for quantized and continuous measurements. At the end of the paper
the theoretical results are validated through simulation under two different
classes of noise, generalized Gaussian noise and Student's-t noise
The impact of cellular characteristics on the evolution of shape homeostasis
The importance of individual cells in a developing multicellular organism is
well known but precisely how the individual cellular characteristics of those
cells collectively drive the emergence of robust, homeostatic structures is
less well understood. For example cell communication via a diffusible factor
allows for information to travel across large distances within the population,
and cell polarisation makes it possible to form structures with a particular
orientation, but how do these processes interact to produce a more robust and
regulated structure? In this study we investigate the ability of cells with
different cellular characteristics to grow and maintain homeostatic structures.
We do this in the context of an individual-based model where cell behaviour is
driven by an intra-cellular network that determines the cell phenotype. More
precisely, we investigated evolution with 96 different permutations of our
model, where cell motility, cell death, long-range growth factor (LGF),
short-range growth factor (SGF) and cell polarisation were either present or
absent. The results show that LGF has the largest positive impact on the
fitness of the evolved solutions. SGF and polarisation also contribute, but all
other capabilities essentially increase the search space, effectively making it
more difficult to achieve a solution. By perturbing the evolved solutions, we
found that they are highly robust to both mutations and wounding. In addition,
we observed that by evolving solutions in more unstable environments they
produce structures that were more robust and adaptive. In conclusion, our
results suggest that robust collective behaviour is most likely to evolve when
cells are endowed with long range communication, cell polarisation, and
selection pressure from an unstable environment
Fast and Accurate 3D Face Recognition Using Registration to an Intrinsic Coordinate System and Fusion of Multiple Region classifiers
In this paper we present a new robust approach for 3D face registration to an intrinsic coordinate system of the face. The intrinsic coordinate system is defined by the vertical symmetry plane through the nose, the tip of the nose and the slope of the bridge of the nose. In addition, we propose a 3D face classifier based on the fusion of many dependent region classifiers for overlapping face regions. The region classifiers use PCA-LDA for feature extraction and the likelihood ratio as a matching score. Fusion is realised using straightforward majority voting for the identification scenario. For verification, a voting approach is used as well and the decision is defined by comparing the number of votes to a threshold. Using the proposed registration method combined with a classifier consisting of 60 fused region classifiers we obtain a 99.0% identification rate on the all vs first identification test of the FRGC v2 data. A verification rate of 94.6% at FAR=0.1% was obtained for the all vs all verification test on the FRGC v2 data using fusion of 120 region classifiers. The first is the highest reported performance and the second is in the top-5 of best performing systems on these tests. In addition, our approach is much faster than other methods, taking only 2.5 seconds per image for registration and less than 0.1 ms per comparison. Because we apply feature extraction using PCA and LDA, the resulting template size is also very small: 6 kB for 60 region classifiers
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