11,902 research outputs found
Density estimation on the rotation group using diffusive wavelets
This paper considers the problem of estimating probability density functions
on the rotation group . Two distinct approaches are proposed, one based
on characteristic functions and the other on wavelets using the heat kernel.
Expressions are derived for their Mean Integrated Squared Errors. The
performance of the estimators is studied numerically and compared with the
performance of an existing technique using the De La Vall\'ee Poussin kernel
estimator. The heat-kernel wavelet approach appears to offer the best
convergence, with faster convergence to the optimal bound and guaranteed
positivity of the estimated probability density function
Overcomplete steerable pyramid filters and rotation invariance
A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated
Asymmetric Feature Maps with Application to Sketch Based Retrieval
We propose a novel concept of asymmetric feature maps (AFM), which allows to
evaluate multiple kernels between a query and database entries without
increasing the memory requirements. To demonstrate the advantages of the AFM
method, we derive a short vector image representation that, due to asymmetric
feature maps, supports efficient scale and translation invariant sketch-based
image retrieval. Unlike most of the short-code based retrieval systems, the
proposed method provides the query localization in the retrieved image. The
efficiency of the search is boosted by approximating a 2D translation search
via trigonometric polynomial of scores by 1D projections. The projections are a
special case of AFM. An order of magnitude speed-up is achieved compared to
traditional trigonometric polynomials. The results are boosted by an
image-based average query expansion, exceeding significantly the state of the
art on standard benchmarks.Comment: CVPR 201
Diffeomorphic Learning
We introduce in this paper a learning paradigm in which the training data is
transformed by a diffeomorphic transformation before prediction. The learning
algorithm minimizes a cost function evaluating the prediction error on the
training set penalized by the distance between the diffeomorphism and the
identity. The approach borrows ideas from shape analysis where diffeomorphisms
are estimated for shape and image alignment, and brings them in a previously
unexplored setting, estimating, in particular diffeomorphisms in much larger
dimensions. After introducing the concept and describing a learning algorithm,
we present diverse applications, mostly with synthetic examples, demonstrating
the potential of the approach, as well as some insight on how it can be
improved
Invariance of visual operations at the level of receptive fields
Receptive field profiles registered by cell recordings have shown that
mammalian vision has developed receptive fields tuned to different sizes and
orientations in the image domain as well as to different image velocities in
space-time. This article presents a theoretical model by which families of
idealized receptive field profiles can be derived mathematically from a small
set of basic assumptions that correspond to structural properties of the
environment. The article also presents a theory for how basic invariance
properties to variations in scale, viewing direction and relative motion can be
obtained from the output of such receptive fields, using complementary
selection mechanisms that operate over the output of families of receptive
fields tuned to different parameters. Thereby, the theory shows how basic
invariance properties of a visual system can be obtained already at the level
of receptive fields, and we can explain the different shapes of receptive field
profiles found in biological vision from a requirement that the visual system
should be invariant to the natural types of image transformations that occur in
its environment.Comment: 40 pages, 17 figure
Graph kernels between point clouds
Point clouds are sets of points in two or three dimensions. Most kernel
methods for learning on sets of points have not yet dealt with the specific
geometrical invariances and practical constraints associated with point clouds
in computer vision and graphics. In this paper, we present extensions of graph
kernels for point clouds, which allow to use kernel methods for such ob jects
as shapes, line drawings, or any three-dimensional point clouds. In order to
design rich and numerically efficient kernels with as few free parameters as
possible, we use kernels between covariance matrices and their factorizations
on graphical models. We derive polynomial time dynamic programming recursions
and present applications to recognition of handwritten digits and Chinese
characters from few training examples
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