13,993 research outputs found
Unfolding the Hierarchy of Voids
We present a framework for the hierarchical identification and
characterization of voids based on the Watershed Void Finder. The Hierarchical
Void Finder is based on a generalization of the scale space of a density field
invoked in order to trace the hierarchical nature and structure of cosmological
voids. At each level of the hierarchy, the watershed transform is used to
identify the voids at that particular scale. By identifying the overlapping
regions between watershed basins in adjacent levels, the hierarchical void tree
is constructed. Applications on a hierarchical Voronoi model and on a set of
cosmological simulations illustrate its potential.Comment: 5 pages, 2 figure
Learning to Navigate the Energy Landscape
In this paper, we present a novel and efficient architecture for addressing
computer vision problems that use `Analysis by Synthesis'. Analysis by
synthesis involves the minimization of the reconstruction error which is
typically a non-convex function of the latent target variables.
State-of-the-art methods adopt a hybrid scheme where discriminatively trained
predictors like Random Forests or Convolutional Neural Networks are used to
initialize local search algorithms. While these methods have been shown to
produce promising results, they often get stuck in local optima. Our method
goes beyond the conventional hybrid architecture by not only proposing multiple
accurate initial solutions but by also defining a navigational structure over
the solution space that can be used for extremely efficient gradient-free local
search. We demonstrate the efficacy of our approach on the challenging problem
of RGB Camera Relocalization. To make the RGB camera relocalization problem
particularly challenging, we introduce a new dataset of 3D environments which
are significantly larger than those found in other publicly-available datasets.
Our experiments reveal that the proposed method is able to achieve
state-of-the-art camera relocalization results. We also demonstrate the
generalizability of our approach on Hand Pose Estimation and Image Retrieval
tasks
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Nowadays, hyperspectral image classification widely copes with spatial
information to improve accuracy. One of the most popular way to integrate such
information is to extract hierarchical features from a multiscale segmentation.
In the classification context, the extracted features are commonly concatenated
into a long vector (also called stacked vector), on which is applied a
conventional vector-based machine learning technique (e.g. SVM with Gaussian
kernel). In this paper, we rather propose to use a sequence structured kernel:
the spectrum kernel. We show that the conventional stacked vector-based kernel
is actually a special case of this kernel. Experiments conducted on various
publicly available hyperspectral datasets illustrate the improvement of the
proposed kernel w.r.t. conventional ones using the same hierarchical spatial
features.Comment: 8th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing:
Evolution in Remote Sensing (WHISPERS 2016), UCLA in Los Angeles, California,
U.
Decomposition of Optical Flow on the Sphere
We propose a number of variational regularisation methods for the estimation
and decomposition of motion fields on the -sphere. While motion estimation
is based on the optical flow equation, the presented decomposition models are
motivated by recent trends in image analysis. In particular we treat
decomposition as well as hierarchical decomposition. Helmholtz decomposition of
motion fields is obtained as a natural by-product of the chosen numerical
method based on vector spherical harmonics. All models are tested on time-lapse
microscopy data depicting fluorescently labelled endodermal cells of a
zebrafish embryo.Comment: The final publication is available at link.springer.co
A Scale-Space Medialness Transform Based on Boundary Concordance Voting
The Concordance-based Medial Axis Transform (CMAT) presented in this paper is a multiscale medial axis (MMA) algorithm that computes the medial response from grey-level boundary measures. This non-linear operator responds only to symmetric structures, overcoming the limitations of linear medial operators which create “side-lobe” responses for symmetric structures and respond to edge structures. In addition, the spatial localisation of the medial axis and the identification of object width is improved in the CMAT algorithm compared with linear algorithms. The robustness of linear medial operators to noise is preserved in our algorithm. The effectiveness of the CMAT is accredited to the concordance property described in this paper. We demonstrate the performance of this method with test figures used by other authors and medical images that are relatively complex in structure. In these complex images the benefit of the improved response of our non-linear operator is clearly visible
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