3,364 research outputs found
Approximate Nearest Neighbor Fields in Video
We introduce RIANN (Ring Intersection Approximate Nearest Neighbor search),
an algorithm for matching patches of a video to a set of reference patches in
real-time. For each query, RIANN finds potential matches by intersecting rings
around key points in appearance space. Its search complexity is reversely
correlated to the amount of temporal change, making it a good fit for videos,
where typically most patches change slowly with time. Experiments show that
RIANN is up to two orders of magnitude faster than previous ANN methods, and is
the only solution that operates in real-time. We further demonstrate how RIANN
can be used for real-time video processing and provide examples for a range of
real-time video applications, including colorization, denoising, and several
artistic effects.Comment: A CVPR 2015 oral pape
On a fast bilateral filtering formulation using functional rearrangements
We introduce an exact reformulation of a broad class of neighborhood filters,
among which the bilateral filters, in terms of two functional rearrangements:
the decreasing and the relative rearrangements.
Independently of the image spatial dimension (one-dimensional signal, image,
volume of images, etc.), we reformulate these filters as integral operators
defined in a one-dimensional space corresponding to the level sets measures.
We prove the equivalence between the usual pixel-based version and the
rearranged version of the filter. When restricted to the discrete setting, our
reformulation of bilateral filters extends previous results for the so-called
fast bilateral filtering. We, in addition, prove that the solution of the
discrete setting, understood as constant-wise interpolators, converges to the
solution of the continuous setting.
Finally, we numerically illustrate computational aspects concerning quality
approximation and execution time provided by the rearranged formulation.Comment: 29 pages, Journal of Mathematical Imaging and Vision, 2015. arXiv
admin note: substantial text overlap with arXiv:1406.712
Dynamic load balancing in parallel KD-tree k-means
One among the most influential and popular data mining methods is the k-Means algorithm for cluster analysis.
Techniques for improving the efficiency of k-Means have been
largely explored in two main directions. The amount of computation can be significantly reduced by adopting geometrical constraints and an efficient data structure, notably a multidimensional binary search tree (KD-Tree). These techniques allow to reduce the number of distance computations the algorithm performs at each iteration. A second direction is parallel processing, where data and computation loads are distributed over many processing nodes. However, little work has been done to provide a parallel formulation of the efficient sequential techniques based on KD-Trees. Such approaches are expected to have an irregular distribution of computation load and can suffer from load imbalance. This issue has so far limited the adoption of these efficient k-Means variants in parallel computing environments. In this work, we provide a parallel formulation of the KD-Tree based k-Means algorithm for distributed memory systems and address its load balancing
issue. Three solutions have been developed and tested. Two
approaches are based on a static partitioning of the data set and a third solution incorporates a dynamic load balancing policy
Learning Sparse High Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks
Bilateral filters have wide spread use due to their edge-preserving
properties. The common use case is to manually choose a parametric filter type,
usually a Gaussian filter. In this paper, we will generalize the
parametrization and in particular derive a gradient descent algorithm so the
filter parameters can be learned from data. This derivation allows to learn
high dimensional linear filters that operate in sparsely populated feature
spaces. We build on the permutohedral lattice construction for efficient
filtering. The ability to learn more general forms of high-dimensional filters
can be used in several diverse applications. First, we demonstrate the use in
applications where single filter applications are desired for runtime reasons.
Further, we show how this algorithm can be used to learn the pairwise
potentials in densely connected conditional random fields and apply these to
different image segmentation tasks. Finally, we introduce layers of bilateral
filters in CNNs and propose bilateral neural networks for the use of
high-dimensional sparse data. This view provides new ways to encode model
structure into network architectures. A diverse set of experiments empirically
validates the usage of general forms of filters
Fast and Accurate Bilateral Filtering using Gauss-Polynomial Decomposition
The bilateral filter is a versatile non-linear filter that has found diverse
applications in image processing, computer vision, computer graphics, and
computational photography. A widely-used form of the filter is the Gaussian
bilateral filter in which both the spatial and range kernels are Gaussian. A
direct implementation of this filter requires operations per
pixel, where is the standard deviation of the spatial Gaussian. In
this paper, we propose an accurate approximation algorithm that can cut down
the computational complexity to per pixel for any arbitrary
(constant-time implementation). This is based on the observation that the range
kernel operates via the translations of a fixed Gaussian over the range space,
and that these translated Gaussians can be accurately approximated using the
so-called Gauss-polynomials. The overall algorithm emerging from this
approximation involves a series of spatial Gaussian filtering, which can be
implemented in constant-time using separability and recursion. We present some
preliminary results to demonstrate that the proposed algorithm compares
favorably with some of the existing fast algorithms in terms of speed and
accuracy.Comment: To appear in the IEEE International Conference on Image Processing
(ICIP 2015
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