125,580 research outputs found
Feature Extraction for image super-resolution using finite rate of innovation principles
To understand a real-world scene from several multiview pictures, it is necessary to find
the disparities existing between each pair of images so that they are correctly related to one
another. This process, called image registration, requires the extraction of some specific
information about the scene. This is achieved by taking features out of the acquired
images. Thus, the quality of the registration depends largely on the accuracy of the
extracted features.
Feature extraction can be formulated as a sampling problem for which perfect re-
construction of the desired features is wanted. The recent sampling theory for signals with
finite rate of innovation (FRI) and the B-spline theory offer an appropriate new frame-
work for the extraction of features in real images. This thesis first focuses on extending the
sampling theory for FRI signals to a multichannel case and then presents exact sampling
results for two different types of image features used for registration: moments and edges.
In the first part, it is shown that the geometric moments of an observed scene can
be retrieved exactly from sampled images and used as global features for registration. The
second part describes how edges can also be retrieved perfectly from sampled images for
registration purposes. The proposed feature extraction schemes therefore allow in theory
the exact registration of images. Indeed, various simulations show that the proposed
extraction/registration methods overcome traditional ones, especially at low-resolution.
These characteristics make such feature extraction techniques very appropriate for
applications like image super-resolution for which a very precise registration is needed. The
quality of the super-resolved images obtained using the proposed feature extraction meth-
ods is improved by comparison with other approaches. Finally, the notion of polyphase
components is used to adapt the image acquisition model to the characteristics of real
digital cameras in order to run super-resolution experiments on real images
GraphMatch: Efficient Large-Scale Graph Construction for Structure from Motion
We present GraphMatch, an approximate yet efficient method for building the
matching graph for large-scale structure-from-motion (SfM) pipelines. Unlike
modern SfM pipelines that use vocabulary (Voc.) trees to quickly build the
matching graph and avoid a costly brute-force search of matching image pairs,
GraphMatch does not require an expensive offline pre-processing phase to
construct a Voc. tree. Instead, GraphMatch leverages two priors that can
predict which image pairs are likely to match, thereby making the matching
process for SfM much more efficient. The first is a score computed from the
distance between the Fisher vectors of any two images. The second prior is
based on the graph distance between vertices in the underlying matching graph.
GraphMatch combines these two priors into an iterative "sample-and-propagate"
scheme similar to the PatchMatch algorithm. Its sampling stage uses Fisher
similarity priors to guide the search for matching image pairs, while its
propagation stage explores neighbors of matched pairs to find new ones with a
high image similarity score. Our experiments show that GraphMatch finds the
most image pairs as compared to competing, approximate methods while at the
same time being the most efficient.Comment: Published at IEEE 3DV 201
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Crowdsourcing platforms are now extensively used for conducting subjective
pairwise comparison studies. In this setting, a pairwise comparison dataset is
typically gathered via random sampling, either \emph{with} or \emph{without}
replacement. In this paper, we use tools from random graph theory to analyze
these two random sampling methods for the HodgeRank estimator. Using the
Fiedler value of the graph as a measurement for estimator stability
(informativeness), we provide a new estimate of the Fiedler value for these two
random graph models. In the asymptotic limit as the number of vertices tends to
infinity, we prove the validity of the estimate. Based on our findings, for a
small number of items to be compared, we recommend a two-stage sampling
strategy where a greedy sampling method is used initially and random sampling
\emph{without} replacement is used in the second stage. When a large number of
items is to be compared, we recommend random sampling with replacement as this
is computationally inexpensive and trivially parallelizable. Experiments on
synthetic and real-world datasets support our analysis
Contour Detection from Deep Patch-level Boundary Prediction
In this paper, we present a novel approach for contour detection with
Convolutional Neural Networks. A multi-scale CNN learning framework is designed
to automatically learn the most relevant features for contour patch detection.
Our method uses patch-level measurements to create contour maps with
overlapping patches. We show the proposed CNN is able to to detect large-scale
contours in an image efficienly. We further propose a guided filtering method
to refine the contour maps produced from large-scale contours. Experimental
results on the major contour benchmark databases demonstrate the effectiveness
of the proposed technique. We show our method can achieve good detection of
both fine-scale and large-scale contours.Comment: IEEE International Conference on Signal and Image Processing 201
GraphLab: A New Framework for Parallel Machine Learning
Designing and implementing efficient, provably correct parallel machine
learning (ML) algorithms is challenging. Existing high-level parallel
abstractions like MapReduce are insufficiently expressive while low-level tools
like MPI and Pthreads leave ML experts repeatedly solving the same design
challenges. By targeting common patterns in ML, we developed GraphLab, which
improves upon abstractions like MapReduce by compactly expressing asynchronous
iterative algorithms with sparse computational dependencies while ensuring data
consistency and achieving a high degree of parallel performance. We demonstrate
the expressiveness of the GraphLab framework by designing and implementing
parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and
Compressed Sensing. We show that using GraphLab we can achieve excellent
parallel performance on large scale real-world problems
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