2,085 research outputs found
Video Registration in Egocentric Vision under Day and Night Illumination Changes
With the spread of wearable devices and head mounted cameras, a wide range of
application requiring precise user localization is now possible. In this paper
we propose to treat the problem of obtaining the user position with respect to
a known environment as a video registration problem. Video registration, i.e.
the task of aligning an input video sequence to a pre-built 3D model, relies on
a matching process of local keypoints extracted on the query sequence to a 3D
point cloud. The overall registration performance is strictly tied to the
actual quality of this 2D-3D matching, and can degrade if environmental
conditions such as steep changes in lighting like the ones between day and
night occur. To effectively register an egocentric video sequence under these
conditions, we propose to tackle the source of the problem: the matching
process. To overcome the shortcomings of standard matching techniques, we
introduce a novel embedding space that allows us to obtain robust matches by
jointly taking into account local descriptors, their spatial arrangement and
their temporal robustness. The proposal is evaluated using unconstrained
egocentric video sequences both in terms of matching quality and resulting
registration performance using different 3D models of historical landmarks. The
results show that the proposed method can outperform state of the art
registration algorithms, in particular when dealing with the challenges of
night and day sequences
A deep learning pipeline for product recognition on store shelves
Recognition of grocery products in store shelves poses peculiar challenges.
Firstly, the task mandates the recognition of an extremely high number of
different items, in the order of several thousands for medium-small shops, with
many of them featuring small inter and intra class variability. Then, available
product databases usually include just one or a few studio-quality images per
product (referred to herein as reference images), whilst at test time
recognition is performed on pictures displaying a portion of a shelf containing
several products and taken in the store by cheap cameras (referred to as query
images). Moreover, as the items on sale in a store as well as their appearance
change frequently over time, a practical recognition system should handle
seamlessly new products/packages. Inspired by recent advances in object
detection and image retrieval, we propose to leverage on state of the art
object detectors based on deep learning to obtain an initial productagnostic
item detection. Then, we pursue product recognition through a similarity search
between global descriptors computed on reference and cropped query images. To
maximize performance, we learn an ad-hoc global descriptor by a CNN trained on
reference images based on an image embedding loss. Our system is
computationally expensive at training time but can perform recognition rapidly
and accurately at test time
Uniform convergence of V-cycle multigrid algorithms for two-dimensional fractional Feynman-Kac equation
In this paper we derive new uniform convergence estimates for the V-cycle MGM
applied to symmetric positive definite Toeplitz block tridiagonal matrices, by
also discussing few connections with previous results. More concretely, the
contributions of this paper are as follows: (1) It tackles the Toeplitz systems
directly for the elliptic PDEs. (2) Simple (traditional) restriction operator
and prolongation operator are employed in order to handle general Toeplitz
systems at each level of the recursion. Such a technique is then applied to
systems of algebraic equations generated by the difference scheme of the
two-dimensional fractional Feynman-Kac equation, which describes the joint
probability density function of non-Brownian motion. In particular, we consider
the two coarsening strategies, i.e., doubling the mesh size (geometric MGM) and
Galerkin approach (algebraic MGM), which lead to the distinct coarsening
stiffness matrices in the general case: however, several numerical experiments
show that the two algorithms produce almost the same error behaviour.Comment: 26 page
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