29,939 research outputs found
Solving the subset-sum problem with a light-based device
We propose a special computational device which uses light rays for solving
the subset-sum problem. The device has a graph-like representation and the
light is traversing it by following the routes given by the connections between
nodes. The nodes are connected by arcs in a special way which lets us to
generate all possible subsets of the given set. To each arc we assign either a
number from the given set or a predefined constant. When the light is passing
through an arc it is delayed by the amount of time indicated by the number
placed in that arc. At the destination node we will check if there is a ray
whose total delay is equal to the target value of the subset sum problem (plus
some constants).Comment: 14 pages, 6 figures, Natural Computing, 200
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
Online handwritten Chinese text recognition (OHCTR) is a challenging problem
as it involves a large-scale character set, ambiguous segmentation, and
variable-length input sequences. In this paper, we exploit the outstanding
capability of path signature to translate online pen-tip trajectories into
informative signature feature maps using a sliding window-based method,
successfully capturing the analytic and geometric properties of pen strokes
with strong local invariance and robustness. A multi-spatial-context fully
convolutional recurrent network (MCFCRN) is proposed to exploit the multiple
spatial contexts from the signature feature maps and generate a prediction
sequence while completely avoiding the difficult segmentation problem.
Furthermore, an implicit language model is developed to make predictions based
on semantic context within a predicting feature sequence, providing a new
perspective for incorporating lexicon constraints and prior knowledge about a
certain language in the recognition procedure. Experiments on two standard
benchmarks, Dataset-CASIA and Dataset-ICDAR, yielded outstanding results, with
correct rates of 97.10% and 97.15%, respectively, which are significantly
better than the best result reported thus far in the literature.Comment: 14 pages, 9 figure
Orbital angular momentum 25 years on [invited]
Twenty-five years ago Allen, Beijersbergen, Spreeuw, and Woerdman published their seminal paper establishing that light beams with helical phase-fronts carried an orbital angular momentum. Previously orbital angular momentum had been associated only with high-order atomic/molecular transitions and hence considered to be a rare occurrence. The realization that every photon in a laser beam could carry an orbital angular momentum that was in excess of the angular momentum associated with photon spin has led both to new understandings of optical effects and various applications. These applications range from optical manipulation, imaging and quantum optics, to optical communications. This brief review will examine some of the research in the field to date and consider what future directions might hold
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer
graphics has a long history and an appealing elegance, but it has proved
difficult to directly implement. Instead, most vision tasks are approached via
complex bottom-up processing pipelines. Here we show that it is possible to
write short, simple probabilistic graphics programs that define flexible
generative models and to automatically invert them to interpret real-world
images. Generative probabilistic graphics programs consist of a stochastic
scene generator, a renderer based on graphics software, a stochastic likelihood
model linking the renderer's output and the data, and latent variables that
adjust the fidelity of the renderer and the tolerance of the likelihood model.
Representations and algorithms from computer graphics, originally designed to
produce high-quality images, are instead used as the deterministic backbone for
highly approximate and stochastic generative models. This formulation combines
probabilistic programming, computer graphics, and approximate Bayesian
computation, and depends only on general-purpose, automatic inference
techniques. We describe two applications: reading sequences of degraded and
adversarially obscured alphanumeric characters, and inferring 3D road models
from vehicle-mounted camera images. Each of the probabilistic graphics programs
we present relies on under 20 lines of probabilistic code, and supports
accurate, approximately Bayesian inferences about ambiguous real-world images.Comment: The first two authors contributed equally to this wor
- …