162,649 research outputs found
Global versus Localized Generative Adversarial Nets
In this paper, we present a novel localized Generative Adversarial Net (GAN)
to learn on the manifold of real data. Compared with the classic GAN that {\em
globally} parameterizes a manifold, the Localized GAN (LGAN) uses local
coordinate charts to parameterize distinct local geometry of how data points
can transform at different locations on the manifold. Specifically, around each
point there exists a {\em local} generator that can produce data following
diverse patterns of transformations on the manifold. The locality nature of
LGAN enables local generators to adapt to and directly access the local
geometry without need to invert the generator in a global GAN. Furthermore, it
can prevent the manifold from being locally collapsed to a dimensionally
deficient tangent subspace by imposing an orthonormality prior between
tangents. This provides a geometric approach to alleviating mode collapse at
least locally on the manifold by imposing independence between data
transformations in different tangent directions. We will also demonstrate the
LGAN can be applied to train a robust classifier that prefers locally
consistent classification decisions on the manifold, and the resultant
regularizer is closely related with the Laplace-Beltrami operator. Our
experiments show that the proposed LGANs can not only produce diverse image
transformations, but also deliver superior classification performances
Craquelure as a Graph: Application of Image Processing and Graph Neural Networks to the Description of Fracture Patterns
Cracks on a painting is not a defect but an inimitable signature of an
artwork which can be used for origin examination, aging monitoring, damage
identification, and even forgery detection. This work presents the development
of a new methodology and corresponding toolbox for the extraction and
characterization of information from an image of a craquelure pattern.
The proposed approach processes craquelure network as a graph. The graph
representation captures the network structure via mutual organization of
junctions and fractures. Furthermore, it is invariant to any geometrical
distortions. At the same time, our tool extracts the properties of each node
and edge individually, which allows to characterize the pattern statistically.
We illustrate benefits from the graph representation and statistical features
individually using novel Graph Neural Network and hand-crafted descriptors
correspondingly. However, we also show that the best performance is achieved
when both techniques are merged into one framework. We perform experiments on
the dataset for paintings' origin classification and demonstrate that our
approach outperforms existing techniques by a large margin.Comment: Published in ICCV 2019 Workshop
Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand
Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications
Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
Lens modeling is the key to successful and meaningful automated strong
galaxy-scale gravitational lens detection. We have implemented a lens-modeling
"robot" that treats every bright red galaxy (BRG) in a large imaging survey as
a potential gravitational lens system. Using a simple model optimized for
"typical" galaxy-scale lenses, we generate four assessments of model quality
that are used in an automated classification. The robot infers the lens
classification parameter H that a human would have assigned; the inference is
performed using a probability distribution generated from a human-classified
training set, including realistic simulated lenses and known false positives
drawn from the HST/EGS survey. We compute the expected purity, completeness and
rejection rate, and find that these can be optimized for a particular
application by changing the prior probability distribution for H, equivalent to
defining the robot's "character." Adopting a realistic prior based on the known
abundance of lenses, we find that a lens sample may be generated that is ~100%
pure, but only ~20% complete. This shortfall is due primarily to the
over-simplicity of the lens model. With a more optimistic robot, ~90%
completeness can be achieved while rejecting ~90% of the candidate objects. The
remaining candidates must be classified by human inspectors. We are able to
classify lens candidates by eye at a rate of a few seconds per system,
suggesting that a future 1000 square degree imaging survey containing 10^7
BRGs, and some 10^4 lenses, could be successfully, and reproducibly, searched
in a modest amount of time. [Abridged]Comment: 17 pages, 11 figures, submitted to Ap
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