85,161 research outputs found
Detecting and localizing edges composed of steps, peaks and roofs
It is well known that the projection of depth or orientation
discontinuities in a physical scene results in image
intensity edges which are not ideal step edges but
are more typically a combination of steps, peak and
roof profiles. However most edge detection schemes
ignore the composite nature of these edges, resulting
in systematic errors in detection and localization. We
address the problem of detecting and localizing these
edges, while at the same time also solving the problem
of false responses in smoothly shaded regions with
constant gradient of the image brightness. We show
that a class of nonlinear filters, known as quadratic
filters, are appropriate for this task, while linear filters
are not. A series of performance criteria are derived
for characterizing the SNR, localization and multiple
responses of these filters in a manner analogous to
Canny's criteria for linear filters. A two-dimensional
version of the approach is developed which has the
property of being able to represent multiple edges at the
same location and determine the orientation of each
to any desired precision. This permits junctions to be
localized without rounding. Experimental results are
presented
A framework for community detection in heterogeneous multi-relational networks
There has been a surge of interest in community detection in homogeneous
single-relational networks which contain only one type of nodes and edges.
However, many real-world systems are naturally described as heterogeneous
multi-relational networks which contain multiple types of nodes and edges. In
this paper, we propose a new method for detecting communities in such networks.
Our method is based on optimizing the composite modularity, which is a new
modularity proposed for evaluating partitions of a heterogeneous
multi-relational network into communities. Our method is parameter-free,
scalable, and suitable for various networks with general structure. We
demonstrate that it outperforms the state-of-the-art techniques in detecting
pre-planted communities in synthetic networks. Applied to a real-world Digg
network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure
Composite Fermions, Edge Currents and the Fractional Quantum Hall Effect
We present a theory of composite fermion edge states and their transport
properties in the fractional and integer quantum Hall regimes. We show that the
effective electro-chemical potentials of composite fermions at the edges of a
Hall bar differ, in general, from those of electrons. An expression for the
difference is given. Composite fermion edge states of three different types are
identified. Two of the three types have no analog in previous theories of the
integer or fractional quantum Hall effect. The third type includes the usual
integer edge states. The direction of propagation of the edge states agrees
with experiment. The present theory yields the observed quantized Hall
conductances at Landau level filling fractions p/(mp+-1), for m=0,2,4, p=
1,2,3,... It explains the results of experiments that involve conduction across
smooth potential barriers and through adiabatic constrictions, and of
experiments that involve selective population and detection of fractional edge
channels. The relationship between the present work and Hartree theories of
composite fermion edge structure is discussed.Comment: 19 pages + 6 figures. Self-unpacking uuencoded postscript. To appear
in Physical Review B. Revised version has more details in the Appendix and a
discussion of one more experiment in Section
How Many Communities Are There?
Stochastic blockmodels and variants thereof are among the most widely used
approaches to community detection for social networks and relational data. A
stochastic blockmodel partitions the nodes of a network into disjoint sets,
called communities. The approach is inherently related to clustering with
mixture models; and raises a similar model selection problem for the number of
communities. The Bayesian information criterion (BIC) is a popular solution,
however, for stochastic blockmodels, the conditional independence assumption
given the communities of the endpoints among different edges is usually
violated in practice. In this regard, we propose composite likelihood BIC
(CL-BIC) to select the number of communities, and we show it is robust against
possible misspecifications in the underlying stochastic blockmodel assumptions.
We derive the requisite methodology and illustrate the approach using both
simulated and real data. Supplementary materials containing the relevant
computer code are available online.Comment: 26 pages, 3 figure
Performance improvement of edge detection based on edge likelihood index
One of the problems of conventional edge detectors is the difficulty in distinguishing noise and true edges correctly using a simple measurement, such as gradient, local energy, or phase congruency. This paper proposes a performance improvement algorithm for edge detection based on a composite measurement called Edge Likelihood Index (ELI). In principle, given a raw edge map obtained from any edge detectors, edge contours can be extracted where gradient, continuity and smoothness of each contour are measured. The ELI of an edge contour is defined as directly proportional to its gradient and length, and inversely proportional to its smoothness, which offers a more flexible representation of true edges, such as those with low gradient, but continuous and smooth. The proposed method was tested on the South Florida data sets, using the Canny edge operator for edge detection, and evaluated using the Receiver Operator Characteristic curves. It can be shown that the proposed method reduces Bayes risk of ROC curves by over 10% in the aggregate test results.published_or_final_versio
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