85,414 research outputs found
Efficient dominant point detection based on discrete curve structure
International audienceIn this paper, we investigate the problem of dominant point detection on digital curves which consists in finding points with local maximum curvature. Thanks to previous studies of the decomposition of curves into sequence of discrete structures [5–7], namely maximal blurred segments of width [13], an initial algorithm has been proposed in [14] to detect dominant points. However, an heuristic strategy is used to identify the dominant points. We now propose a modified algorithm without heuristics but a simple measure of angle. In addition, an application of polygonal simplification is as well proposed to reduce the number of detected dominant points by associating a weight to each of them. The experimental results demonstrate the e and robustness of the proposed method
Director Field Model of the Primary Visual Cortex for Contour Detection
We aim to build the simplest possible model capable of detecting long, noisy
contours in a cluttered visual scene. For this, we model the neural dynamics in
the primate primary visual cortex in terms of a continuous director field that
describes the average rate and the average orientational preference of active
neurons at a particular point in the cortex. We then use a linear-nonlinear
dynamical model with long range connectivity patterns to enforce long-range
statistical context present in the analyzed images. The resulting model has
substantially fewer degrees of freedom than traditional models, and yet it can
distinguish large contiguous objects from the background clutter by suppressing
the clutter and by filling-in occluded elements of object contours. This
results in high-precision, high-recall detection of large objects in cluttered
scenes. Parenthetically, our model has a direct correspondence with the Landau
- de Gennes theory of nematic liquid crystal in two dimensions.Comment: 9 pages, 7 figure
Crystal image analysis using synchrosqueezed transforms
We propose efficient algorithms based on a band-limited version of 2D
synchrosqueezed transforms to extract mesoscopic and microscopic information
from atomic crystal images. The methods analyze atomic crystal images as an
assemblage of non-overlapping segments of 2D general intrinsic mode type
functions, which are superpositions of non-linear wave-like components. In
particular, crystal defects are interpreted as the irregularity of local
energy; crystal rotations are described as the angle deviation of local wave
vectors from their references; the gradient of a crystal elastic deformation
can be obtained by a linear system generated by local wave vectors. Several
numerical examples of synthetic and real crystal images are provided to
illustrate the efficiency, robustness, and reliability of our methods.Comment: 27 pages, 17 figure
Powellsnakes II: a fast Bayesian approach to discrete object detection in multi-frequency astronomical data sets
Powellsnakes is a Bayesian algorithm for detecting compact objects embedded
in a diffuse background, and was selected and successfully employed by the
Planck consortium in the production of its first public deliverable: the Early
Release Compact Source Catalogue (ERCSC). We present the critical foundations
and main directions of further development of PwS, which extend it in terms of
formal correctness and the optimal use of all the available information in a
consistent unified framework, where no distinction is made between point
sources (unresolved objects), SZ clusters, single or multi-channel detection.
An emphasis is placed on the necessity of a multi-frequency, multi-model
detection algorithm in order to achieve optimality
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