1,720,993 research outputs found
Phase transitions curve evolution, and the control of semiconductor manufacturing processes
No full textBerg, Jordan M.; Yezzi, A.; Tannenbaum, Allen. (1997). Phase transitions curve evolution, and the control of semiconductor manufacturing processes. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/3034
Conformal metrics and true "gradient flows" for curves
Full text linkWe wish to endow the manifold M of smooth curves in Rn with a Riemannian metric that allows us to treat continuous morphs (homotopies) between two curves c0 and c1 as trajectories with computable lengths which are independent of the parameterization or representation of the two curves (and the curves making up the morph between them). We may then define the distance between the two curves using the trajectory of minimal length (geodesic) between them, assuming such a minimizing trajectory exists. At first we attempt to utilize the metric structure implied rather unanimously by the past twenty years or so of shape optimization literature in computer vision. This metric arises as the unique metric which validates the common references to a wide variety of contour evolution models in the literature as "gradient flows" to various formulated energy functionals. Surprisingly, this implied metric yields a pathological and useless notion of distance between curves. In this paper, we show how this metric can be minimally modified using conformal factors that depend upon a curve's total arclength. A nice property of these new conformal metrics is that all active contour models that have been called "gradient flows" in the past will constitute true gradient flows with respect to these new metrics under specific time reparameterizations
Directionally paired principal component analysis for bivariate estimation problems
No full textWe propose Directionally Paired Principal Component Analysis (DP-PCA), a novel linear dimension-reduction model for estimating coupled yet partially observable variable sets. Unlike partial least squares methods (e.g., partial least squares regression and canonical correlation analysis) that maximize correlation/covariance between the two datasets, our DP-PCA directly minimizes, either conditionally or unconditionally, the reconstruction and prediction errors for the observable and unobservable part, respectively. We demonstrate the optimality of the proposed DP-PCA approach, we compare and evaluate relevant linear cross-decomposition methods with data reconstruction and prediction experiments on synthetic Gaussian data, multi-target regression datasets, and a single-channel image dataset. Results show that when only a single pair of bases is allowed, the conditional DP-PCA achieves the lowest reconstruction error on the observable part and the total variable sets as a whole; meanwhile, the unconditional DP-PCA reaches the lowest prediction errors on the unobservable part. When an extra budget is allowed for the observable part's PCA basis, one can reach an optimal solution using a combined method: standard PCA for the observable part and unconditional DP-PCA for the unobservable part
Geometric seismic-wave inversion by the boundary element method
No full textSurface-wave methods are widely used in earth sciences and engineering for the geometric characterization of geological bodies and tectonic structures of the subsurface. These techniques exploit the dispersive nature of Rayleigh waves to indirectly estimate shear wave velocity profiles from surface-wave measurements; however, they are limited to parallel-layered geometries. To overcome such limitations, we present a new class of geometric inverse models for a full waveform inversion (FWI) based on the boundary element method (BEM). The proposed approach enables an effective identification of two dimensional (2D) subsurface geometries by directly estimating the shape of laterally varying interfaces from raw measurements. It thus aims at filling the gap between the standard simplistic parallellayered-based inversion and that of more complex three-dimensional (3D) geometries based on finite element methods (FEMs). Numerical tests on synthetic data unveil the effectiveness of the inverse algorithm, and its applicability to field measurements is finally presented
A nonparametric statistical method for image segmentation using information theory and curve evolution
No full textIn this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Multiple Object Tracking via Prediction and Filtering with a Sobolev-Type Metric on Curves
No full textThe problem of multi-target tracking of deforming objects in
video sequences arises in many situations in image processing and com-
puter vision. Many algorithms based on finite dimensional particle fil-
ters have been proposed. Recently, particle filters for infinite dimensional
Shape Spaces have been proposed although predictions are restricted to
a low dimensional subspace. We try to extend this approach using pre-
dictions in the whole shape space based on a Sobolev-type metric for
curves which allows unrestricted infinite dimensional deformations. For
the measurement model, we utilize contours which locally minimize a
segmentation energy function and focus on the multiple contour track-
ing framework when there are many local minima of the segmentation
energy to be detected. The method detects figures moving without the
need of initialization and without the need for prior shape knowledge of
the objects tracked
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