1,721,185 research outputs found
Mitsubishi Electric Research Laboratories
No full textire a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2005 201 Broadway, Cambridge, Massachusetts 02139 MERLCoverPageSide2 Change Detection by Frequency Decomposition: Wave-Back Fatih Porikli Christopher R. Wren Mitsubishi Electric Research Laboratories Cambridge, MA, 02139, USA We introduce a frequency decomposition based background generation and subtraction method that explicitly harnesses the scene dynamics to improve segmentation. This allows us to correctly interpret scenes that would confound appearance-based algorithms by having highvariance background in the presence of low-contrast targets, specifically when the background pixels are well modeled as cyclostationary random processes. In other words, we can distinguish near-periodic temporal patterns induced by real-world physics: the motion of plants driven by wind, the action of waves on a beach, an
Multimedia quality assessment
Get PDFThis IEEE Signal Processing Magazine forum discusses the latest advances and challenges in multimedia quality assessment. The forum members bring their expert insights into issues such as perceptual models and quality measures for future applications such as three-dimensional (3-D) videos and interactivity media. The invited forum members are Al Bovik (University of Texas), Chris Plack (University of Manchester), Ghassan AlRegib (Georgia Institute of Technology), Joyce Farrell (Stanford University), Patrick Le Callet (University de Nantes), Quan Huynh-Thu (Tech-nicolor), Sebastian M??ller (Deutsche Telekom Labs, TU Berlin), and Stefan Winkler (Advanced Digital Sciences Center). The moderator of this forum is Dr. Fatih Porikli (MERL, Cambridge)
Automatic Image Segmentation by Wave Propagation
No full textWe develop a level set based region growing method for automatic partitioning of color images into segments. Previous attempts at image segmentation either suffer from requiring a priori information to initialize regions, being computationally complex, or fail to establish the color consistency and spatial connectivity at the same time. Here, we represent the segmentation problem as monotonic wave propagation in an absorbing medium with varying front speeds. We iteratively emit waves from the selected base points. At a base point, the local variance of the data reaches a minimum, which indicates the base point is a suitable representative of its local neighborhood. We determine local variance by applying a hierarchical gradient operator. The speed of the wave is determined by the color similarity of the point on the front to the current coverage of the wave, and by edge information. Thus, the wave advances in an anisotropic spatial-color space. The absorbing function acts as a stopping criterion of the wave front. We take advantage of fast marching methods to solve the Eikonal equation for finding the travel times of the waves. Our method is superior to the linkage-based region growing techniques since it prevents leakage and imposes compactness on the region without over-smoothing its boundary. Furthermore, we can deal with sharp corners and changes in topology. The automatic segmentation method is Eulerian, thus it is computationally efficient. We compare our results with a non-Eulerian approach that evaluates the arrival times of multiple waves as well. Our experiments illustrate the robustness, accuracy, and effectiveness of the proposed method
Fast Construction Of Covariance Matrices For Arbitrary Size Image
No full textWe propose an integral image based algorithm to extract feature covariance matrices of all possible rectangular regions within a given image. Covariance is an essential indicator of how much the deviation of two or more variables match. In our case, these variables correspond to point-wise features, e.g. coordinates, color values, gradients, edge magnitude and orientation, local histograms, filter responses, etc. We significantly improve the speed of the covariance computation by taking advantage of the spatial arrangement of image points using integral images, which are intermediate representations used for calculation of region sums. Each point of the integral image corresponds to the summation of all point values inside the feature image rectangle bounded by the upper left corner and the point of interest. Using this representation, any rectangular region sum can be computed in constant time. We follow a similar idea for fast calculation of region covariance. We construct integral images for all separate features as well as integral images of the multiplication of any two feature combinations. Using these set of integral images and region corner point coordinates, we directly extract the covariance matrix coefficients. We show that the proposed method reduces the computational load to quadratic time
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Multi-Kernel Object Tracking
No full textIn this paper, we present an object tracking algorithm for the low-frame-rate video in which objects have fast motion. The conventional mean-shift tracking fails in case the relocation of an object is large and its regions between the consecutive frames do not overlap. We provide a solution to this problem by using multiple kernels centered at the high motion areas. In addition, we improve the convergence properties of the mean-shift by integrating two likelihood terms, background and template similarities, in the iterative update mechanism. Our simulations prove the effectiveness of the proposed method
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