6,726 research outputs found

    Robust ellipse detection with Gaussian mixture models

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    The Euclidian distance between Gaussian Mixtures has been shown to be robust to perform point set registration (Jian and Vemuri, 2011). We propose to extend this idea for robustly matching a family of shapes (ellipses). Optimisation is performed with an annealing strategy, and the search for occurrences is repeated several times to detect multiple instances of the shape of interest. We compare experimentally our approach to other state-of-the-art techniques on a benchmark database for ellipses, and demonstrate the good performance of our approach

    Globally Optimal Cell Tracking using Integer Programming

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    We propose a novel approach to automatically tracking cell populations in time-lapse images. To account for cell occlusions and overlaps, we introduce a robust method that generates an over-complete set of competing detection hypotheses. We then perform detection and tracking simultaneously on these hypotheses by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor

    Extended Object Tracking: Introduction, Overview and Applications

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    This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

    The Globular Cluster System of the Coma cD Galaxy NGC 4874 from Hubble Space Telescope ACS and WFC3/IR Imaging

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    We present new HST optical and near-infrared (NIR) photometry of the rich globular cluster (GC) system of NGC 4874, the cD galaxy in the core of the Coma cluster (Abell 1656). NGC 4874 was observed with the HST Advanced Camera for Surveys in the F475W (g) and F814W (I) passbands and the Wide Field Camera 3 IR Channel in F160W (H). The GCs in this field exhibit a bimodal optical color distribution with more than half of the GCs falling on the red side at g-I > 1. Bimodality is also present, though less conspicuously, in the optical-NIR I-H color. Consistent with past work, we find evidence for nonlinearity in the g-I versus I-H color-color relation. Our results thus underscore the need for understanding the detailed form of the color-metallicity relations in interpreting observational data on GC bimodality. We also find a very strong color-magnitude trend, or "blue tilt," for the blue component of the optical color distribution of the NGC 4874 GC system. A similarly strong trend is present for the overall mean I-H color as a function of magnitude; for M_814 < -10 mag, these trends imply a steep mass-metallicity scaling with ZMGC1.4±0.4Z\propto M_{\rm GC}^{1.4\pm0.4}, but the scaling is not a simple power law and becomes much weaker at lower masses. As in other similar systems, the spatial distribution of the blue GCs is more extended than that of the red GCs, partly because of blue GCs associated with surrounding cluster galaxies. In addition, the center of the GC system is displaced by 4+/-1 kpc towards the southwest from the luminosity center of NGC 4874, in the direction of NGC 4872. Finally, we remark on a dwarf elliptical galaxy with a noticeably asymmetrical GC distribution. Interestingly, this dwarf has a velocity of nearly -3000 km/s with respect to NGC 4874; we suggest it is on its first infall into the cluster core and is undergoing stripping of its GC system by the cluster potential.Comment: 24 pages, 20 figures, accepted for publication in Ap

    Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome

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    Tiling arrays make possible a large scale exploration of the genome thanks to probes which cover the whole genome with very high density until 2 000 000 probes. Biological questions usually addressed are either the expression difference between two conditions or the detection of transcribed regions. In this work we propose to consider simultaneously both questions as an unsupervised classification problem by modeling the joint distribution of the two conditions. In contrast to previous methods, we account for all available information on the probes as well as biological knowledge like annotation and spatial dependence between probes. Since probes are not biologically relevant units we propose a classification rule for non-connected regions covered by several probes. Applications to transcriptomic and ChIP-chip data of Arabidopsis thaliana obtained with a NimbleGen tiling array highlight the importance of a precise modeling and the region classification

    Probabilistic facial feature extraction using joint distribution of location and texture information

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    In this work, we propose a method which can extract critical points on a face using both location and texture information. This new approach can automatically learn feature information from training data. It finds the best facial feature locations by maximizing the joint distribution of location and texture parameters. We first introduce an independence assumption. Then, we improve upon this model by assuming dependence of location parameters but independence of texture parameters.We model combined location parameters with a multivariate Gaussian for computational reasons. The texture parameters are modeled with a Gaussian mixture model. It is shown that the new method outperforms active appearance models for the same experimental setup

    Computer vision based techniques for fall detection with application towards assisted living

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    In this thesis, new computer vision based techniques are proposed to detect falls of an elderly person living alone. This is an important problem in assisted living. Different types of information extracted from video recordings are exploited for fall detection using both analytical and machine learning techniques. Initially, a particle filter is used to extract a 2D cue, head velocity, to determine a likely fall event. The human body region is then extracted with a modern background subtraction algorithm. Ellipse fitting is used to represent this shape and its orientation angle is employed for fall detection. An analytical method is used by setting proper thresholds against which the head velocity and orientation angle are compared for fall discrimination. Movement amplitude is then integrated into the fall detector to reduce false alarms. Since 2D features can generate false alarms and are not invariant to different directions, more robust 3D features are next extracted from a 3D person representation formed from video measurements from multiple calibrated cameras. Instead of using thresholds, different data fitting methods are applied to construct models corresponding to fall activities. These are then used to distinguish falls and non-falls. In the final works, two practical fall detection schemes which use only one un-calibrated camera are tested in a real home environment. These approaches are based on 2D features which describe human body posture. These extracted features are then applied to construct either a supervised method for posture classification or an unsupervised method for abnormal posture detection. Certain rules which are set according to the characteristics of fall activities are lastly used to build robust fall detection methods. Extensive evaluation studies are included to confirm the efficiency of the schemes
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