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The estimation and correction of handwritten word skew is a difficult and challenging task since it has to be independent of the variations due to handwriting style and writing conditions. In this paper, a coarse-to-fine technique that integrates core-region information is presented. At first, a rough estimation and correction of the skew is accomplished by cutting vertically the word in two overlapping...
The detection and correction of document skew is one of the most important document image analysis steps. The ICDAR2013 Document Image Skew Estimation Contest (DISEC'13) is the first contest which is dedicated to record recent advances in the field of skew estimation using well established evaluation performance measures on a variety of printed document images. The benchmarking dataset that is used...
Probability hypothesis density (PHD) filtering, implemented using particle filters, is a Bayesian technique used to non-linearly track multiple objects. In this paper, we propose a new approach based on PHD particle filters (PHD-PF) to automatically track the number of magnetoencephalography (MEG) neural dipole sources and their unknown states. In particular, by separating the MEG measurements using...
Document skew detection is often done by the use of horizontal projections. In this paper, we introduce a new document skew detection approach that is based on vertical projections as well as bounding box minimization criterion. We motivated by the fact that the majority of the Latin characters have vertical strokes. We claim that the proposed approach is more efficient and gives more accurate results...
Parameter estimation of biological signals such as the electrocardiogram (ECG) is of key clinical significance and can be used to monitor cardiac health and diagnose heart diseases. However, statistical ECG models with unknown parameters depend upon a priori parameters such as mean cardiac frequency and user-specified parameters such as the number of harmonics in the ECG model. These parameters can...
We investigate the target tracking problem of adapting asymmetric multi-modal sensing operation platforms using radio frequency (RF) radar and electro-optical (EO) sensors. Although the multi-modality framework allows for the integration of complementary information, there are many challenges to overcome, including targets with different energy returns, and information loss due to low signal-to-noise...
Sequential Monte Carlo particle filters (PFs) are useful for estimating nonlinear non-Gaussian dynamic system parameters. As these algorithms are recursive, their real-time implementation can be computationally complex. In this paper, we analyze the bottlenecks in existing parallel PF algorithms, and we propose a new approach that integrates parallel PFs with independent Metropolis-Hastings (PPF-IMH)...
Two new space-time-frequency direction of arrival estimation algorithms are presented that decrease the estimation variance under specific conditions of narrow differential direction of arrival when multiple signals impinge upon the sensor array. The first algorithm, called Wide-Lane, provides a modest extension of existing space-time-frequency (STF) techniques by integrating a wide path along the...
We propose an agile sensing algorithm to optimally select the transmission waveform of a multiple-input, multiple-output (MIMO) radar system in order to improve target localization. Specifically, we first derive the Cramer-Rao lower bound (CRLB) for the joint estimation of the antenna reflection coefficients and the range and direction-of-arrival of a stationary target using MIMO radar with colocated...
We consider two Monte-Carlo based methods for characterizing the scattering function of rapidly-varying sea clutter. The first method uses multiple particle filtering to estimate the clutter space-time covariance matrix by exploiting the structure of the matrix. This method is then compared to a baseline approach that estimates the clutter covariance matrix based on the Weibull distribution approximation...
We propose an adaptive estimation method for the spatio- temporal covariance matrix of sea clutter. The motivation is to enable adaptive detection approaches that rely on accurate estimation of this matrix. The method involves vectorization of the equations for the dynamical system model governing the temporal evolution of the clutter matrix followed by a multiple particle filtering approach to deal...
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