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Interacting Multiple Model Methods in Target Tracking: A Survey
The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The main feature of thisExpand
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Fast and accurate Polar Fourier transform
Abstract In a wide range of applied problems of 2D and 3D imaging a continuous formulation of the problem places great emphasis on obtaining and manipulating the Fourier transform in PolarExpand
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A Framework for Discrete Integral Transformations I-The Pseudopolar Fourier Transform
TLDR
The Fourier transform of a continuous function, evaluated at frequencies expressed in polar coordinates, is an important conceptual tool for understanding physical continuum phenomena. Expand
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Fast adaptive wavelet packet image compression
TLDR
We provide a fast numerical implementation of the best wavelet packet algorithm in order to demonstrate that an advantage can be gained by constructing a basis adapted to a target image. Expand
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Deblocking of block-transform compressed images using weighted sums of symmetrically aligned pixels
TLDR
A new class of related algorithms for deblocking block-transform compressed images and video sequences is proposed in this paper. Expand
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Automatic segmentation of moving objects in video sequences: a region labeling approach
TLDR
This paper presents a new method for automatic segmentation of moving objects in image sequences for VOP extraction using graph labeling, based on motion information. Expand
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Fast gradient methods based on global motion estimation for video compression
TLDR
This paper presents a fast global motion estimation (GME) algorithm based on gradient methods (GM), which can be used for real-time applications, such as MPEG4 video compression. Expand
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Pseudopolar-based estimation of large translations, rotations, and scalings in images
TLDR
We use the pseudopolar (PP) Fourier transform to achieve substantial improved approximations of the polar and log-polar Fourier transforms of an image. Expand
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SpectralCAT: Categorical spectral clustering of numerical and nominal data
TLDR
We present an automated technique that performs an unsupervised clustering of high-dimensional data with anywhere from four to thousands of dimensions, which contains either numerical or nominal or mix of numerical and nominal attributes. Expand
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