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Joint universal lossy coding and identification of stationary mixing sources with general alphabets
 IEEE Trans. Inform. Theory
"... Abstract — We consider the problem of joint universal variablerate lossy coding and identification for parametric classes of stationary βmixing sources with general (Polish) alphabets. Compression performance is measured in terms of Lagrangians, while identification performance is measured by the ..."
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Abstract — We consider the problem of joint universal variablerate lossy coding and identification for parametric classes of stationary βmixing sources with general (Polish) alphabets. Compression performance is measured in terms of Lagrangians, while identification performance is measured by the variational distance between the true source and the estimated source. Provided that the sources are mixing at a sufficiently fast rate and satisfy certain smoothness and Vapnik–Chervonenkis learnability conditions, it is shown that, for bounded metric distortions, there exist universal schemes for joint lossy compression and identification whose Lagrangian redundancies converge to zero as p Vn log n/n as the block length n tends to infinity, where Vn is the Vapnik–Chervonenkis dimension of a certain class of decision regions defined by the ndimensional marginal distributions of the sources; furthermore, for each n, the decoder can identify ndimensional marginal of the active source up to a ball of radius O ( p Vn log n/n) in variational distance, eventually with probability one. The results are supplemented by several examples of parametric sources satisfying the regularity conditions. Index Terms—Learning, minimumdistance density estimation, twostage codes, universal vector quantization, Vapnik– Chervonenkis dimension. I.
Joint Universal Lossy Coding and Identification of Stationary Mixing Sources
"... Abstract — The problem of joint universal source coding and modeling, treated in the context of lossless codes by Rissanen, was recently generalized to fixedrate lossy coding of finitely parametrized continuousalphabet i.i.d. sources. We extend these results to variablerate lossy block coding of ..."
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Abstract — The problem of joint universal source coding and modeling, treated in the context of lossless codes by Rissanen, was recently generalized to fixedrate lossy coding of finitely parametrized continuousalphabet i.i.d. sources. We extend these results to variablerate lossy block coding of stationary ergodic sources and show that, for bounded metric distortion measures, any finitely parametrized family of stationary sources satisfying suitable mixing, smoothness and Vapnik–Chervonenkis learnability conditions admits universal schemes for joint lossy source coding and identification. We also give several explicit examples of parametric sources satisfying the regularity conditions. I.
1Adaptive Kalman Filtering for Histogrambased Appearance Learning in Infrared Imagery
"... Abstract—Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors including complex target maneuvers, egomotion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection ..."
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Abstract—Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors including complex target maneuvers, egomotion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this work, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering (AKF) problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance leastsquares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary (WSS) process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALSbased algorithm outperforms covariance matching as well as traditional histogram similaritybased methods, achieving subpixel tracking accuracy against the wellknown AMCOM closure sequences and the recent SENSIAC ATR dataset. Index Terms – Appearance learning, histogrambased appearance model, infrared tracking, adaptive Kalman filter