Gray-level Texture Characterization Based on a New Adaptive

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Gray-level Texture Characterization Based on a New Adaptive Nonlinear Auto-Regressive Filter

In this paper, we propose a new nonlinear exponential adaptive two-dimensional (2-D) filter for texture characterization. The filter coefficients are updated with the Least Mean Square (LMS) algorithm. The proposed nonlinear model is used for texture characterization with a 2-D Auto-Regressive (AR) adaptive model. The main advantage of the new nonlinear exponential adaptive 2-D filter is the reduced number of coefficients used to characterize the nonlinear parametric models of images regarding the 2-D second-order Volterra model. Whatever the degree of the non-linearity, the problem results in the same number of coefficients as in the linear case. The characterization efficiency of the proposed exponential model is compared to the one provided by both 2-D linear and Volterra filters and the cooccurrence matrix method. The comparison is based on two criteria usually used to evaluate the features discriminating ability and the class quantification. Extensive experiments proved that the exponential model coefficients give better results in texture discrimination than several other parametric features even in a noisy context

Caractérisation des textures avec les coefficients 2-D transverses et de réflexion : Une étude comparative

Dans cet article, on traite le problème de la caractérisation des textures avec de nouvelles approches de modélisation paramétrique. On se propose de fournir une réponse à la question suivante : lesquels parmi les coefficients 2-D transverses ou de réflexion 2-D ( treillis) permettent-ils de mieux caractériser les textures ? Pour ceci, on considère plusieurs classes de textures et on estime pour chaque texture les deux types de coefficients avec l'algorithme adaptatif 2-D FLRLS (2-D Fast Lattice Recursive Least Square). Comme critère de comparaison, on définit un pouvoir séparateur (rapport des variances entre-classes et dans la classe) pour chaque coefficients. On montre que les coefficients de réflexion présentent un meilleur pouvoir séparateur que celui des coefficients transverses

Is type 1 diabetes a chaotic phenomenon?

A database of ten type 1 diabetes patients wearing a continuous glucose monitoring device has enabled to record their blood glucose continuous variations every minute all day long during fourteen consecutive days. These recordings represent, for each patient, a time series consisting of 1 value of glycaemia per minute during 24 hours and 14 days, i.e., 20,160 data point. Thus, while using numerical methods, these time series have been anonymously analyzed. Nevertheless, because of the stochastic inputs induced by daily activities of any human being, it has not been possible to discriminate chaos from noise. So, we have decided to keep only the 14 nights of these ten patients. Then, the determination of the time delay and embedding dimension according to the delay coordinate embedding method has allowed us to estimate for each patient the correlation dimension and the maximal Lyapunov exponent. This has led us to show that type 1 diabetes could indeed be a chaotic phenomenon. Once this result has been confirmed by the determinism test, we have computed the Lyapunov time and found that the limit of predictability of this phenomenon is nearly equal to half the 90-minutes sleep-dream cycle. We hope that our results will prove to be useful to characterize and predict blood glucose variations

Fire Tracking in Video Sequences Using Geometric Active Contours Controlled by Artificial Neural Network

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Multi-Object tracking based on Kalman Filtering Combining Radar and Image Measurements

International audienceThe purpose of this paper is to develop a tracking system. The designed platform is based on two kinds of physical sensors: a doppler radar module and HD Camera. All the measurements from those modules are processed using a data fusion method. In this work, a method based on the Gaussian mixture model is used for foreground detection (i.e. background subtraction). After that our move to a filtering step which is used to refine the detection results firstly obtained, then a tracking process is introduced. As a final stage, all the vision-based measurements are combined with the processed radar raw data. Here, the goal is to perfectly estimate the target velocity in the real time. Added to target 2D positions, this speed information is considered as a third dimension. This is very useful in many applications such as traffic control, robotics, autonomous vehicles etc. In this work a set of experiments is conducted in order to validate the developed tracking method

A Neural Adaptive Level Set Method for Wildland Forest Fire Tracking

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