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RotationInvariant Texture Retrieval with gaussianized steerable pyramids
Tzagkarakis, G.; BeferullLozano, B.; Tsakalides, P.
(2005). ArticleThis paper presents a novel rotationinvariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alphastable subGaussian model to capture their nonGaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotationinvariant version of the KullbackLeibler Divergence between their corresponding multivariate Gaussian...
This paper presents a novel rotationinvariant image retrieval scheme based on a transformation of the texture information via a steerable pyramid. First, we fit the distribution of the subband coefficients using a joint alphastable subGaussian model to capture their nonGaussian behavior. Then, we apply a normalization process in order to Gaussianize the coefficients. As a result, the feature extraction step consists of estimating the covariances between the normalized pyramid coefficients. The similarity between two distinct texture images is measured by minimizing a rotationinvariant version of the KullbackLeibler Divergence between their corresponding multivariate Gaussian distributions, where the minimization is performed over a set of rotation angles.
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Lossy network correlated data gathering with highresolution coding
Cristescu, R.; BeferullLozano,
(2005). ArticleWe consider a sensor network measuring correlated data, where the task is to gather all data from the network nodes to a sink. We consider the case where data at nodes is lossy coded with highresolution, and the information measured by the nodes should be available at the sink within certain total and individual distortion bounds. First, we consider the problem of finding the optimal transmission structure and the ratedistortion allocations at the various spatially located nodes, such as to minimize the total power consumption cost of the network. We prove that the optimal transmission structure is the shortest path tree and that the problems of rate and distortion allocation separate in...
We consider a sensor network measuring correlated data, where the task is to gather all data from the network nodes to a sink. We consider the case where data at nodes is lossy coded with highresolution, and the information measured by the nodes should be available at the sink within certain total and individual distortion bounds. First, we consider the problem of finding the optimal transmission structure and the ratedistortion allocations at the various spatially located nodes, such as to minimize the total power consumption cost of the network. We prove that the optimal transmission structure is the shortest path tree and that the problems of rate and distortion allocation separate in the highresolution case, namely, we first find the distortion allocation as a function of the transmission structure, and then the rate allocation is computed. Then, we also study the case when the node positions can be chosen, by finding the optimal node placement when two different targets of interest are considered, namely total power minimization and network lifetime extension.
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Efficient routing with small buffers in dense networks
Barrenetxea, G.; BeferullLozano, B.; Vetterli, M.
(2005). ArticleThe analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases....
The analysis and design of routing algorithms for finite buffer networks requires solving the associated queue network problem which is known to be hard. We propose alternative and more accurate approximation models to the usual Jackson's theorem that give more insight into the effect of routing algorithms on the queue size distributions. Using the proposed approximation models, we analyze and design routing algorithms that minimize overflow losses in grid networks with finite buffers and different communication patterns, namely uniform communication and data gathering. We show that the buffer size required to achieve the maximum possible rate decreases as the network size increases. Motivated by the insight gained in grid networks, we apply the same principles to the design of routing algorithms for random networks with finite buffers that minimize overflow losses. We show that this requires adequately combining shortest path tree routing and traveling salesman routing. Our results show that such specially designed routing algorithms increase the transmitted rate for a given loss probability up to almost three times, on average, with respect to the usual shortest path tree routing.
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Adaptive distributed algorithms for powerefficient data gathering in sensor networks
Acimovic, J.; BeferullLozano, B.; Cristescu, R.
(2005). ArticleIn this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution waveletbased algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide...
In this work, we consider the problem of designing adaptive distributed processing algorithms in large sensor networks that are efficient in terms of minimizing the total power spent for gathering the spatially correlated data from the sensor nodes to a sink node. We take into account both the power spent for purposes of communication as well as the power spent for local computation. Our distributed algorithms are also matched to the nature of the correlated field, namely, for piecewise smooth signals, we provide two distributed multiresolution waveletbased algorithms, while for correlated Gaussian fields, we use distributed prediction based processing. In both cases, we provide distributed algorithms that perform network division into groups of different sizes. The distribution of the group sizes within the network is the result of an optimal tradeoff between the local communication inside each group needed to perform decorrelation, the communication needed to bring the processed data (coefficients) to the sink and the local computation cost, which grows as the network becomes larger. Our experimental results show clearly that important gains in power consumption can be obtained with respect to the case of not performing any distributed decorrelating processing.
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Approximation power of Directionlets
Velisavljevic, V.; BeferullLozano, B.; vetterli, M.; Dragotti, P. L.
(2005). ArticleIn spite of the success of the standard wavelet transform (WT) in image processing, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in only horizontal and vertical directions. Onedimensional (1D) discontinuities in images (edges and contours), which are very important elements in visual perception, intersect too many wavelet basis functions and reduce the sparsity of the representation. To capture efficiently these anisotropic geometrical structures, a more complex multidirectional (MDIR) and anisotropic transform is required. We present a new latticebased perfect reconstruction and critically sampled anisotropic MDIR WT (with the...
In spite of the success of the standard wavelet transform (WT) in image processing, the efficiency of its representation is limited by the spatial isotropy of its basis functions built in only horizontal and vertical directions. Onedimensional (1D) discontinuities in images (edges and contours), which are very important elements in visual perception, intersect too many wavelet basis functions and reduce the sparsity of the representation. To capture efficiently these anisotropic geometrical structures, a more complex multidirectional (MDIR) and anisotropic transform is required. We present a new latticebased perfect reconstruction and critically sampled anisotropic MDIR WT (with the corresponding basis functions called directionlets) that retains the separable filtering and simple filter design from the standard twodimensional (2D) WT and imposes directional vanishing moments (DVM). Furthermore, we show that this novel transform has nonlinear approximation efficiency competitive to the other previously proposed oversampled transform constructions.
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Networked SlepianWolf: Theory, Algorithms and Scaling Laws
Cristescu, R.; BeferullLozano, B.; Vetterli, M.
(2005). ArticleConsider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the datagathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by SlepianWolf coding. The overall minimization can be achieved in two...
Consider a set of correlated sources located at the nodes of a network, and a set of sinks that are the destinations for some of the sources. The minimization of cost functions which are the product of a function of the rate and a function of the path weight is considered, for both the datagathering scenario, which is relevant in sensor networks, and general traffic matrices, relevant for general networks. The minimization is achieved by jointly optimizing a) the transmission structure, which is shown to consist in general of a superposition of trees, and b) the rate allocation across the source nodes, which is done by SlepianWolf coding. The overall minimization can be achieved in two concatenated steps. First, the optimal transmission structure is found, which in general amounts to finding a Steiner tree, and second, the optimal rate allocation is obtained by solving an optimization problem with cost weights determined by the given optimal transmission structure, and with linear constraints given by the SlepianWolf rate region. For the case of data gathering, the optimal transmission structure is fully characterized and a closedform solution for the optimal rate allocation is provided. For the general case of an arbitrary traffic matrix, the problem of finding the optimal transmission structure is NPcomplete. For large networks, in some simplified scenarios, the total costs associated with SlepianWolf coding and explicit communication (conditional encoding based on explicitly communicated side information) are compared. Finally, the design of decentralized algorithms for the optimal rate allocation is analyzed.
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Granular system models for real time simulation
Pla Castells, M.; García Fernández, I.; Martínez Durá, R. J.
(2004). ArticleProceedings of the Industrial Simulation Conference 2004, 8893 Granular systems appear in many industrial and agricultural disciplines, which has lead to a wide range of models for their simulation, both in engineering and applied physics. However, most of the current models are addressed to make a deep analysis of the properties of the system, and they are not suitable for their use in realtime simulations. Thus, despite of the efforts in computational ﬂuid dynamics and molecular dynamics, the application of granular systems in the ﬁeld of virtual reality and computer graphics lacks of a set of fast interactive models that allow their execution in realtime. In this paper we propose an...
Proceedings of the Industrial Simulation Conference 2004, 8893 Granular systems appear in many industrial and agricultural disciplines, which has lead to a wide range of models for their simulation, both in engineering and applied physics. However, most of the current models are addressed to make a deep analysis of the properties of the system, and they are not suitable for their use in realtime simulations. Thus, despite of the efforts in computational ﬂuid dynamics and molecular dynamics, the application of granular systems in the ﬁeld of virtual reality and computer graphics lacks of a set of fast interactive models that allow their execution in realtime. In this paper we propose an efﬁcient granular system model based on cellular automata, designed to be used in computer graphics applications. The model is provided with inertia which gives it more realistic physical properties. Also, several details for its implementation are given together with an analysis of its computational complexity.
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Approximation of Continuous Media Models for Granular Systems Using Cellular Automata
Pla Castells, M.; García Fernández I.; Martínez Durá, R. J.
(2004). ArticleLecture Notes in Computer Science, 2004, 230237 In this paper a new cellular automata model suitable for granular systems simulation is presented. The proposed model is shown to be equivalent to a particularization of the well known BCRE model of granular systems and a correspondence between the parameters of the presented model and the BCRE model is also set, allowing to ﬁt these parameters for a given system. The model has the advantage over other cellular automata models of being more realistic in the behavior of the surface of heaps and slopes. The dynamics of the CA is analyzed in order to conﬁrm that it also has one of the most important features of these systems, 1/f noise.

Continuous force reaction in animation of avatars
Rodríguez Cerro, A.; García Fernández, I.; Martínez Durá, R. J.
(2004). ArticleSCA '10: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation 2010 Prerecorded animation is the best way to reproduce detailed and natural human movements. However, in interactive animations we need techniques to modify the avatar movement in a feasible way. This research combines prerecorded and physicsbased animation to simulate the movement of the body when it is perturbed by external forces. The main results of this work are the possibility of separately animating different parts of the body and to obtain the reaction to forces that last long intervals of time.

RotationInvariant Features based on Steerable Transforms with an application to distributed image classification
BeferullLozano, B.; Xie, H.; Ortega, A.
(2004). ArticleIn this paper, we propose a new rotationinvariant image retrieval system based on steerable pyramids and the concept of angular alignment across scales. First, we define energybased texture features which are steerable under rotation, i.e., such that features corresponding to the rotated version of an image can be easily obtained from the features of the original (nonrotated) image. We also propose an approach to measure similarity between images that is robust to rotation; images are compared after being aligned in angle. The retrieval process is performed by means of a decision tree classifier where the angular alignment is performed at each node in the tree. To demonstrate the...
In this paper, we propose a new rotationinvariant image retrieval system based on steerable pyramids and the concept of angular alignment across scales. First, we define energybased texture features which are steerable under rotation, i.e., such that features corresponding to the rotated version of an image can be easily obtained from the features of the original (nonrotated) image. We also propose an approach to measure similarity between images that is robust to rotation; images are compared after being aligned in angle. The retrieval process is performed by means of a decision tree classifier where the angular alignment is performed at each node in the tree. To demonstrate the effectiveness of our system we consider a distributed image classification system, where the feature encoder and the classifier are physically apart and thus features are compressed before being transmitted. Our results of retrieval performance versus rate show a clear gain with respect to a wavelet transform (as an example, for the same rate, the retrieval precision is increased from 40% to 65%).
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