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RotationInvariant Texture Retrieval with Gaussianized Steerable Pyramids
Tzagkarakis, G.; BeferullLozano, B.; Tsakalides, P.
(2006). 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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Directionlets: Anisotropic Multidirectional Representation with separable filtering
Velisavijevic, V.; BeferullLozano, B.; Vetterli, M.; Dragotti, P. L.
(2006). 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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Lattice Sensor Networks: Capacity Limits and Optimal Routing
Barrenetxea, G.; BeferullLozano, B.; Vetterli, M.
(2006). ArticleLattice networks are widely used in regular settings like grid computing, distributed control, satellite constellations, and sensor networks. Thus, limits on capacity, optimal routing policies, and performance with finite buffers are key issues and are addressed in this paper. In particular, we study the routing algorithms that achieve the maximum rate per node for infinite and finite buffers in the nodes and different communication models, namely uniform communications, central data gathering and border data gathering. In the case of nodes with infinite buffers, we determine the capacity of the network and we characterize the set of optimal routing algorithms that achieve capacity. In the...
Lattice networks are widely used in regular settings like grid computing, distributed control, satellite constellations, and sensor networks. Thus, limits on capacity, optimal routing policies, and performance with finite buffers are key issues and are addressed in this paper. In particular, we study the routing algorithms that achieve the maximum rate per node for infinite and finite buffers in the nodes and different communication models, namely uniform communications, central data gathering and border data gathering. In the case of nodes with infinite buffers, we determine the capacity of the network and we characterize the set of optimal routing algorithms that achieve capacity. In the case of nodes with finite buffers, we approximate the queue network problem and obtain the distribution on the queue size at the nodes. This distribution allows us to study the effect of routing on the queue distribution and derive the algorithms that achieve the maximum rate.
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PowerEfficient Sensor Placement and Transmission Structure for Data Gathering under Distortion Constraints
Ganesan, D.; Cristescu, R.; BeferullLozano, B.
(2006). ArticleWe consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use joint entropy coding based on explicit communication between sensor nodes, and consider both maximum and average distortion bounds. The optimization is complex since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem...
We consider the joint optimization of sensor placement and transmission structure for data gathering, where a given number of nodes need to be placed in a field such that the sensed data can be reconstructed at a sink within specified distortion bounds while minimizing the energy consumed for communication. We assume that the nodes use joint entropy coding based on explicit communication between sensor nodes, and consider both maximum and average distortion bounds. The optimization is complex since it involves an interplay between the spaces of possible transmission structures given radio reachability limitations, and feasible placements satisfying distortion bounds. We address this problem by first looking at the simplified problem of optimal placement in the onedimensional case. An analytical solution is derived for the case when there is a simple aggregation scheme, and numerical results are provided for the cases when joint entropy encoding is used. We use the insight from our 1D analysis to extend our results to the 2D case, and show that our algorithm for twodimensional placement and transmission structure provides significant power benefit over a commonly used combination of uniformly random placement and shortest path trees.
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Oversampled A/D Conversion and ErrorRate Dependence of NonBandlimited Signals with Finite Rate of Innovation
Jovanovic, I.; BeferullLozano, B.
(2006). ArticleWe study the problem of A/D conversion and errorrate dependence of a class of nonbandlimited signals with finite rate of innovation. In particular, we analyze a continuous periodic stream of Diracs, characterized by a finite set of time positions and weights. Previous research has only considered sampling of this type of signals, ignoring the presence of quantization, necessary for any practical implementation. To this end, we first define the concept of consistent reconstruction and introduce corresponding oversampling in both time and frequency. High accuracy in a consistent reconstruction is achieved by enforcing the reconstructed signal to satisfy three sets of constraints, related to...
We study the problem of A/D conversion and errorrate dependence of a class of nonbandlimited signals with finite rate of innovation. In particular, we analyze a continuous periodic stream of Diracs, characterized by a finite set of time positions and weights. Previous research has only considered sampling of this type of signals, ignoring the presence of quantization, necessary for any practical implementation. To this end, we first define the concept of consistent reconstruction and introduce corresponding oversampling in both time and frequency. High accuracy in a consistent reconstruction is achieved by enforcing the reconstructed signal to satisfy three sets of constraints, related to lowpass filtering, quantization and the space of continuous periodic streams of Diracs. We provide two schemes to reconstruct the signal. For the first one, we prove that the estimation mean squared error of the time positions is O(1/Rt2Rf3), where Rt and Rf are the oversampling ratios in time and frequency, respectively. For the second scheme, it is experimentally observed that, at the cost of higher complexity, the estimation accuracy lowers to O(1/Rt2Rf5). Our experimental results show a clear advantage of consistent over nonconsistent reconstruction. Regarding the rate, we consider a threshold crossing based scheme where, as opposed to previous research, both oversampling in time and in frequency influence the coding rate. We compare the errorrate behavior resulting, on the one hand, from increasing the oversampling in time and/or frequency, and, on the other hand, from decreasing the quantization stepsize.
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Network Correlated Data Gathering with Explicit Communication: NPCompleteness and Algorithms
Cristescu, R.; BeferullLozano, B.; Vetterli, M.; Wattenhofer, R.
(2006). ArticleWe consider the problem of correlated data gathering by a network with a sink node and a treebased communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropybased coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove...
We consider the problem of correlated data gathering by a network with a sink node and a treebased communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropybased coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NPhard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
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IADATmicv2005. International Conference on Multimedia, Image Processing and Computer Vision: A 3D environment for driving simulation scenario design & evaluation
coma, Inmaculada; Fernández, Marcos; Vera, Lucía; Olanda, Ricardo
(2005). ArticleSimulation and virtual environments are used increasingly for training and behaviour evaluation purposes. Driving simulators are a specific case of these systems. They have been proof as effective tools for drivers training, risky driving styles evaluation or the evaluation of the effects of external factors or new technologies in driving task. The interest in behaviour evaluation and training of drivers makes it necessary to design experiments that provide measurements of driver’s actions. These experiments are usually dynamic scenarios, which can be defined as a set of dynamic elements (cars, lights, pedestrians, etc.) carrying out some tasks in a synchronized way. The purpose of these...
Simulation and virtual environments are used increasingly for training and behaviour evaluation purposes. Driving simulators are a specific case of these systems. They have been proof as effective tools for drivers training, risky driving styles evaluation or the evaluation of the effects of external factors or new technologies in driving task. The interest in behaviour evaluation and training of drivers makes it necessary to design experiments that provide measurements of driver’s actions. These experiments are usually dynamic scenarios, which can be defined as a set of dynamic elements (cars, lights, pedestrians, etc.) carrying out some tasks in a synchronized way. The purpose of these scenarios is to force the driver to make a decision.
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IADAT Journal of Advanced Technology: Dynamic driving scenario design
Coma,Inmaculada; Fernández, Vera, Lucía; Olanda, Ricardo
(2005). ArticleSimulation and virtual environments are used increasingly for training and behaviour evaluation purposes. Driving simulators are a specific case of these systems. They have been proof as effective tools for drivers training, risky driving styles evaluation or the evaluation of the effects of external factors or new technologies in driving task. The interest in behaviour evaluation and training of drivers makes it necessary to design experiments that provide measurements of driver’s actions. These experiments are usually dynamic scenarios, which can be defined as a set of dynamic elements (cars, lights, pedestrians, etc.) carrying out some tasks in a synchronized way. The purpose of these...
Simulation and virtual environments are used increasingly for training and behaviour evaluation purposes. Driving simulators are a specific case of these systems. They have been proof as effective tools for drivers training, risky driving styles evaluation or the evaluation of the effects of external factors or new technologies in driving task. The interest in behaviour evaluation and training of drivers makes it necessary to design experiments that provide measurements of driver’s actions. These experiments are usually dynamic scenarios, which can be defined as a set of dynamic elements (cars, lights, pedestrians, etc.) carrying out some tasks in a synchronized way. The purpose of these scenarios is to force the driver to make a decision.
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On the interaction of data representation and routing in sensor networks
Cristescu, R.; BeferullLozano, B.; Vetterli, M.; Ganesan, D.; Acimovic, J.
(2005). ArticleWe consider data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information, collected by the nodes, to the sink node. This problem requires a joint optimization of the data representation at the nodes and of the transmission structure. First, we study the case when the measured data are correlated random variables, both in the lossless scenario with SlepianWolf coding, and in the highresolution lossy scenario with optimal ratedistortion allocation. We show that the optimal transmission structure is the shortest path tree, and we find, in closedform, the rate and distortion...
We consider data gathering by a network with a sink node and a tree communication structure, where the goal is to minimize the total transmission cost of transporting the information, collected by the nodes, to the sink node. This problem requires a joint optimization of the data representation at the nodes and of the transmission structure. First, we study the case when the measured data are correlated random variables, both in the lossless scenario with SlepianWolf coding, and in the highresolution lossy scenario with optimal ratedistortion allocation. We show that the optimal transmission structure is the shortest path tree, and we find, in closedform, the rate and distortion allocation. Second, we study the case when the measured data are deterministic piecewise constant signals, and data is described with adaptive level waveletbased multiresolution representation. We show experimentally that, when computation is decentralized, there is an optimal network division into node groups of adaptive size. Finally, we also analyze the node positioning problem where, given a correlation structure and an available number of sensors, the goal is to place the nodes optimally in terms of minimizing the transmission cost; our results show that important gains can be obtained compared to a uniformly distributed sensor positioning.
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Efficient Distributed Multiresolution processing for data gathering in sensor networks
Acimovic, J.; Cristescu, R.; beferullLozano, B.
(2005). ArticleWe consider large sensor networks where the cost of collecting data from the network nodes to the data gathering sink is critical. We propose several algorithms that use limited local communication and distributed signal processing to make communication more efficient in terms of transmission cost. We consider a model that uses distributed waveletbased signal processing. We first propose an algorithm that performs processing at nodes as data is forwarded to the sink. Then, we analyze algorithms that perform network division into groups of adaptive size and for which signal processing is applied separately to each group. We show by numerical simulations that such multiresolution approaches...
We consider large sensor networks where the cost of collecting data from the network nodes to the data gathering sink is critical. We propose several algorithms that use limited local communication and distributed signal processing to make communication more efficient in terms of transmission cost. We consider a model that uses distributed waveletbased signal processing. We first propose an algorithm that performs processing at nodes as data is forwarded to the sink. Then, we analyze algorithms that perform network division into groups of adaptive size and for which signal processing is applied separately to each group. We show by numerical simulations that such multiresolution approaches result in significant improvements for data gathering in terms of total communication costs.
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