Search results for "Linear Algebra."
showing 10 items of 552 documents
Springs-based Simulation for Image Retargeting
2011
In this paper an efficient method for image retargeting is pro- posed. It relies onto a mechanical model based on springs network. Each pixel displacement (compression or expan- sion) is given by the network response, according to the springs stiffness. The properties of the springs are deter- mined as function of the visual relevance of the pixels. Such model does not require any optimization, since its so- lution is obtained simply from a linear system of equations, allowing real-time application even for large images. The approach is fully automatic, though can be improved by interactively providing cues such as geometric constraints and/or manual relevant object labeling. The results pr…
Real-Time Object Detection in Embedded Video Surveillance Systems
2008
In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.
An FPGA Implementation of a Quadruple-Based Multiplier for 4D Clifford Algebra
2008
Geometric or Clifford algebra is an interesting paradigm for geometric modeling in fields as computer graphics, machine vision and robotics. In these areas the research effort is actually aimed at finding an efficient implementation of geometric algebra. The best way to exploit the symbolic computing power of geometric algebra is to support its data types and operators directly in hardware. However the natural representation of the algebra elements as variable-length objects causes some problems in the case of a hardware implementation. This paper proposes a 4D Clifford algebra in which the variable-length elements are mapped into fixed-length elements (quadruples). This choice leads to a s…
Computational issues of an electromagnetics transient meshless method
2019
In this paper we refer to the computational issues in solving Maxwell’ s curl equations without using any connectivity among the points in which the problem domain is discretized. The adopted procedure is able to approximate the electric and magnetic vector fields making use of the derivatives of a kernel function at points arranged in the computational domain. In order to improve the numerical accuracy, dealing with irregular data distribution or data located near the boundary, a suitable strategy is considered. The computational core of the overall process requires elementary linear algebra operations. In the paper the method is presented and the discussion is revolved to the computationa…
Incoherent dispersive shocks in the spectral evolution of random waves
2013
We predict theoretically and numerically the existence of incoherent dispersive shock waves. They manifest themselves as an unstable singular behavior of the spectrum of incoherent waves that evolve in a noninstantaneous nonlinear environment. This phenomenon of "spectral wave breaking" develops in the weakly nonlinear regime of the random wave. We elaborate a general theoretical formulation of these incoherent objects on the basis of a weakly nonlinear statistical approach: a family of singular integro-differential kinetic equations is derived, which provides a detailed deterministic description of the incoherent dispersive shock wave phenomenon.
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval
2016
Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training data sets. With the increasing amount of optical remote sensing data made available for analysis and the possibility of using a large amount of simulated data from radiative transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques will need to be implemented. Active learning (AL) methods enable to select the most informative samples in a data set. This letter introduces six AL methods for achieving optimized biophysical variable estimat…
Nonlinear Distribution Regression for Remote Sensing Applications
2020
In many remote sensing applications, one wants to estimate variables or parameters of interest from observations. When the target variable is available at a resolution that matches the remote sensing observations, standard algorithms, such as neural networks, random forests, or the Gaussian processes, are readily available to relate the two. However, we often encounter situations where the target variable is only available at the group level, i.e., collectively associated with a number of remotely sensed observations. This problem setting is known in statistics and machine learning as multiple instance learning (MIL) or distribution regression (DR). This article introduces a nonlinear (kern…
Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering
2018
Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smoothness that graph signals exhibit with respect to the underlying graph. To address the challenge o…
Online Non-linear Topology Identification from Graph-connected Time Series
2021
Estimating the unknown causal dependencies among graph-connected time series plays an important role in many applications, such as sensor network analysis, signal processing over cyber-physical systems, and finance engineering. Inference of such causal dependencies, often know as topology identification, is not well studied for non-linear non-stationary systems, and most of the existing methods are batch-based which are not capable of handling streaming sensor signals. In this paper, we propose an online kernel-based algorithm for topology estimation of non-linear vector autoregressive time series by solving a sparse online optimization framework using the composite objective mirror descent…
Design of Asymmetric Shift Operators for Efficient Decentralized Subspace Projection
2021
A large number of applications in decentralized signal processing includes projecting a vector of noisy observations onto a subspace dictated by prior information about the field being monitored. Accomplishing such a task in a centralized fashion in networks is prone to a number of issues such as large power consumption, congestion at certain nodes and suffers from robustness issues against possible node failures. Decentralized subspace projection is an alternative method to address those issues. Recently, it has been shown that graph filters (GFs) can be implemented to perform decentralized subspace projection. However, most of the existing methods have focused on designing GFs for symmetr…