The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Location Model is a classification model that capable to deal with mixtures of binary and continuous variables simultaneously. The binary variables create segmentation in the groups called cells whilst the continuous variables measure the differences between groups based on information inside the cells. It is important to note that location model is biased and even impossible to be constructed when...
The present paper describes a low-cost algorithm for video stabilization. Like other feature based algorithms, it is robust to motion blur, noise and illumination changes. Moreover, maintaining real time processing, it is not negatively affected by moving objects in the scene, works fine even in conditions of low details in the background and it is robust to scene changes.
This paper proposes a new wearable eye-gaze tracking system with a single webcam mounted on the glasses. First, the region of interest (ROI) of eye is extracted by skin detection and eyelid removing. Then Hough circle detection is used to search the candidate of circles in the eye's ROI and applied to determine the status of opening or closing of eye. Based on the circle detection, the eye center...
This paper presents two different approaches to derive the asymptotic distributions of the robust Adaptive Normalized Matched Filter (ANMF) under both H0 and H1 hypotheses. More precisely, the ANMF has originally been derived under the assumption of partially homogenous Gaussian noise, i.e. where the variance is different between the observation under test and the set of secondary data. We propose...
A generalised Lasso iteratively reweighted scheme is here introduced to perform spatially regularised Hurst estimation on semi-local, weakly self-similar processes. This is extended further to the robust, heavy-tailed case whereupon the generalised M-Lasso is proposed. The design successfully incorporates both a spatial derivative in the generalised Lasso regulariser operator and a weight matrix formulated...
Existing methods for smart data reduction are typically sensitive to outlier data that do not follow postulated data models. We propose robust censoring as a joint approach unifying the concepts of robust learning and data censoring. We focus on linear inverse problems and formulate robust censoring through a sparse sensing operator, which is a non-convex bilinear problem. We propose two solvers,...
In this contribution, we implement a fully distributed diffusion field estimation algorithm based on the use of average consensus schemes. We show that the field reconstruction problem is equivalent to estimating the sources of the field, and then derive an exact inversion formula for jointly recovering these sources when they are localized and instantaneous. Next we adapt this formula to the sensor...
In the context of robust covariance matrix estimation, this work generalizes the shrinkage covariance matrix estimator introduced in [1, 2]. The shrinkage method is a way to improve and to regularize the Tyler's estimator [3, 4]. This paper proves that the shrinkage estimator does not require any trace constraint to be well-defined, as it has been previously developed in [1]. The existence and the...
In this work we consider the estimation of spatio-temporal covariance matrices in the low sample non-Gaussian regime. We impose covariance structure in the form of a sum of Kronecker products decomposition [1, 2] with diagonal correction [2], which we refer to as DC-KronPCA, in the estimation of multiframe covariance matrices. This paper extends the approaches of [1] in two directions. First, we modify...
When there are more than necessary distance measurements in localization by distance measurements with closed form estimators, forming smaller subgroups of measurements and averaging the location estimates obtained with these subgroups of measurements makes it possible to eliminate outlier measurements if they are present. In order to eliminate these outlier results, the nearest estimate to the geometric...
In this paper, we consider the linear estimation problem under structured data uncertainties. A robust algorithm is presented under bounded uncertainties under the mean square error (MSE) criterion. The performance of the linear estimator is defined relative to the performance of the linear minimum MSE (MMSE) estimator tuned to the underlying unknown data uncertainties, i.e., the introduced algorithm...
We explore the problem of anomaly detection based on several one-dimensional projections. The main advantage of the proposed approach is that it does not require any covariance matrix estimation, allowing to compute spatial adaptive anomaly detection in small neighborhoods. Although this is contrary to common sense, theoretical results support the consistence of our approach when a large number of...
In this paper we propose a method of detecting and recognizing the elements of a Sudoku Puzzle and providing a digital copy of the solution for it using MATLAB. The method involves a vision-based sudoku solver. The solver is capable of solving a sudoku directly from an image captured from any digital camera. After applying appropriate pre-processing to the acquired image we use efficient area calculation...
In this paper we analyze the problem of object identification in channels with desynchronization. In our analysis we assume that the identification system is designed using a pilot-based re-synchronization mechanism that assists desynchro-nization compensation with a certain accuracy. We demonstrate how the accuracy of re-synchronization impacts the information-theoretic limits of identification system...
We present a 3D feature descriptor that represents local topologies within a set of folded concentric rings by distances from local points to a projection plane. This feature, called as Concentric Ring Signature (CORS), possesses similar computational advantages to point signatures yet provides more accurate matches. It produces more compact and discriminative descriptors than shape context. It robust...
Background subtraction is a basic task for many computer vision applications, yet in dynamic scenes it is still a challenging problem. In this paper, we propose a new method to deal with this difficulty. Our approach is based on robust linear regression model and casts background subtraction as a outlier signal estimation problem. In our linear regression model, we explicitly model the error term...
Multiple people tracking from multiple cameras can suffer from various problems, particularly from inter-person occlusions. This paper attempts to solve the problems by analyzing the view visibility and ranking the reliability of the cues from 2D views. It combines the visibility with the smoothness constraints into a probability framework, which offers a more flexible and robust estimation. Moreover,...
The paper proposes a robust estimation method which implements, in cascade, two algorithms: (i) a Random Sample and Consensus (RANSAC) algorithm and (ii) a novel nonlinear prediction error estimator minimizing a cost function inspired by the mathematical definition of Gibbs entropy. The minimization of the nonlinear cost function allows to refine the Consensus Set found with standard RANSAC in order...
This paper presents a novel real-time approach for robust high precision and high quality depth estimation. It extends recent work on real-time Patch-Sweeping by combining the advantages of a robust hybrid stereo-based disparity estimator with the high accuracy of the Patch-Sweeping approach. It overcomes limitations of the existing Patch-Sweep approach, such as limited search range. Further, it implicitly...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.