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In many practical situations, we have several estimates x1, ..., xn of the same quantity x, i.e., estimates for which x1 ≈ x, x2 ≈ x, ..., and xn ≈ x. It is desirable to combine (fuse) these estimates into a single estimate for x. From the fuzzy viewpoint, a natural way to combine these estimates is: (1) to describe, for each x and for each i, the degree μ≈(xi-x) to which x is close to xi, (2) to...
The seafloor high-frequency backscatter average statistics, including the backscatter strength and the backscatter cross section, are statistically analyzed. According to Gamma distribution model of the backscatter cross section, the probability density function (PDF) of the backscatter strength is derived, and it is proved that the backscatter strength approaches a Gaussian distribution. The data...
In this paper we present several information-theoretic similiarity measures for shape retrieval in combination with non-rigid registration processes. The challenging property of these measures is that they are bypass divergences, that is, do not require the estimation of the probability density function for each shape. After presenting the dissimilarities and proposing some new ones, we analyze their...
We present a general methodology that aims to learn multi-variate statistics of high dimensional images, in order to capture the inter-individual variability of imaging data from a limited number of training images. The statistical learning procedure is used for identifying abnormalities as deviations from the normal variation. In most practical applications, learning an accurate statistical model...
In this article, a novel method to accurately estimate 3D surface of objects of interest is proposed. Each ray projected from 2D image plane to 3D space is modelled with the Gaussian kernel function. Then a mean shift algorithm with an annealing scheme is used to find maximums of the probability density function and recovers the 3D surface. Experimental results show that our method is more accurate...
In recent years, uncertain data have gained increasing research interests due to its natural presence in many applications such as location based services and sensor services. In this paper, we study the problem of clustering uncertain objects. We propose a new deviation function that approximates the underlying uncertain model of objects and a new density-based clustering algorithm, U-DBSCAN, that...
Several data management applications rely on data clustering methods which are usually designed to handle a static object as a single point in space. In recent years, clustering static objects seems to reach a stable point. Clustering uncertain objects is more challenging than clustering static objects and currently, it is actively studied in data mining clustering researches. In this paper, we study...
Integrated circuit process technology is entering the ultra deep submicron era. At this level, interconnect structure becomes very stiff and the metal resistance shielding effects problem is more serious. Although several delay metrics have been proposed, they are inefficient and difficult to implement. Hence, we propose a new delay and slew metric for interconnect based on Beta distribution and which...
In this paper, an online estimation method of multijoint human arm viscoelasticity for the case of unknown variance is considered. For the unknown variance process of a human multijoint arm, an estimation method of the human multijoint arm viscoelasticity is proposed. The proposed method includes two estimators, the first estimator is to calculate the variance, and the human multijoint arm viscoelasticity...
The mineral separation efficiency of flotation process depends very much on the surface properties of feed ore and addition of chemical reagents. Machine vision based analysis of froth appearance is considered as an indication of flotation performance. Bubble structure obtained by watershed segmentation scheme is used to determine the amount of reagent. To explore bubble size distribution, nonparametric...
We present a motion descriptor for human action recognition where appearance and shape information are unreliable. Unlike other motion-based approaches, we leverage image characteristics specific to human movement to achieve better robustness and lower computational cost. Drawing on recent work on motion recognition with ballistic dynamics, an action is modeled as a series of short correlated linear...
Local convergence is a limitation of many optimization approaches for multimodal functions. For hybrid model learning, this can mean a compromise in accuracy. We develop an approach for learning the model parameters of hybrid discrete-continuous systems that avoids getting stuck in locally optimal solutions. We present an algorithm that implements this approach that 1) iteratively learns the locations...
In this study, we discuss recent advances in the theory and practice of exemplar-based clustering. In the context of clustering, exemplars are those representative objects in the data sets. A recently proposed approach called convex clustering with exemplar-based models, referred as (CCE), adopts a convex objective function with a global solution. Although the existing frame work of CCE is attractive,...
The main contribution of this paper is a procedure for the control by energy shaping via Casimir generation of high-order port-Hamiltonian systems obtained from the spatial discretization of infinite dimensional dynamics. Beside the intrinsic difficulties related to the large number of state variables, the finite element model is generally given in terms of a Dirac structure and is completely a-causal,...
The stochastic distribution control (SDC) problem is a generalised form of the minimum variance control problem where non-Gaussian noise distributions are encountered. The problem has been previously solved using two alternative approaches. When it is assumed that the output probability distribution function (PDF) is measurable, then a parameterized controller is obtained. If on the other hand this...
Since its foundation in 1994, the grouping genetic algorithm (GGA) is the only evolutionary algorithm heavily modified to suit the structure of grouping problems. In this paper we design the grouping version of evolution strategies (ES). It is well-known that ES maintains a Gaussian mutation, recombination and a selection operator for optimizing non-linear continuous functions. Therefore, the development...
As customer demands become more and more individualised, also the process of providing estimates and tendering offers is becoming increasingly complicated. In the future, apart from technological feasibility, order placement will to a great extent depend on the bidder's ability to respond to individual customer requests. Automated pre-calculation of production times and costs is thus becoming more...
In this paper, we focus on the problem of shape retrieval. A novel approach, called improved graph transduction, is proposed. As preceding graph transduction method, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph. Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to...
Composite fading takes place in several communication channels due to the random variations of the local average power of the received multipath-faded signal. The generalized-K (gamma-gamma) probability density function (PDF) has been proposed recently to model composite fading in wireless channels. However, further derivations using the generalized-K PDF have shown to be quite involved due to the...
In this paper, we discuss an integrated system for video rotoscoping extracting and tracking object sketch (structural shape) across video sequence. This system consists of two key components: object sketch computing and graph-based object tracking. Given a video clip, we first use a primal sketch algorithm to search bottom-up sketch proposals and additively pursue sketch strokes in each frame. User...
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