Pose estimation from multiple cameras based on Sylvester’s equation

In this paper, we introduce a method to estimate the object’s pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester’s equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester’s equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester’s equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.     

Two-stage bias correction estimators based on generalized partitioning estimation method†

The generalized partitioning estimation method, which is well known in tho literature (for example, Lainiotis 1976), is applied to solve the bias correction filtering, predicting and amoothing problems for a linear continuous.time system with undisturbable bias subsystem. With tho aid of an initial dependent-type partitioning approach, it is shown that the Friedland (1969) elemental results con be readily extended to the more general case where the bias and the original states are mutually dependent at the initial time, and that two-stage bias correction predictors and smoothers can also be developed. Finally, the dual set of Chandrasekhar algorithms, which alleviates the computation burden for a bias correction fixed-interval smoother evolving forwards in time, is presented.     

Spinning frequency estimation algorithm of MEMS gyro’s output signal based on FFT coefficient

Microsystem Technologies - To obtain accurate demodulation of the spinning frequency of MEMS carrier-driven gyro output signal, a frequency estimation algorithm combining the real and imaginary...     

Exchange rates determination based on genetic algorithms using Mendel’s principles: Investigation and estimation under uncertainty

A genetic algorithm using Mendel’s principle (Mendel-GA), in which the random assignment of alleles from parents to offsprings is implied by the Mendel genetic operator, is proposed for the exchange rates determination problem. Besides the traditional genetic operators of selection, crossover, and mutation, Mendel’s principles are included, in the form of an operator in the genetic algorithm’s evolution process. In the quantitative analysis of exchange rates determination, the Mendel-GA examines the exchange rate fluctuations at the short-run horizon. Specifically, the aim is to revisit the determination of high-frequency exchange rates and examine the differences between the method of genetic algorithms and that of the traditional estimation methods. A simulation with a given initial conditions has been devised in MATLAB, and it is shown that the Mendel-GA can work valuably as a tool for the exchange rates estimation modelling with high-frequency data.     

Fast calculation of α-stable density functions based on off-line precomputations. Application to ML parameter estimation

This paper proposes a fast algorithm to compute arbitrary α-stable PDFs and CDFs. The procedure is based on off-line precomputations of α-stable values on a grid of points in the α–β parameter space, as well as in a set of abscissa points. The grid is built by defining two quality measures and assuring that minimum values for these measures are reached on those points. Results indicate that the method here proposed is substantially faster than directly evaluating the standard expressions proposed by Nolan [1]. The proposed algorithm makes it possible to efficiently use estimation methods based on the evaluation of the PDF, such as the Maximum Likelihood (ML) principle, which gives asymptotically unbiased and efficient estimates. To this end, an ML-based estimation algorithm is provided as well.     

Hierarchical approaches for multicast based on Euclid’s algorithm

We introduce a hierarchical approach for secure multicast where rekeying of groups of users is made through a method based on Euclid’s algorithm for computing GCD. We consider tree arrangements of users that decrease requirements on bandwidth as protocols of the same nature, but also show that computational requirements are less than in other similar approaches. We also introduce a distributed protocol by groups with group managers that not only helps to decrease size of rekeying messages with respect to a centralized approach, but also to increase the security level concerning authentication of users and distributed information.     

Concurrent depth-first search algorithms based on Tarjan’s Algorithm

We present concurrent algorithms, based on depth-first search, for three problems relevant to model checking: given a state graph, to find its strongly connected components, which states are in loops, and which states are in “lassos”. Each algorithm is a variant of Tarjan’s Algorithm. Our algorithms typically exhibit a three- or four-fold speed-up over the corresponding sequential algorithms on an eight-core machine.     

Global sensitivity analysis based on Gini’s mean difference

Global sensitivity analysis has been widely used to detect the relative contributions of input variables to the uncertainty of model output, and then more resources can be assigned to the important input variables to reduce the uncertainty of model output more efficiently. In this paper, a new kind of global sensitivity index based on Gini’s mean difference is proposed. The proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions. Through the decomposition of Gini’s mean difference, it shows that the proposed sensitivity index can be represented by the energy distance, which measures the difference between probability distributions. Therefore, the proposed sensitivity index also takes the probability distribution of model output into consideration. In order to estimate the proposed sensitivity index efficiently, an efficient Monte Carlo simulation method is also proposed, which avoids the nested sampling procedure. The test examples show that the proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions.     

Remote sensing image fusion based on Bayesian linear estimation

A new remote sensing image fusion method based on statistical parameter estimation is proposed in this paper. More specially, Bayesian linear estimation (BLE) is applied to observation models between remote sensing images with different spa- tial and spectral resolutions. The proposed method only estimates the mean vector and covariance matrix of the high-resolution multispectral (MS) images, instead of assuming the joint distribution between the panchromatic (PAN) image and low-resolution multispectral image. Furthermore, the proposed method can enhance the spatial resolution of several principal components of MS images, while the traditional Principal Component Analysis (PCA) method is limited to enhance only the first principal component. Experimental results with real MS images and PAN image of Landsat ETM demonstrate that the proposed method performs better than traditional methods based on statistical parameter estimation, PCA-based method and wavelet-based method.     

Kernel Fuzzy Kohonen’s Clustering Neural Network and It’s Recursive Learning

The architecture of multilayer kernel clustering neuro-fuzzy system and algorithm of its self-learning are intended for the recovery of overlapped clusters in situations when the streams of observations are fed in the online mode is proposed. The designed system, based on the T. Kohonen’s self-organizing map, permits to recover linearly nonseparated data classes, processes information in an online mode, doesn’t suffer from the “curse of dimensionality” and is easy in implementation.     

Transformation of feature space based on Fisher’s linear discriminant

Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant.     

Fuzzy approach for intrusion detection based on user’s commands

The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user’s activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user’s profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users’ behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user’s profiles based on any countable features derived from user’s actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.     

3D-freehand-pose initialization based on operator’s cognitive behavioral models

Tracking, recognition and interaction based on 3D freehand are a part of our virtual assembly system, in which monocular camera is used to input online freehand videos and the hand pose tracker requires a reliable initial pose in the first frame. A novel approach to initializing 3D pose and position of freehand is put forward in this paper visualization of 3D hand model and modeling the operators’ cognitive behaviors. Our approach is composed of three phases: hand posture recognition, coarse-tuning and fine-tuning. The operator moves his/her hand onto the to meet the needs of our virtual assembly system. The main contribution of this paper is that the three core techniques are for the first time integrated together, including human–computer interaction (HCI) in the process of initializing, projection of the 3D hand model in the period of coarse-tuning time. Then, the computer repeatedly fine-tunes the 3D hand model until the projection of the 3D hand model is completely superimposed onto the operator’s hand image. We focus on exploring and modeling cognitive behavior of operator’s hand upon which we design our initialization algorithm. Our research shows that cognitive behavioral models are not only beneficial to reducing cognitive loads for operators, because it makes the computers cater for the changes of the operators’ hand poses, but also helpful to address high dimensionality of articulated 3D hand model. Our experimental results also show that the approach presented in this paper is easier, more pleasurable and satisfactory experience for the operators. Our initialization system has successfully been applied to our 3D freehand tracking system and a simulation virtual assembly system.     

Image betrayal checking based on organization’s watermarking in Internet

There is a growing need for confidential institutions to actively check the documents in Internet for detecting some leaked confidential images. This paper presents an efficient method of active image betrayal checking based on a new type of watermarking in addition to the user-specific fingerprint. The implemented organization’s watermarking, as a ‘fingerprint’ for a group of staffs, with a codeword length far shorter than that of the user-specific fingerprint, is extracted from a suspected image at the first stage of betrayal checking, for the purpose of reducing the number of suspected images by a great deal. Then, only the remaining suspected images are subjected to the time-consuming user-specific fingerprint checking. Experimental tests confirmed the improved efficiency of this method.     

A target advertisement system based on TV viewer’s profile reasoning

The traditional broadcasting services such as terrestrial, satellite and cable broadcasting have been unidirectional mass media regardless of TV viewer’s preferences. Recently rich media streaming has become possible via the broadband networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming service has been emerging by taking into account the user’s preference on content genres, viewing times and actors/actresses etc. Accordingly personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for TV viewers. The user profile reasoning is made in terms of genre preference and TV viewing times for TV viewer’s groups in different genders and ages. For user profiling reasoning, the TV viewing history data is used to train the proposed user profiling reasoning algorithm which allows for target advertisement for different age/gender groups. To show the effectiveness of our proposed user profile reasoning method, we present plenty of the experimental results by using real TV usage history.     

Term weighting for information retrieval based on term’s discrimination power

One of the most important research topics in Information Retrieval is term weighting for document ranking and retrieval, such as TFIDF, BM25, etc. We propose a term weighting method that utilizes past retrieval results consisting of the queries that contain a particular term, retrieval documents, and their relevance judgments. A term’s Discrimination Power(DP) is based on the difference degree of the term’s average weights obtained from between relevant and non-relevant retrieved document sets. The difference based DP performs better compared to ratio based DP introduced in the previous research. Our experimental result shows that a term weighting scheme based on the discrimination power method outperforms a TF*IDF based scheme.     

Classification of image distortion based on the generalized Benford’s law

Multimedia Tools and Applications - Distortion classification is an important step in blind image quality assessment. In this paper, a new image distortion classification algorithm is presented....     

Synthesis of stabilizing spacecraft control based on generalized Ackermann’s formula

Problem of modal synthesis of controllers and observers using the generalized Ackermann’s formula is solved for a spacecraft as a complex dynamic system with high interconnections. All possible controller matrices (the whole set of controllers) are obtained for solution of the problem of stabilization of orbital orientation of the spacecraft in inseparable channels of bank and yaw angles.