Recursive Estimation Process Research Articles

A model of plaid motion perception based on recursive Bayesian integration of the 1-D and 2-D motions of plaid features

We describe a theoretical and computational model of the perception of plaid pattern motion which fully accounts for the majority of cases in which misperception of the direction of motion of Type II plaids has been observed [Yo, C., & Wilson, H. (1992). Perceived direction of moving two-dimensional patterns depends on duration, contrast, and eccentricity. Vision Research 32, 135–147]. The model consists of two stages: in the first stage local motion detectors signal both the one-dimensional (1-D) and two-dimensional (2-D) motion of the high luminance features (blobs) in the plaid pattern; in the second stage these local motion signals are combined using a recursive Bayesian least squares estimation process. We demonstrate both theoretically and using simulations of the computational model that the estimated direction of the plaid motion for Type II plaids is initially dominated by the 1-D motion of the longer edges of the elongated blobs, which is in a direction close to the vector sum direction of the component gratings. The recursive estimation process which combines the local motion signals in the second stage of the model results in a dynamic shift in the estimated plaid direction towards the direction of the 2-D motion of the blobs, which corresponds to the veridical plaid direction.

시간가변적 공적분관계의 안정화

변수들 사이 공적분관계의 존재는 불안정한 시계열의 선형결합이 안정적임을 뜻하지만 이 결합이 표본기간 내 발생한 사건에 영향을 받는다면 공적분검정의 기본 가정에 위배되어 검정력은 약해지고 결과는 현실과 차이가 난다. 이러한 관점에 입각하여 본 연구에서는 전체 표본의 일부를 단계적으로 증가시켜 가며 국내 주식시장 장기균형체계를 추정하여 시간가변성 및 안정성을 검정하였으며, 구조적 변화의 발생이 확인된 구간에 대해서는 가변수를 추가하는 방식으로 전 구간에 걸쳐 공적분 벡터를 안정화시키는 방안을 모색하여 보았다. If a cointegrating relation is affected by important economic and political events occurred in the sample period, the assumption of the time-invariant cointegrating vector is violated, which leads to the misrep-resentation of the actual relations between the variables. From such a viewpoint, this study utilizes the recursive estimation process in testing for the stability of the long-run equilibrium of the domestic stock market system and then attempts to develop the framework for stabilizing time-variant cointegraing relations by introducing the dummy variables where the structural changes are found to exist.

PARTICLE PETRI NET-BASED ESTIMATION IN HYBRID SYSTEMS TO DETECT INCONSISTENCIES

Monitoring a complex system consists in analysing its behaviour and reacting to abnormal situations. In this context, we have proposed a model for hybrid systems: particle Petri nets. This model allows to represent the discrete dynamics of the system through the Petri net structure and the continuous dynamics by evolution equations associated to some places. From this model, a recursive estimation process has been defined and allows corrected numerical tokens and corrected symbolic tokens to be compared. The comparison highlights inconsistent states, i.e. the states that are unreachable from the initial state of the Petri net. Inconsistencies may reveal an abnormal situation. An application to the tracking of an airliner pilot's activity is finally presented.

A framework for heading-guided recognition of human activity

A combined 2D, 3D approach is presented that allows for robust tracking of moving people and recognition of actions. It is assumed that the system observes multiple moving objects via a single, uncalibrated video camera. Low-level features are often insufficient for detection, segmentation, and tracking of non-rigid moving objects. Therefore, an improved mechanism is proposed that integrates low-level (image processing), mid-level (recursive 3D trajectory estimation), and high-level (action recognition) processes. A novel extended Kalman filter formulation is used in estimating the relative 3D motion trajectories up to a scale factor. The recursive estimation process provides a prediction and error measure that is exploited in higher-level stages of action recognition. Conversely, higher-level mechanisms provide feedback that allows the system to reliably segment and maintain the tracking of moving objects before, during, and after occlusion. Heading-guided recognition (HGR) is proposed as an efficient method for adaptive classification of activity. The HGR approach is demonstrated using “motion history images” that are then recognized via a mixture-of-Gaussians classifier. The system is tested in recognizing various dynamic human outdoor activities: running, walking, roller blading, and cycling. In addition, experiments with real and synthetic data sets are used to evaluate stability of the trajectory estimator with respect to noise.

Passive localization algorithms

An acoustic source can be localized by exploiting the fact that, if the range is not too great, the wavefront of the incoming acoustic energy can be assumed to be circular. This is sometimes called ‘‘Wavefront Curvature Ranging.’’ Here, the estimation of range reduces to the estimation of the radius of a circle. In fact, if sufficient horizontal and vertical aperture is available, range, bearing and elevation can be estimated, given a minimum of four receivers. The technique is based on the measurement of the relative phases between pairs of receivers. In this paper, two methods of improving the performance of such localizers are investigated. In the case where the receivers are mounted on a moving platform, it is shown that exploitation of this motion can improve performance by utilizing information that is usually ignored in phase measurements. Also, since the motion introduces a time dependence in the true values of the time delays, a limit on the accuracy of their measurement is incurred. A recursive estimation process is proposed as a remedy for this problem. These techniques will be demonstrated by using simulated data.