Adaptive robust estimation of affine parameters from block motion vectors

In this paper, we propose an affine parameter estimation algorithm from block motion vectors for extracting accurate motion information with the assumption that the undergoing motion can be characterized by an affine model. The motion may be caused either by a moving camera or a moving object. The proposed method first extracts motion vectors from a sequence of images by using size-variable block matching and then processes them by adaptive robust estimation to estimate affine parameters. Typically, a robust estimation filters out outliers (velocity vectors that do not fit into the model) by fitting velocity vectors to a predefined model. To filter out potential outliers, our adaptive robust estimation defines a continuous weight function based on a Sigmoid function. During the estimation process, we tune the Sigmoid function gradually to its hard-limit as the errors between the model and input data are decreased, so that we can effectively separate non-outliers from outliers with the help of the finally tuned hard-limit form of the weight function. Experimental results show that the suggested approach is very effective in estimating affine parameters reliably.     

3D motion estimation of bubbles of gas in fluid glass,using an optical flow gradient technique extended to a third dimension

To solve the problem of estimating velocities of gas bubbles in melted glass, a method based on optical flow constraint (OFC) has been extended to the 3D case. A single camera, whose distance to the fluid is variable in time, is used to capture a sequence of frames at different depths. Since objects are not static, we cannot obtain two frames of different height values at the same time, and to our knowledge, this prevents the use of common 3D motion estimation techni ques. Since the information will be rather sparse, our estimation takes several measures around a given pixel and discards the erroneous ones, using a robust estimator. Along with the exposition of the practical application, the estimation proposed here is first contrasted in the 2D case to common benchmarks and then evaluated for a synthetic problem where velocities are known. Received: 9 July 2001 / Accepted: 5 August 2002 Published online: 3 June 2003 This work has been supported by Saint Gobain Cristaleria S.A., under contract FUO-EM-034-01 with Oviedo University, Spain.     

On the observability of linear motion quantities in navigation systems

Navigation systems are a key element in a large variety of mobile platforms, where the correct knowledge of their position and attitude is essential in most applications. This paper focuses on the observability of linear motion quantities (position, linear velocity, linear acceleration, and accelerometer bias). It presents necessary and sufficient conditions, with a clear physical insight, for the observability of these variables in 3-D. The analysis provided is based on kinematic models, which are exact and intrinsic to the motion of a rigid-body, and different cases are presented depending on the assumptions made on the sensor suite that is available on-board.     

Solving camera parameters from the perspective projection of a parameterized curve

Given the two-dimensional perspective projection of a known conic or polygon of unknown size and position in three-dimensional space, we show how to determine the camera look angles relative to the plane where the curve lies. Separate cases are discussed for conic and polygons. We then show that this technique can be used to solve for any planar or nonplanar curves, as long as they can be represented in parametric forms.     

Unified optical flow field approach to motion analysis from a sequence of stereo images

In this paper a new approach to motion analysis from stereo image sequences using unified temporal and spatial optical flow field (UOFF) is reported. That is, based on a four-frame rectangular model and the associated six UOFF field quantities, a set of equations is derived from which both position and velocity can be determined. It does not require feature extraction and correspondence establishment, which are known to be difficult, and only partial solutions suitable for simplistic situations have been developed. Furthermore, it is capable of detecting multiple moving objects even when partial occlusion occurs, and is potentially suitable for nonrigid motion analysis. Unlike the current existing techniques for motion analysis from stereo imagery, the recovered motion by using this new approach is for a whole continuous field instead of only for some features. It is a purely optical flow approach. Two experiments are presented to demonstrate the feasibility of the approach.     

公路汽车视觉导航中运动目标的检测与跟踪方法的研究

通过对室外行驶车辆上所采集的长序列图象的处理与分析,研究公路汽车计算机辅助礼堂导航中运动目标的检测与跟踪问题。算法的核心是根据图象中的水平、垂直边缘及目标尾部的局部对称性检测和图象中目标的存在,进而建立描述目标与摄象机之间的相对运动的状态模型和表示目标循征的透视投影模型,应用扩展的卡尔曼滤波器进行3维运动参数估计。     

Best performing parameters of linear and non-linear seismic base-isolator systems obtained by the power flow analysis

In this paper the problem of finding the best performing base isolator parameters of a seismic excited structure is treated. The structural and earthquake parameters and the isolator type are considered assigned. The approach here proposed is based on the minimization of the power flow transmitted by the isolator to the structure. The power flow is a statistical measure of the power transmitted to the structure during an earthquake. The earthquake is idealized as a filtered stochastic process. This approach considers the structure–isolator–excitation system as a super-structure in which the structure, the isolator and the excitation filter are the sub-structures. It guarantees a very good global behaviour of the structure, both in terms of relative displacements and in terms of accelerations. The isolator types considered in this work are the linear, the yielding and the friction ones.     

Designing and testing a calibrating procedure for combining the coordination systems of a handling robot and a stationed video camera

The paper discusses some of the results of designing and testing a calibrating procedure with important implications in contemporary robotics. This procedure is also mentioned in the literature as the “procedure for calibrating by matching the coordination systems of a robot and a stationed video camera”. The procedure is tested by a training robotechnic system, which consists of an anthropomorphic robot with five stages of freedom and a stationed video camera that can identify solid stationary objects. The results of this experimental testing are used to create and adopt some new methods for laboratory exams in robotics for students of FA in a Technical University and for developing further research on calibrating procedures in robotics needed for the industry.     

Decomposition and composition of the properties of alternatives in multicriteria decision-making problems

It is shown that under the vector approach, the problem of making decisions by decomposing the properties of alternatives can be represented by a hierarchical system of criteria. At the lower hierarchy level, an alternative is evaluated for separate properties on the basis of a criterion vector and, at the upper level, a composition is used to evaluate the alternative as a whole. The problem is solved by the method of nested scalar convolutions of vector-valued criteria. Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 117–122, January–February 2009.     

Finding the focus of expansion and estimating range using optical flow images and a matched filter

The focus of expansion plays an important role in many vision applications such as three-dimensional reconstruction, range estimation, time-to-impact computation, and obstacle avoidance. Most current techniques are based on correspondence or on accurate flow estimation and are therefore considered computationally heavy. This paper presents an efficient technique to find the focus of expansion from optical flow. The technique utilizes a specially designed matched filter that does not require an exact estimation of the optical flow but rather can use a low-quality estimation of it. In addition, based on the location of the focus of expansion and its immediate neighborhood, the paper suggests a way to estimate the range to the focus of expansion. Based on the experimental results, the technique has proved to be both accurate and efficient.Received: 26 January 2003, Accepted: 18 March 2004, Published online: 14 September 2004 Correspondence to: Didi Sazbon     

Sequential learning employing unfamiliar teacher hypothesis (SLEUTH) with concurrent estimation of both the parameters and teacher characteristics

Parametric learning in an unfamiliar environment, i.e., under unknown degree of supervision, forms the topic of this study. In addition to the learning of the distribution parameters, the learning scheme presented here has the novel capability of learning about the unfamiliar teacher, i.e., estimating the teacher characteristics inherent in the environment. This learning, under an unfamiliar teacher hypothesis, is seen to be computationally feasible and the results of simulation reveal the efficacy of the scheme in improving the learning under the unfamiliar teacher through learning about the teacher.