12695513
A new calibration model of camera lens distortion
Lens distortion is one of the main factors affecting camera calibration. In this paper, a new model of camera lens distortion is presented, according to which lens distortion is governed by the coefficients of radial distortion and a transform from ideal image plane to real sensor array plane. The transform is determined by two angular parameters describing the pose of the real sensor array plane with respect to the ideal image plane and two linear parameters locating the real sensor array with respect to the optical axis. Experiments show that the new model has about the same correcting effect upon lens distortion as the conventional model including all the radial distortion, decentering distortion and prism distortion. Compared with the conventional model, the new model has fewer parameters to be calibrated and more explicit physical meaning.
47115701
An Adaptive Selection of Motion for Online Hand-Eye Calibration
As the robot makes unplanned movement, online hand-eye calibration determines the relative pose between the robot gripper/end-effector and the sensors mounted on it. With noisy measurements, hand-eye calibration is sensitive to small rotations in real applications. Moreover, degenerate cases such as pure translations have no effect in hand-eye calibration. This paper proposes an adaptive motion selection algorithm for online hand-eye calibration, which can adaptively set the thresholds of motion selection according to the characteristics of the unplanned motion sequence. It is achieved by using polynomial-regression to predict the relationship between RMS of calibration error and thresholds. Thus, this procedure leads to an adaptive method of motion selection. It can adapt itself to online hand-eye calibration in various applications. Experiments using simulated data are conducted and present good results. Experiments using real scenes also show that the method is promising.
35849251
Motion Segmentation by Multibody Trifocal Tensor Using Line Correspondence
We present a geometric approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody trilinear constrain and its associated multibody trifocal tensor. We show how to obtain the individual trifocal tensor from the second order derivatives of the multibody trilinear constraint. Given trifocal tensors, we obtain the clustering of the motions and correspondences. Experimental results on synthetic and real dynamic scenes are presented
25014721
Motion segmentation of multiple translating objects from line correspondences
This paper presents an analytic approach to motion segmentation of multiple translating objects from line correspondences in three perspective views. The basic idea of our algorithm is to view the estimation of multiple translational motions as the estimation of a single, though more complex, multibody motion model that is then factored into the original models by polynomial differentiation. Experimental results on synthetic and real scenes are presented.
15124700
Motion segmentation of multiple translating objects using line correspondences
We present an algebraic approach to multibody motion segmentation from line correspondences. Given three perspective views containing multiple linearly moving objects, we demonstrate that after applying a polynomial embedding to the line correspondences, they became related by the so-called multibody line constraint of translational motions. We show how to linearly estimate the multibody trifocal epipole from line-line-line correspondences. The individual trifocal epipoles are then obtained from the derivatives of the multibody line constraint (up to an unknown factor). Given normalized trifocal epipoles, we can use any special clustering technique to obtain the clustering of the motions and the correspondences. The limitations of the proposed algorithm are also discussed. Experimental results on synthetic and real dynamic scenes are presented.
15086637
A New Calibration Model and Method of Camera Lens Distortion
Lens distortion is one of the main factors affecting camera calibration. In this paper, a new model of camera lens distortion is presented, according to which lens distortion is governed by the coefficients of radial distortion and a transform from ideal image plane to real sensing array plane. The transform is determined by two angular parameters describing the pose and two linear parameters locating the position of the sensing array plane. Compared with the conventional ones, the new model has fewer parameters to be calibrated and more explicit physical meaning. Calibration method of the new model is also proposed. Experiments show that calibration results from the new model and method can correct lens distortion better than conventional ones
16216207
A Novel Video Searching Model Based on Ontology Inference and Multimodal Information Fusion
Video comprises multiple types of textual, audio and visual information, and each of them contains abundant semantic information. Therefore multimodal features query and fusion are necessary in video retrieval. In this paper, we propose a new video retrieval model, which adopts multi-model including text, image, semantic concept and camera motion to query video. Then relation algebra expression is advanced to fuse multimodal information instead of traditional linear fusion method. In semantic concept detection model, Bayesian network based ontology is proposed to extract concepts. The experiments on TRECVID 2005 corpus have demonstrated a superior performance compared with exiting key approaches of video retrieval by multimodal information fusion.
8194176
AMOEBA-RT: Run-Time Verification of Adaptive Software
Increasingly, software must dynamically adapt its behavior in response to changes in the supporting computing, communication infrastructure, and in the surrounding physical environment. Assurance that the adaptive software correctly satisfies its requirements is crucial if the software is to be used in high assurance systems, such as command and control or critical infrastructure protection systems. Adaptive software development for these systems must be grounded upon formalism and rigorous software engineering methodology to gain assurance. In this paper, we briefly describe AMOebA-RT , a run-time monitoring and verification technique that provides assurance that dynamically adaptive software satisfies its requirements.
7784033
Increase of potential intellectual bandwidth in a scientific community through implementation of an end-user information system
Qureshi, et al. (2002) presented a case study where they used a framework, the intellectual bandwidth model to measure an organization's ability to create value. The model consists of two dimensions, information assimilation and collaboration. The IB model is a useful tool for managers because it enables them to plan solutions to complex business problems and to easily communicate these plans to key individuals. The model can also serve as a guide when making investment decisions. While it is important to increase an organization's intellectual bandwidth, it is equally important to assure that the resulting increase is employed to improve the organization's ability to innovate since, as it has been shown, innovation is the number one creator of organizational wealth (Baum et al., 2000). In this paper we discuss how intellectual bandwidth as proposed by Nunamaker et al. (2001) can be extended beyond the highest level of information assimilation identified in the model, automated sense-making, to ensure implementation of product innovations. To do this we propose adding two new information assimilation features, automated utilization and automated implementation. A real business example from the bioscience industry is presented in order to demonstrate how the IB model with extensions can be used. The study discusses an end-user information system, its current use and its potential to enhance an organization's value-creating capability.
9440354
Sequential Proximity-Based Clustering for Telecommunication Network Alarm Correlation
Alarm correlation for fault management in large telecommunication networks demands scalable and reliable algorithms. In this paper, we propose a clustering based alarm correlation approach using sequential proximity between alarms. We define two novel distance metrics appropriate for measuring similarity between alarm sequences obtained from interval-based division: 1) the two-digit binary metric that values the occurrences of two alarms in neighboring intervals to tolerate the false separation of alarms due to interval-based alarm sequence division, and 2) the sequential ordering-based distance metric that considers the time of arrival for different alarms within the same interval. We validate both metrics by applying them with hierarchical clustering using real-world cellular network alarm data. The efficacy of the proposed sequential proximity based alarm clustering is demonstrated through a comparative study with existing similarity metrics.
206703335
Minitrack: E-Government Information and Knowledge Management
The public sector realizes the strategic importance of methods, tools and concepts applied to the efficient and effective Information and Knowledge resource Management (IKM). Because governments and public administrations deal with vast amounts of information and knowledge, it is particularly important to leverage research agendas that advance tenets related to the management of these resources. For example, much of the work of public authorities and governments is related to the collection, processing, interpretation, and use of data, information and knowledge on citizens, businesses, governments themselves, the society, the market, the environment, laws, politics, etc.