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.

title: Motion segmentation of multiple translating objects from line correspondences
abstract: 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.

title: Motion segmentation of multiple translating objects using line correspondences
abstract: 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.

title: Adaptive motion selection for online hand-eye calibration
abstract: While a robot moves, online hand–eye calibration to determine the relative pose between the robot gripper/end-effector and the sensors mounted on it is very important in a vision-guided robot system. During online hand–eye calibration, it is impossible to perform motion planning to avoid degenerate motions and small rotations, which may lead to unreliable calibration results. This paper proposes an adaptive motion selection algorithm for online hand–eye calibration, featured by dynamic threshold determination for motion selection and getting reliable hand–eye calibration results. Simulation and real experiments demonstrate the effectiveness of our method.

title: A New Calibration Model and Method of Camera Lens Distortion
abstract: 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

title: 3D Motion Segmentation from Straight-Line Optical Flow
abstract: We present a closed form solution to the problem of segmenting multiple 3D motion models from straight-line optical flow. We introduce the multibody line optical flow constraint(MLOFC), a polynomial equation relating motion models and line parameters. We show that the motion models can be obtained analytically as the derivative of the MLOFC at the corresponding line measurement, without knowing the motion model associated with that line. Experiments on real and synthetic sequences are also presented.

title: C-SAW and genAWeave: a two-level aspect weaving toolsuite
abstract: This demonstration will feature overviews of the C-SAW and GenAWeave projects. The first half of the presentation will introduce the concept of two-level aspect weaving, which unites a <i>model transformation</i> tool with a <i>program transformation</i> engine. From models representing an avionics application, it will be shown how changes to model properties trigger corresponding adaptations to the related source code. The second half of the demonstration is focused on using a program transformation engine to perform the task of aspect weaving. In particular, several crosscutting concerns will be woven into an Object Pascal application.

title: A Composition-Oriented Approach for Business Process Formal Design
abstract: Traditional approaches to designing business processes are often inadequate for complicated, distributed, and collaborated settings. However since network technologies have become widespread, an increasing number of business processes exhibit these properties. Therefore a new approach is needed. To this end, we describe the motivation, conceptualization, and analysis of a composition-oriented business process design method. The key advance of our method is that it uses process algebra pi-calculus as its formal basis and builds a complex process by composing simple structured process control modules to reduce the complexity of business process design as well as let several designers collaborate the design of a complex process. To make business process more clear, we also provide way of separating exception processing logic from normal process control logic and provide scooping mechanisms to minimize the detrimental effects of unplanned contingencies by ways of composition

title: Gene Specific Co-regulation Discovery: An Improved Approach
abstract: Discovering gene co-regulatory relationships is a new but important research problem in DNA microarray data analysis. The problem of gene specific co-regulation discovery is to, for a particular gene of interest, called the target gene , identify its strongly co-regulated genes and the condition subsets where such strong gene co-regulations are observed. The study on this problem can contribute to a better understanding and characterization of the target gene. The existing method, using the genetic algorithm (GA), is slow due to its expensive fitness evaluation and long individual representation. In this paper, we propose an improved method for finding gene specific co-regulations. Compared with the current method, our method features a notably improved efficiency. We employ k NN Search Table to substantially speed up fitness evaluation in the GA. We also propose a more compact representation scheme for encoding individuals in the GA, which contributes to faster crossover and mutation operations. Experimental results with a real-life gene microarray data set demonstrate the improved efficiency of our technique compared with the current method.

title: Hyperspectral images lossless compression by a novel three-dimensional wavelet coding
abstract: In this paper, we present a 3D hyperspectral images lossless compression algorithm, which is based on the asymmetric 3D quincunx structured wavelet transform (A3D-QWT) and the adaptive classification coding. A3D-QWT is a simpler 3D transform compared to the classical asymmetric 3D pyramidal wavelet structure (A3D-DWT) with nearly equal entropy; furthermore the spectral correlation after this quincunx structured transform is higher, which is useful to the adaptive classification coding. The adaptive classification coding can make full use of not only the spatial correlation but also the spectral correlation characteristics of hyperspectral images. Experiments show that our method is capable of providing a higher compression performance for hyperspectral images.

title: Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
abstract: Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.