Learn More
The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares(More)
Within the context of active vision, scant attention has been paid to the execution of motion saccades—rapid re-adjustments of the direction of gaze to attend to moving objects. In this paper we first develop a methodology for, and give real-time demonstrations of, the use of motion detection and segmentation processes to initiate “capture saccades” towards(More)
User interaction within a virtual environment may take various forms: a teleconferencing application will require users to speak to each other (Geak, 1993), with computer supported cooperative working; an Engineer may wish to pass an object to another user for examination; in a battle field simulation (McDonough, 1992), users might exchange fire. In all(More)
CAVE-like displays allow a user to walk in to a virtual environment, and use natural movement to change the viewpoint of virtual objects which they can manipulate with a hand held device. This maps well to many surgical procedures offering strong potential for training and planning. These devices may be networked together allowing geographically remote(More)
This paper introduces a new robust nonlinear identification algorithm using the predicted residual sums of squares (PRESS) statistic and forward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the(More)
—In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on(More)