Showing papers by "John Porrill published in 1987"

Matching geometrical descriptions in three-space

Abstract: A matching strategy for combining two or more three-space descriptions, obtained here from edge-based binocular stereo, of a scene is discussed. The scheme combines features of a number of recent model-matching algorithms with heuristics aimed to reduce the space of potential rigid transformations that relate scene descriptions.

Optimal combination of multiple sensors including stereo vision

Abstract: The statistical combination of information from multiple sources is considered. The particular needs of the target application, stereo vision, require that the formulation be adequate to deal with highly correlated errors and constraints, and that it deal naturally with geometrical data.

TINA: the sheffield AIVRU vision system

Abstract: We describe the Sheffield AIVRU 3D vision system for robotics. The system currently supports model based object recognition and location; its potential for robotics applications is demonstrated by its guidance of a UMI robot arm in a pick and place task. The system comprises: 1) The recovery of a sparse depth map using edge based passive stereo triangulation. 2) The grouping, description and segmentation of edge segments to recover a 3D description of the scene geometry in terms of straight lines and circular arcs. 3) The statistical combination of 3D descriptions for the purpose of object model creation from multiple stereo views, and the propagation of constraints for within view refinement. 4) The matching of 3D wireframe models to 3D scene descriptions, to recover an initial estimate of their position and orientation.

Segmentation and description of binocularly viewed contours

Abstract: Edge-based binocular correspondence produces a sparse disparity map, available information being distributed along space curves which project to matched image edges. To become useful, these contours must be parsed into describable sections. A novel view of the segmentation/ description process is presented and an effective algorithm based on the model is described.

TINA: A 3D Vision System for Pick and Place.

Abstract: The paper describes the Sheffield AIVRU 3D vision system for robotics. The system currently supports model-based object recognition and location; its potential for robotics applications is demonstrated by its guidance of a UMI robot arm in a pick-and-place task. The system comprises: recovery of a sparse depth map using edgebased passive stereo triangulation; grouping, description and segmentation of edge segments to recover a 3D description of the scene geometry in terms of straight lines and circular arcs; statistical combination of 3D descriptions for the purpose of object model creation from multiple stereo views, and the propagation of constraints for within-view refinement; and matching 3D wireframe models to 3D scene descriptions to recover an initial estimate of their position and orientation.