Orientation (computer vision)

About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.

Weighted selection of image features for resolved rate visual feedback control

Abstract: The authors develop methodologies for the automatic selection of image features to be used to visually control the relative position and orientation (pose) between the end-effector of an eye-in-hand robot and a workpiece. A resolved motion rate control scheme is used to update the robot's pose based on the position of three features in the camera's image. The selection of these three features depends on a blend of image recognition and control criteria. The image recognition criteria include feature robustness, completeness, cost of feature extraction, and feature uniqueness. The control criteria include system observability, controllability, and sensitivity. A weighted criteria function is used to select the combination of image features that provides the best control of the end-effector of a general six-degrees-of-freedom manipulator. Both computer simulations and laboratory experiments on a PUMA robot arm were conducted to verify the performance of the feature-selection criteria. >

Frame-free dynamic digital vision

Abstract: Conventional image sensors produce massive amounts of redundant data and are limited in temporal resolution by the frame rate This paper reviews our recent breakthrough in the development of a high- performance spike-event based dynamic vision sensor (DVS) that discards the frame concept entirely, and then describes novel digital methods for efficient low-level filtering and feature extraction and high-level object tracking that are based on the DVS spike events These methods filter events, label them, or use them for object tracking Filtering reduces the number of events but improves the ratio of informative events Labeling attaches additional interpretation to the events, eg orientation or local optical flow Tracking uses the events to track moving objects Processing occurs on an event-by-event basis and uses the event time and identity as the basis for computation A common memory object for filtering and labeling is a spatial map of most recent past event times Processing methods typically use these past event times together with the present event in integer branching logic to filter, label, or synthesize new events These methods are straightforwardly computed on serial digital hardware, resulting in a new event- and timing-based approach for visual computation that efficiently integrates a neural style of computation with digital hardware All code is open- sourced in the jAER project (jaerwikisourceforgenet)

Three-Dimensional Structure Determination from Common Lines in Cryo-EM by Eigenvectors and Semidefinite Programming

Abstract: The cryo-electron microscopy reconstruction problem is to find the three-dimensional (3D) structure of a macromolecule given noisy samples of its two-dimensional projection images at unknown random directions. Present algorithms for finding an initial 3D structure model are based on the “angular reconstitution” method in which a coordinate system is established from three projections, and the orientation of the particle giving rise to each image is deduced from common lines among the images. However, a reliable detection of common lines is difficult due to the low signal-to-noise ratio of the images. In this paper we describe two algorithms for finding the unknown imaging directions of all projections by minimizing global self-consistency errors. In the first algorithm, the minimizer is obtained by computing the three largest eigenvectors of a specially designed symmetric matrix derived from the common lines, while the second algorithm is based on semidefinite programming (SDP). Compared with existing algorithms, the advantages of our algorithms are five-fold: first, they accurately estimate all orientations at very low common-line detection rates; second, they are extremely fast, as they involve only the computation of a few top eigenvectors or a sparse SDP; third, they are nonsequential and use the information in all common lines at once; fourth, they are amenable to a rigorous mathematical analysis using spectral analysis and random matrix theory; and finally, the algorithms are optimal in the sense that they reach the information theoretic Shannon bound up to a constant for an idealized probabilistic model.