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.

Efficient & Effective Prioritized Matching for Large-Scale Image-Based Localization

Abstract: Accurately determining the position and orientation from which an image was taken, i.e., computing the camera pose, is a fundamental step in many Computer Vision applications. The pose can be recovered from 2D-3D matches between 2D image positions and points in a 3D model of the scene. Recent advances in Structure-from-Motion allow us to reconstruct large scenes and thus create the need for image-based localization methods that efficiently handle large-scale 3D models while still being effective, i.e., while localizing as many images as possible. This paper presents an approach for large scale image-based localization that is both efficient and effective. At the core of our approach is a novel prioritized matching step that enables us to first consider features more likely to yield 2D-to-3D matches and to terminate the correspondence search as soon as enough matches have been found. Matches initially lost due to quantization are efficiently recovered by integrating 3D-to-2D search. We show how visibility information from the reconstruction process can be used to improve the efficiency of our approach. We evaluate the performance of our method through extensive experiments and demonstrate that it offers the best combination of efficiency and effectiveness among current state-of-the-art approaches for localization.

Interactive aircraft training system and method

Abstract: The system includes a computer (20), a detector (40) for detecting and tracking head orientation and head movement of an individual, a pair of tactile gloves (42, 44) which are donned by the individual (51) for detecting and transmitting to the computer (20) as input data orientation and movements of the hands of the individual inserted in the tactile gloves (42, 44), a stereoscopic, head-mounted display (31), a subsystem for enabling the computer to generate a stereoscopic image of the training environment, a subsystem for causing the stereoscopic image of the training environment to be displayed and changed by the computer relative to input data received by the computer relating to the head orientation and head movement of the individual, relative to input data received by the computer relating to orientation and movement of the hands of the individual inserted in the tactile gloves, and relative to input data reflecting realistic changes in the training environment that would be perceived by the individual if interacting with an identical, non-simulated, training environment. An object (43) representative of a tool is adapted for transmitting tactile information to the computer (20). Sounds representative of the environment are transmitted to the individual (51) through the earphones (33). Vocal emanations of the individual are detected by a microphone (48). System peripheral items (31, 33, 40, 42, 43, 44, 48) are connected to the computer (20) by means of wires (60, 61, 62, 63, 64, 65). The computer (20) interfaces and incorporates an environment simulation and modeling subsystem (22), an image generation component (24), a user interface management component (26), and a component for formulating and transmitting instructional input (28).

Design of steerable filters for feature detection using canny-like criteria

Abstract: We propose a general approach for the design of 2D feature detectors from a class of steerable functions based on the optimization of a Canny-like criterion. In contrast with previous computational designs, our approach is truly 2D and provides filters that have closed-form expressions. It also yields operators that have a better orientation selectivity than the classical gradient or Hessian-based detectors. We illustrate the method with the design of operators for edge and ridge detection. We present some experimental results that demonstrate the performance improvement of these new feature detectors. We propose computationally efficient local optimization algorithms for the estimation of feature orientation. We also introduce the notion of shape-adaptable feature detection and use it for the detection of image corners.