scispace - formally typeset
Search or ask a question
Topic

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.


Papers
More filters
Patent
Yuichi Bannai1
31 Mar 2006
TL;DR: In this paper, an information processing method and apparatus enables one or more further users to share a mixed reality space image including a virtual object (43) superimposed in a space where a first user (40) exists.
Abstract: An information processing method and apparatus enables one or more further users (50) to share a mixed reality space image including a virtual object(43) superimposed in a space where a first user (40) exists. A first stereo image is acquired based on a stereo video captured by a first stereo capturing section (22) mounted on the first user and a virtual object image created based on the position and orientation of the first stereo capturing section. A second stereo image is acquired based on a stereo video captured by a second stereo capturing section (70) provided in the space where the first user exists and a virtual object image (43) created based on the position and orientation of the second stereo capturing section. An image is selected from the first stereo image and the second stereo image according to an instruction of the further user. The selected image is presented to the further user.

111 citations

Journal ArticleDOI
TL;DR: A global optimization algorithm for solving the detection of significant local reflectional symmetry in grey level images is presented and is related to genetic algorithms and to adaptive random search techniques.
Abstract: The detection of significant local reflectional symmetry in grey level images is considered. Prior segmentation is not assumed, and it is intended that the results could be used for guiding visual attention and for providing side information to segmentation algorithms. A local measure of reflectional symmetry that transforms the symmetry detection problem to a global optimization problem is defined. Reflectional symmetry detection becomes equivalent to finding the global maximum of a complicated multimodal function parameterized by the location of the center of the supporting region, its size, and the orientation of the symmetry axis. Unlike previous approaches, time consuming exhaustive search is avoided. A global optimization algorithm for solving the problem is presented. It is related to genetic algorithms and to adaptive random search techniques. The efficiency of the suggested algorithm is experimentally demonstrated. Just one thousand evaluations of the local symmetry measure are typically needed in order to locate the dominant symmetry in natural test images.

111 citations

Journal ArticleDOI
TL;DR: This architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally, as well as for convolutional neural network architecture in general.

111 citations

Journal ArticleDOI
M.P. Heilbrun1, Paul McDonald, Wiker C, S. Koehler, William Peters 
TL;DR: Initial accuracy tests of stereotactic localization with video cameras were performed using a standard Brown-Roberts-Wells (BRW) phantom simulator coupled with the BRW angiographic localizer.
Abstract: Machine vision techniques (video cameras) can be used to determine the three-dimensional position of objects. This transformation can be accomplished with standard mathematical algorithms. Initial accuracy tests of stereotactic localization with video cameras were performed using a standard Brown-Roberts-Wells (BRW) phantom simulator coupled with the BRW angiographic localizer. Localization accuracy was within 1.5 mm. Potential applications of machine vision techniques include freehand stereotactic localization of the position and orientation of surgical instruments. With sufficient computer speed these techniques can be used for continuous monitoring of the position of instruments within the cranial vault.

110 citations

01 Jan 2001
TL;DR: The Applanix Position and Orientation System for Airborne Vehicles (POS/AV TM) has been used successfully since 1994 to georeference airborne data collected from multispectral and hyperspectral scanners, LIDAR's, and film and digital cameras.
Abstract: This paper describes how position and orientation measurement systems are used to directly georeference airborne imagery data, and presents the accuracies that are attainable for the final mapping products. The Applanix Position and Orientation System for Airborne Vehicles (POS/AV TM ) has been used successfully since 1994 to georeference airborne data collected from multispectral and hyperspectral scanners, LIDAR’s, and film and digital cameras. The POS/ AV TM uses integrated inertial/GPS technology to directly compute the position and orientation of the airborne sensor with respect to the local mapping frame. A description of the POS/AV TM system is given, along with an overview of the sensors used and the theory behind the integrated inertial/GPS processing. An error analysis for the airborne direct georeferencing technique is then presented. Firstly, theoretical analysis is used to determine the attainable positioning accuracy of ground objects using only camera position, attitude, and image data, without ground control. Besides theoretical error analysis, a practical error analysis was done to present actual results using only the POS data plus digital imagery without ground control except for QA/QC. The results show that the use of POS/AV enables a variety of mapping products to be generated from airborne navigation and imagery data without the use of ground control.

110 citations


Network Information
Related Topics (5)
Segmentation
63.2K papers, 1.2M citations
82% related
Pixel
136.5K papers, 1.5M citations
79% related
Image segmentation
79.6K papers, 1.8M citations
78% related
Image processing
229.9K papers, 3.5M citations
77% related
Feature (computer vision)
128.2K papers, 1.7M citations
76% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202212
2021535
2020771
2019830
2018727
2017691