Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '99 Proceedings of the International Workshop on Vision Algorithms: Theory and Practice
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Automatic Mosaicing with Super-Resolution Zoom
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping
Efficient View-Dependent Image-Based Rendering with Projective Texture-Mapping
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Generic Camera Model and Calibration Method for Conventional, Wide-Angle, and Fish-Eye Lenses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Parameter-Free Radial Distortion Correction with Center of Distortion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Integration and Display Using Virtual Mirrors
CRV '08 Proceedings of the 2008 Canadian Conference on Computer and Robot Vision
Calibration of Cameras with Radially Symmetric Distortion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Camera calibration with two arbitrary coaxial circles
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Mirrormap: augmenting 2d mobile maps with virtual mirrors
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
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In this article, an image integration technique called Virtual Mirroring (VM) is evaluated. VM is a technique that combines multiple 2D views of a 3D scene into a single composite image by overlaying views onto virtual mirrors. Given multiple views of a scene, one view is augmented with the remaining views by placing virtual mirrors on the first view and overlaying onto them the corresponding remaining views. Unlike a standard array presentation, where 2D views are not integrated and simply placed adjacent to one another, the VM presentation preserves the relative location, orientation, and scale between views. As such, it is our contention that humans will fare better at performing certain visual tasks, such as scene identification, when viewing a 3D scene via a VM presentation than when viewing an array presentation. We performed an experiment on 12 participants, where participants were required to identify 96 scenes both with a VM and an array presentation and we compared their % correctness and response times. Moreover, we studied the effects of adding an auditory attentional load on performance. We found that regardless of load, participants were able to identify scenes using VM presentation with greater accuracy and at greater speeds.