Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Pose tracking from natural features on mobile phones
ISMAR '08 Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality
Parallel Tracking and Mapping on a camera phone
ISMAR '09 Proceedings of the 2009 8th IEEE International Symposium on Mixed and Augmented Reality
Machine learning for high-speed corner detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This paper describes a system for object detection and tracking implemented as an iPad application, which has been tested and successfully accomplishes its task. The system is based on finding the initial position of the object by matching features between a template and the device's picture. Tracking is achieved by using a pyramidal implementation of the iterative Lucas-Kanade algorithm. The implemented system was tested on two different instruments used for testing internal combustion engines. The paper discusses the possibilities offered by mobile devices, like the iPad, for the development of applications with computer vision and augmented reality elements and also describes the major problems that have been encountered on such platforms.