A variety of application areas can be attained in the fields of
human-computer interaction for augmented and mixed reality, object
tracking and gesture recognition. By combining the areas of 3D
computer graphics, computer vision and programming, we have
developed a fast, yet robust and accurate image feature detector
and matcher to solve common problems that arise in the mentioned
research areas. In this thesis, frequent computer vision and
augmented reality problems related to camera calibration, object
recognition/tracking, image stitching and gesture recognition, are
shown to be solved in real-time using our novel feature detection
and matching technique. Our method is referred to as FIRST - Fast
Invariant to Rotation and Scale Transform. We have also generalized
our texture tracking algorithm to a near model base tracking
method, using pre-calibrated static planar structures. Our results
are compared and discussed with other state of the art works in the
areas of invariant feature descriptors and vision based augmented
reality, both in accuracy and performance.
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