FIRST - Fast Invariant to Rotation and Scale Transform
Invariant Image Features for Augmented Reality and Computer Vision
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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 r...
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.
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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