Fundamentals of digital image processing
Fundamentals of digital image processing
Some Properties of the E Matrix in Two-View Motion Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the finite displacement using moments
Pattern Recognition Letters
On Recognizing and Positioning Curved 3-D Objects from Image Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Merging virtual objects with the real world: seeing ultrasound imagery within the patient
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Virtual reality: through the new looking glass
Virtual reality: through the new looking glass
A survey of image registration techniques
ACM Computing Surveys (CSUR)
Knowledge-based augmented reality
Communications of the ACM - Special issue on computer augmented environments: back to the real world
Autocalibration for virtual environments tracking hardware
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Improving static and dynamic registration in an optical see-through HMD
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Using Geometric Distance Fits for 3-D Object Modeling and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Numerical Methods
Dynamic registration correction in augmented-reality systems
VRAIS '95 Proceedings of the Virtual Reality Annual International Symposium (VRAIS'95)
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The paper proposes aligning perspective contours with 3D objects without knowledge of the correspondence between 2D image points and a projected 3D model, by matching some global image characteristics from the images. It was found that simply using image moments was not sufficient for registration, but converting the image with orthogonal polynomials gave good results. The proposed approach only requires computational complexity of O(n), where n is the number of image contour points to be aligned, and provides the flexibility of not requiring the same number of 2D and 3D points. Furthermore, convergence regions for finding numerical solutions are enlarged significantly by using central moments. Global convergence is achieved using 64 different initial guesses. Experiments with Monte Carlo analysis of three different objects with different movements have been conducted to show the effectiveness of the proposed approach. The results of using three different orthogonal polynomials: Chebyshev, Gram and Legendre polynomials at three different noise levels are also compared.