Image registration by local approximation methods
Image and Vision Computing
Fitting ellipses and predicting confidence envelopes using a bias corrected Kalman filter
Image and Vision Computing - Special issue: 5th Alvey vision meeting
Least-Squares Estimation of Transformation Parameters Between Two Point Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A note on the least squares fitting of ellipses
Pattern Recognition Letters
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
From the Hough transform to a new approach for the detection and approximation of elliptical arcs
Computer Vision and Image Understanding
Multiple view geometry in computer vision
Multiple view geometry in computer vision
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic algorithm for affine point pattern matching
Pattern Recognition Letters
IEICE - Transactions on Information and Systems
A Pseudo-Hilbert Scan for Arbitrarily-Sized Arrays
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Image data ordering and compression using Peano scan and LOT
IEEE Transactions on Consumer Electronics
Automatic target recognition by matching oriented edge pixels
IEEE Transactions on Image Processing
Hilbert scanning search algorithm for motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
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Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.