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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.

- Publication
- IEICE TRANSACTIONS on Information Vol.E91-D No.10 pp.2477-2484

- Publication Date
- 2008/10/01

- Publicized

- Online ISSN
- 1745-1361

- DOI
- 10.1093/ietisy/e91-d.10.2477

- Type of Manuscript
- PAPER

- Category
- Pattern Recognition

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Li TIAN, Sei-ichiro KAMATA, "A Two-Stage Point Pattern Matching Algorithm Using Ellipse Fitting and Dual Hilbert Scans" in IEICE TRANSACTIONS on Information,
vol. E91-D, no. 10, pp. 2477-2484, October 2008, doi: 10.1093/ietisy/e91-d.10.2477.

Abstract: 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.

URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e91-d.10.2477/_p

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@ARTICLE{e91-d_10_2477,

author={Li TIAN, Sei-ichiro KAMATA, },

journal={IEICE TRANSACTIONS on Information},

title={A Two-Stage Point Pattern Matching Algorithm Using Ellipse Fitting and Dual Hilbert Scans},

year={2008},

volume={E91-D},

number={10},

pages={2477-2484},

abstract={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.},

keywords={},

doi={10.1093/ietisy/e91-d.10.2477},

ISSN={1745-1361},

month={October},}

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TY - JOUR

TI - A Two-Stage Point Pattern Matching Algorithm Using Ellipse Fitting and Dual Hilbert Scans

T2 - IEICE TRANSACTIONS on Information

SP - 2477

EP - 2484

AU - Li TIAN

AU - Sei-ichiro KAMATA

PY - 2008

DO - 10.1093/ietisy/e91-d.10.2477

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E91-D

IS - 10

JA - IEICE TRANSACTIONS on Information

Y1 - October 2008

AB - 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.

ER -