IEICE TRANSACTIONS on Information
Signature Pattern Recognition Using Moments Invariant and a New Fuzzy LVQ Model
Summary :
In this paper we have introduced a new method for signature pattern recognition, taking advantage of some image moment transformations combined with fuzzy logic approach. For this purpose first we tried to model the noise embedded in signature patterns inherently and separate it from environmental effects. Based on the first step results, we have performed a mapping into the unit circle using the error least mean square (LMS) error criterion, to get ride of the variations caused by shifting or scaling. Then we derived some orientation invariant moments introduced in former reports and studied their statistical properties in our special input space. Later we defined a fuzzy complex space and also a fuzzy complex similarity measure in this space and constructed a new training algorithm based on fuzzy learning vector quantization (FLVQ) method. A comparison method has also been proposed so that any input pattern could be compared to the learned prototypes through the pre-defined fuzzy similarity measure. Each set of the above image moments were used by the fuzzy classifier separately and the mis-classifications were detected as a measure of error magnitude. The efficiency of the proposed FLVQ model has been numerically shown compared to the conventional FLVQs reported so far. Finally some satisfactory results are derived and also a comparison is made between the above considered image transformations.
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- IEICE TRANSACTIONS on Information Vol.E81-D No.12 pp.1483-1493
- Publication Date
- 1998/12/25
- Publicized
- Online ISSN
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- Type of Manuscript
- Category
- Image Processing,Computer Graphics and Pattern Recognition
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Payam NASSERY, Karim FAEZ, "Signature Pattern Recognition Using Moments Invariant and a New Fuzzy LVQ Model" in IEICE TRANSACTIONS on Information,
vol. E81-D, no. 12, pp. 1483-1493, December 1998, doi: .
Abstract: In this paper we have introduced a new method for signature pattern recognition, taking advantage of some image moment transformations combined with fuzzy logic approach. For this purpose first we tried to model the noise embedded in signature patterns inherently and separate it from environmental effects. Based on the first step results, we have performed a mapping into the unit circle using the error least mean square (LMS) error criterion, to get ride of the variations caused by shifting or scaling. Then we derived some orientation invariant moments introduced in former reports and studied their statistical properties in our special input space. Later we defined a fuzzy complex space and also a fuzzy complex similarity measure in this space and constructed a new training algorithm based on fuzzy learning vector quantization (FLVQ) method. A comparison method has also been proposed so that any input pattern could be compared to the learned prototypes through the pre-defined fuzzy similarity measure. Each set of the above image moments were used by the fuzzy classifier separately and the mis-classifications were detected as a measure of error magnitude. The efficiency of the proposed FLVQ model has been numerically shown compared to the conventional FLVQs reported so far. Finally some satisfactory results are derived and also a comparison is made between the above considered image transformations.
URL: https://global.ieice.org/en_transactions/information/10.1587/e81-d_12_1483/_p
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@ARTICLE{e81-d_12_1483,
author={Payam NASSERY, Karim FAEZ, },
journal={IEICE TRANSACTIONS on Information},
title={Signature Pattern Recognition Using Moments Invariant and a New Fuzzy LVQ Model},
year={1998},
volume={E81-D},
number={12},
pages={1483-1493},
abstract={In this paper we have introduced a new method for signature pattern recognition, taking advantage of some image moment transformations combined with fuzzy logic approach. For this purpose first we tried to model the noise embedded in signature patterns inherently and separate it from environmental effects. Based on the first step results, we have performed a mapping into the unit circle using the error least mean square (LMS) error criterion, to get ride of the variations caused by shifting or scaling. Then we derived some orientation invariant moments introduced in former reports and studied their statistical properties in our special input space. Later we defined a fuzzy complex space and also a fuzzy complex similarity measure in this space and constructed a new training algorithm based on fuzzy learning vector quantization (FLVQ) method. A comparison method has also been proposed so that any input pattern could be compared to the learned prototypes through the pre-defined fuzzy similarity measure. Each set of the above image moments were used by the fuzzy classifier separately and the mis-classifications were detected as a measure of error magnitude. The efficiency of the proposed FLVQ model has been numerically shown compared to the conventional FLVQs reported so far. Finally some satisfactory results are derived and also a comparison is made between the above considered image transformations.},
keywords={},
doi={},
ISSN={},
month={December},}
Copy
TY - JOUR
TI - Signature Pattern Recognition Using Moments Invariant and a New Fuzzy LVQ Model
T2 - IEICE TRANSACTIONS on Information
SP - 1483
EP - 1493
AU - Payam NASSERY
AU - Karim FAEZ
PY - 1998
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E81-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 1998
AB - In this paper we have introduced a new method for signature pattern recognition, taking advantage of some image moment transformations combined with fuzzy logic approach. For this purpose first we tried to model the noise embedded in signature patterns inherently and separate it from environmental effects. Based on the first step results, we have performed a mapping into the unit circle using the error least mean square (LMS) error criterion, to get ride of the variations caused by shifting or scaling. Then we derived some orientation invariant moments introduced in former reports and studied their statistical properties in our special input space. Later we defined a fuzzy complex space and also a fuzzy complex similarity measure in this space and constructed a new training algorithm based on fuzzy learning vector quantization (FLVQ) method. A comparison method has also been proposed so that any input pattern could be compared to the learned prototypes through the pre-defined fuzzy similarity measure. Each set of the above image moments were used by the fuzzy classifier separately and the mis-classifications were detected as a measure of error magnitude. The efficiency of the proposed FLVQ model has been numerically shown compared to the conventional FLVQs reported so far. Finally some satisfactory results are derived and also a comparison is made between the above considered image transformations.
ER -
English
