Signature pattern recognition using pseudo Zernike moments and a fuzzy logic classifier

Nassery, P.; Faez, Karim

doi:10.1109/icip.1996.560637

Cited by 24 publications

(11 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These schemes include moment invariants (Belkasim et al, 1989), Zernike moments (Khotanzad and Hong, 1990), pseudo-Zernike moments (Nassery and Faez, 1996) etc. Several papers have revealed that using the feature set of Zernike moment magnitudes or moment invariants can achieve better results than others (Kim and Kim, 1998).…”

Section: Introductionmentioning

confidence: 99%

Trademark image retrieval by distance–angle pair‐wise histogram

Lin

Shie

Chen

et al. 2005

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Because of the continuously increasing number of registered trademarks, it is more and more difficult to design and register a new trademark without conflicting with the registered trademarks or logos. Traditionally, trademark indexing by text description is applied for trademark image databases. However, as more and more trademarks have been designed with complex content, describing a trademark only with text description is not an efficient approach any more. Therefore, trademark indexing by its content is required for comparing, retrieving and classifying similar trademarks. In this article, a content-based trademark image retrieval system is proposed. The newly designed trademarks can be avoided from being similar with the registered ones with such a system. This scheme makes use of the distance-angle pair-wise histogram as a feature vector of trademark shape for retrieving similar registered trademarks. Several momentbased trademark retrieval schemes have also been implemented in computer simulations. Experiments have been conducted on registered trademark databases. Impressive results are shown to demonstrate the robustness of the proposed scheme. Moreover, it is quite simple to construct the distance-angle pair-wise histogram for a trademark object. Therefore, the retrieval speed is acceptable and the system can be accessed through internet on a web browser.

show abstract

Section: Introductionmentioning

confidence: 99%

Trademark image retrieval by distance–angle pair‐wise histogram

Lin

Shie

Chen

et al. 2005

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…They are widely used in popular types of intelligent systems handling variations of the input data [40,43,50]. Because of the nature of signature verification problems fuzzy approaches are also employed in this kind of research.…”

Section: Related Work and Critical Remarksmentioning

confidence: 99%

A new approach to signature recognition using the fuzzy method

Kudłacik

Porwik

2012

Pattern Anal Applic

View full text Add to dashboard Cite

The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

show abstract

“…, ( (14) where s(k, i) are the Stirling numbers of the first kind satisfying the following recurrence relations Based on Eq. (18), one can derive the translation and scale invariants of Tchebichef moments using the corresponding invariants of geometric moments.…”

Section: 3derivation Of Invariants Using Geometric Momentsmentioning

confidence: 99%

“…Another related orthogonal moments, denoted as pseudo-Zernike moments [9], was derived based on the basis set of pseudo-Zernike polynomials. These orthogonal moments have been proved to be less sensitive to image noise as compared to geometric moments, and possess better feature representation ability [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Translation and scale invariants of Tchebichef moments

Zhu

Xia

et al. 2007

Pattern Recognition

View full text Add to dashboard Cite

Abstract⎯Discrete orthogonal moments such as Tchebichef moments have been successfully used in the field of image analysis. However, the invariance property of these moments has not been studied mainly due to the complexity of the problem. Conventionally, the translation and scale invariant functions of Tchebichef moments can be obtained either by normalizing the image or by expressing them as a linear combination of the corresponding invariants of geometric moments. In this paper, we present a new approach that is directly based on Tchebichef polynomials to derive the translation and scale invariants of Tchebichef moments. Both derived invariants are unchanged under image translation and scale transformation. The performance of the proposed descriptors is evaluated using a set of binary characters.Examples of using the Tchebichef moments invariants as pattern features for pattern classification are also provided.

show abstract

Signature pattern recognition using pseudo Zernike moments and a fuzzy logic classifier

Cited by 24 publications

References 3 publications

Trademark image retrieval by distance–angle pair‐wise histogram

Trademark image retrieval by distance–angle pair‐wise histogram

A new approach to signature recognition using the fuzzy method

Translation and scale invariants of Tchebichef moments

Contact Info

Product

Resources

About