Improved moment invariants for shape discrimination

Chen, Chaur-Chin

doi:10.1016/0031-3203(93)90121-c

Cited by 182 publications

(85 citation statements)

References 7 publications

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“…Thus, the United Moment Invariant (UMI) (Yinan et al, 2003) is useful for global features' extraction from the handwriting and shape of fingerprint produced by twins. UMI was formulated based on Hu (1962) Geometric Moment Invariant (GMI) and Chen (1993) Improve Moment Invariant (IMI). In this context, the feasibility of GMI for representation of region in subtle situation has been proven in the work of Chen (1993), but considering that boundary representation requires high computational times, IMI is proposed for boundary and quicker computation.…”

Section: Aumi With Twin Multi-biometricmentioning

confidence: 99%

A Multimodal Biometric System using Global Features for Identical Twins Identification

Mohammed

Shamsuddin

2018

Journal of Computer Science

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Pattern recognition studies are currently focusing on twin's biometric identification. The system of twins' biometric Identification can potentially differentiate the individual's biometric pattern. With the new Unimodal biometric identification, identical twins are precisely and reliably identified, with well exposure of certain traits. However, it is much more challenging to identify identical twins as they share so many similarities between them, as opposed to identifying the non-twins. Therefore, this study proposes the use of more than one biometric trait with global features. Pattern recognition application includes extracting and selecting meaningful features and this has brought to the key question in twin handwriting-fingerprint identification: How to obtain features from many writing styles and shapes of twin handwritingfingerprint in order that the reflection of the right person between twins can be obtained. Global with Aspect United Moment Invariant for the extractions of global feature using the identical twin multi-biometric identification is thus proposed by this study.

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Section: Aumi With Twin Multi-biometricmentioning

confidence: 99%

A Multimodal Biometric System using Global Features for Identical Twins Identification

Mohammed

Shamsuddin

2018

Journal of Computer Science

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“…It is of great importance to define pattern features for characterizing the leaf images. For this purpose, circle moment invariants (CMI) are applied on the extracted shape boundary image and are proven to be invariant under object translation (change of position), scale (change of size) and rotation (change of orientation) [11]. Shape boundary of the binary images proposed by Karuna etal [10].…”

Section: Circle Moment Invariants (Cmi)mentioning

confidence: 99%

Moment based Object Classification from Shapes

Karuna¹,

Reddy²,

Rao³

2015

IJCA

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A flexible object recognition system is considered which can compute the good features for high classification of objects. To characterize an output class or object usage of appropriate features is vital for all classification problems. Moment invariant functions are very useful for object classification, regardless of its orientation, size and position. The main features leading us to our objective are representations of objects using 2D images. The proposed paper focus on, the classification performance of classifiers with moment-based feature sets is introduced after only efficient feature extraction. The experimental results shows that the proposed feature descriptor performs well with existing texture descriptors in terms of classification accuracy.

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“…The geometric moments can also be computed on the contour of the object as introduced in (Chen, 1993;Sardana et al, 1994) by using eq. 1 only for the pixels of the boundary of the object.…”

Section: Moment Invariantsmentioning

confidence: 99%

Relational indexing of vectorial primitives for symbol spotting in line-drawing images

Rusiñol

Borrís

Lladós

2010

Pattern Recognition Letters

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This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an offthe-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.

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Improved moment invariants for shape discrimination

Cited by 182 publications

References 7 publications

A Multimodal Biometric System using Global Features for Identical Twins Identification

A Multimodal Biometric System using Global Features for Identical Twins Identification

Moment based Object Classification from Shapes

Relational indexing of vectorial primitives for symbol spotting in line-drawing images

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