Radial and Angular Moment Invariants for Image Identification

Reddi, S. S.

doi:10.1109/tpami.1981.4767087

Cited by 126 publications

(28 citation statements)

References 2 publications

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“…In the literature, many works have presented various methods to derive moment invariants to other transformations such as scale and 2D rotation. For instance, moment invariants to rotation have been obtained from radial and angular moments [23], Zernike moments [30], [2], [32], and complex moments [1], [10]. As for invariants to scale, several combinations of moments have been proposed, such as for example [19]:…”

Section: A Moment Invariantsmentioning

confidence: 99%

“…In addition to the advantages recalled above, judicious combinations of moments are indeed invariant to some transformations such as scale, 2D translation and/or 2D rotation. This property is of great interest in pat tern recognition, which explains the amount of work about the determination of moments invariants (see [23], [2], [32], [19] for instance). As it will be shown in this paper, such invariance property is also of particular interest in visual servoing.…”

Section: Introduction T O Date An Open Question In Image-based VImentioning

confidence: 99%

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Point-based and region-based image moments for visual servoing of planar objects

2005

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To cite this version:O. Tahri, François Chaumette. Point-based and region-based image moments for visual servoing of planar objects. IEEE Transactions on Robotics, IEEE, 2005, 21 (6) Abstract-Moments are generic (and usually intuitive) descriptors that can be computed from several kinds of objects defined either from closed contours or from a set of points. In this paper, we present improvements in image-based visual servo using image moments. First, the analytical form of the interaction matrix related to the moments computed from a set of coplanar points is derived, and we show that it is different of the form obtained previously using coplanar closed contours. Six visual features are selected to design a decoupled control scheme when the object is parallel to the image plane. This nice property is then generalized to the case where the desired object position is not parallel to the image plane. Finally, experimental results are presented to illustrate the validity of our approach and its robustness with respect to modeling errors.

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Section: A Moment Invariantsmentioning

confidence: 99%

Section: Introduction T O Date An Open Question In Image-based VImentioning

confidence: 99%

Point-based and region-based image moments for visual servoing of planar objects

2005

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“…However, Hu's Invariant Moments have not come from a family of orthogonal functions that implies the linear independence of the corresponding Moments. Reddi [5] [6]. Abu-Mostafa and Psaltis [7] proposed complex Geometrical Moments, and used them to derive a set of moment Invariants.…”

Section: Related Workmentioning

confidence: 99%

Efficient Image Recognition Technique Using Invariant Moments and Principle Component Analysis

Arafah¹,

Moghli²

2017

JDAIP

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Image recognition is widely used in different application areas such as shape recognition, gesture recognition and eye recognition. In this research, we introduced image recognition using efficient invariant moments and Principle Component Analysis (PCA) for gray and color images using different number of invariant moments. We used twelve moments for each image of gray images and Hu's seven moments for color images to decrease dimensionality of the problem to 6 PCA's for gray and 5 PCA's for color images and hence the recognition time. PCA is then employed to decrease dimensionality of the problem and hence the recognition time and this is our main objective. The PCA is derived from Karhunen-Loeve's transformation. Given an N-dimensional vector representation of each image, PCA tends to find a K-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space. This new subspace is normally lower dimensional (K  N). Three known datasets are used. The first set is the known Flower dataset. The second is the Africans dataset, and the third is the Shapes dataset. All these datasets were used by many researchers.

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“…Dudani et al have applied moment invariants to identify an aircraft [2]. Reddi [8] proposed a simpler construction method using radial and angular moments. Teague [3] extended Hu's idea by introducing the orthogonal moment set to recover images from moments.…”

Section: Introductionmentioning

confidence: 99%

Retina Identification Based on Moment Invariant

Kavianpour¹,

Shoari²,

Kavianpour³

2015 ASEE Annual Conference and Exposition Proceedings

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This paper demonstrates the importance of topics in physics and mathematics courses such as matrix, eigenvectors, centroid, and moment of inertia in the pattern recognition applications.Teaching advance topics in physics and mathematics is not an easy task. Students always ask this question: What is the use? The best practice for teaching these topics is to combine them with a real industry application. For example, computer hard disk reading is based on the concept of derivatives and the change in the magnetic fields. To demonstrate this type of teaching, the use of three dimensional moments in the pattern recognition is explained.The three-dimensional moments may be used to detect different patterns in a digitally represented image. The combination of several moments for an object has an invariant property. The mathematical foundation of an invariant feature is related to the theory that images taken from different angles from the same object have the same set of moment invariants. Moments contain information of an image which can be used in calculating the location and orientation of an object. An algorithm for recognition of an individual identity based on a digitally represented image of the scanned retina is presented. The technology is based upon the fact that no two retinal patterns are alike.In this paper, the nine parameters of an ellipsoidal shape fitted into a retinal image such as coordinates of the center of the ellipsoid, the length of major, minor, intermediate axes, and the direction of three axes will be calculated. For each individual, these parameters are unique.

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Radial and Angular Moment Invariants for Image Identification

Cited by 126 publications

References 2 publications

Point-based and region-based image moments for visual servoing of planar objects

Point-based and region-based image moments for visual servoing of planar objects

Efficient Image Recognition Technique Using Invariant Moments and Principle Component Analysis

Retina Identification Based on Moment Invariant

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