Plain, Edge, and Texture Detection Based on Orthogonal Moment (original) (raw)
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Image classification using separable invariant moments of Krawtchouk-Tchebichef
2015
In this paper, we propose a new method for image classification by the content in heterogeneous databases. This approach is based on the use of new series of separable discrete orthogonal moments as shape descriptors and the Support Vector Machine as classifier. In fact, the proposed descriptors moments are defined from the bivariate discrete orthogonal polynomials of Charlier-Meixner which are invariant to translation, scaling and rotation of the image. We also propose a new algorithm to accelerate the image classification process. This algorithm is based on two steps: the first step is the fast computation of the values of Charlier-Meixner polynomials by using a new recurrence relationship between the values of polynomials Charlier-Meixner. The second one is the new image representation and slice blocks. The proposed method is tested on three different sets of standard data which are well known to computer vision: COIL-100, 256-CALTECH and Corel. The simulation results show the invariance of the discrete orthogonal separable moments of Charlier-Meixner against the various geometric transformations and the ability for the classification of heterogeneous images.
Image Classification Using Separable Discrete Moments of Charlier-Tchebichef
Lecture Notes in Computer Science, 2014
In this paper, we propose a new set of separable twodimensional discrete orthogonal moments called Charlier-Tchebichef's moments. This set of moments is based on the bivariate discrete orthogonal polynomials defined from the product of Charlier and Tchebichef discrete orthogonal polynomials with one variable. We also present an approach for fast computation of Charlier-Tchebichef's moments by using the image slice representation. In this approach the image is decomposes into series of non-overlapped binary slices and each slice is described by a number of homogenous rectangular blocks. Once the image is partitioned into slices and blocks, the computation of Charlier-Tchebichef's moments can be accelerated, as the moments can be computed from the blocks of each slice. A novel set of Charlier-Tchebichef invariant moments is also presented. These invariant moments are derived algebraically from the geometric invariant moments and their computation is accelerated using an image representation scheme. The presented approaches are tested in several well known computer vision datasets including computational time, image reconstruction, moment's invariability and classification of objects. The performance of these invariant moments used as pattern features for a pattern classification is compared with Tchebichef-Krawtchouk, Tchebichef-Hahn and Krawtchouk-Hahn invariant moments
Image representation using separable two-dimensional continuous and discrete orthogonal moments
Pattern Recognition, 2012
This paper addresses bivariate orthogonal polynomials, which are a tensor product of two different orthogonal polynomials in one variable. These bivariate orthogonal polynomials are used to define several new types of continuous and discrete orthogonal moments. Some elementary properties of the proposed continuous Chebyshev-Gegenbauer moments (CGM), Gegenbauer-Legendre moments (GLM), and Chebyshev-Legendre moments (CLM), as well as the discrete Tchebichef-Krawtchouk moments (TKM), Tchebichef-Hahn moments (THM), Krawtchouk-Hahn moments (KHM) are presented. We also detail the application of the corresponding moments describing the noise-free and noisy images. Specifically, the local information of an image can be flexibly emphasized by adjusting parameters in bivariate orthogonal polynomials. The global extraction capability is also demonstrated by reconstructing an image using these bivariate polynomials as the kernels for a reversible image transform. Comparisons with the known moments are performed, and the results show that the proposed moments are useful in the field of image analysis. Furthermore, the study investigates invariant pattern recognition using the proposed three moment invariants that are independent of rotation, scale and translation, and an example is given of using the proposed moment invariants as pattern features for a texture classification application.
Image Classification Using Novel Set of Charlier Moment Invariants
Wseas Transactions on Signal Processing, 2014
The use of the discrete orthogonal moments, as feature descriptors in image analysis and pattern recognition is limited by their high computational cost. To solve this problem, we propose, in this paper a new approach for fast computation of Charlier"s discrete orthogonal moments. This approach is based on the use of recurrence relation with respect to variable x instead of order n in the computation of Charlier"s discrete orthogonal polynomials and on the image block representation for binary images and intensity slice representation for gray-scale images. The acceleration of the computation time of Charlier moments is due to an innovative image representation, where the image is described by a number of homogenous rectangular blocks instead of individual pixels. A novel set of invariants moment based on the Charlier moments is also proposed. These invariants moment are derived algebraically from the geometric moment invariants and their computation is accelerated using image representation scheme. The proposed algorithms are tested in several well known computer vision datasets, regarding computational time, image reconstruction, invariability and classification. The performance of Charlier invariants moment used as pattern features for a pattern recognition and classification is compared with Hu and Legendre invariants moment.
Discrete vs. continuous orthogonal moments for image analysis
2001
Image feature representation techniques using orthogonal moment functions have been used in many applications such as invariant pattern recognition, object identification and image reconstruction. Legendre and Zernike moments are very popular in this class, owing to their feature representation capability with a minimal information redundancy measure. This paper presents a comparative analysis between these moments and a new set of discrete orthogonal moments based on Tchebichef polynomials. The implementation aspects of orthogonal moments are discussed, and experimental results using both binary and gray-level images are included to show the advantages of discrete orthogonal moments over continuous moments.
This paper presents a new approach and an algorithm for binary image representation, which is applied for the fast and efficient computation of moments in binary images. In the terminology of this paper the binary image representation scheme is called block representation, since it represents the image as a set of nonoverlapping rectangular areas. The fast computation of moments in block represented images, is achieved exploiting the rectangular structure of the blocks.
Improving the performance of image classification by Hahn moment invariants
Journal of the Optical Society of America A, 2013
The discrete orthogonal moments are powerful descriptors for image analysis and pattern recognition. However, the computation of these moments is a time consuming procedure. To solve this problem, a new approach that permits the fast computation of Hahn's discrete orthogonal moments is presented in this paper. The proposed method is based, on the one hand, on the computation of Hahn's discrete orthogonal polynomials using the recurrence relation with respect to the variable x instead of the order n and the symmetry property of Hahn's polynomials and, on the other hand, on the application of an innovative image representation where the image is described by a number of homogenous rectangular blocks instead of individual pixels. The paper also proposes a new set of Hahn's invariant moments under the translation, the scaling, and the rotation of the image. This set of invariant moments is computed as a linear combination of invariant geometric moments from a finite number of image intensity slices. Several experiments are performed to validate the effectiveness of our descriptors in terms of the acceleration of time computation, the reconstruction of the image, the invariability, and the classification. The performance of Hahn's moment invariants used as pattern features for a pattern classification application is compared with Hu [IRE Trans. Inform. Theory 8, 179 (1962)] and Krawchouk [IEEE Trans. Image Process. 12, 1367] moment invariants.
A filter bank method to construct rotationally invariant moments for pattern recognition
We propose multiresolution filter bank techniques to construct rotationally invariant moments. The multiresolution pyramid motivates a simple but efficient feature selection procedure based on a combination of a pruning algorithm, a new version of the Apriori mining techniques and the partially supervised fuzzy C-mean clustering. The recognition accuracy of the proposed techniques has been tested with the reference to conventional methods. The numerical experiments, with more than 50,000 images taken from standard image datasets, demonstrate an accuracy increase ranging from 5% to 27% depending on the noise level.