The main objective of this paper is to investigate the use of Quality Threshold ARTMAP (QTAM) neural network in classifying the feature vectors generated by moment invariant for the insect recognition task. In this work, six different types of moment invariant technique are adopted to extract the shape features of the insect images. These moment techniques are Geometrical Moment Invariant (GMI), United Moment Invariant (UMI), Zernike Moment Invariant (ZMI), Legendre Moment Invariant (LMI), Tchebichef Moment Invariant (TMI) and Krawtchouk Moment Invariant (KMI). All the moment techniques are analyzed using the concept of intraclass and interclass analysis. In intraclass analysis, several computation methods are introduced in order to examine the invariance properties of adopted moment techniques for the same insect object. Meanwhile, the classification accuracy of neural networks is adopted to measure the interclass characteristic and the effectiveness of moment technique in extracting the shape features of insect images. Other types of neural networks are also utilized in this research work. This includes novel enhancement technique based on the Gaussian and Mahalanobis function that design to increase its prediction accuracy. All the other networks used to classify the feature vectors are based on the Fuzzy ARTMAP (FAM) neural network. The experimental results indicated that the Krawtchouk Moment Invariant technique generated the highest classification accuracy for most of the networks used and generated the smallest error for the intraclass analysis. Using different normalization technique, the Quality Threshold ARTMAP and Mahalanobis distance function (QTAM-m) network gave the highest insect recognition results when compared to other networks.

An insect classification analysis based on shape features using quality threshold ARTMAP and moment invariant

An efficient multiple classifier system for Arabic handwritten words recognition

In this work, we propose a new set of an intelligent classifier of biomedical color images using Quaternion Discrete Radial Tchebichef Moments (QRTM). This moment has shown very high robustness in terms of color image representation. Thus, we propose a new approach for the fast and accurate reconstruction of multi-level and color images. This approach based on the reconstruction of color images by applying matrix computation. In the second step, we propose a new method for the extraction of Quaternion Discrete Radial Tchebichef Moments invariant (QRTMI). This method based on the representation of the extraction of these invariants. The performance of the invariance of these moments under the three types of geometrical transformations (Rotation, translation, scale) are very important. Finally, we present a new model based on a Multilayer perceptron (MLP) for the classification of biomedical color images. The experimental results show that the QRTMI very powerful compared with radial Legendre - Fourier and Quaternion discrete radial Krawtchouk moments invariant for the biomedical colors images using the BreaKHis Database (Breast Tumor).

Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments

This paper proposes a new set of 3D rotation scaling and translation invariants of 3D radially shifted Legendre moments. We aim to develop two kinds of transformed shifted Legendre moments: a 3D substituted radial shifted Legendre moments (3DSRSLMs) and a 3D weighted radial one (3DWRSLMs). Both are centered on two types of polynomials. In the first case, a new 3D radial complex moment is proposed. In the second case, new 3D substituted/weighted radial shifted Legendre moments (3DSRSLMs/3DWRSLMs) are introduced using a spherical representation of volumetric image. 3D invariants as derived from the suggested 3D radial shifted Legendre moments will appear in the third case. To confirm the proposed approach, we have resolved three issues. To confirm the proposed approach, we have resolved three issues: rotation, scaling and translation invariants. The result of experiments shows that the 3DSRSLMs and 3DWRSLMs have done better than the 3D radial complex moments with and without noise. Simultaneously, the reconstruction converges rapidly to the original image using 3D radial 3DSRSLMs and 3DWRSLMs, and the test of 3D images are clearly recognized from a set of images that are available in Princeton shape benchmark (PSB) database for 3D image.

Rotation Scaling and Translation Invariants of 3D Radial Shifted Legendre Moments

Handwriting recognition is a complicated process that many applications rely on, such as mail sorting, cheque processing, digitalisation and translation. The recognition of handwritten Arabic is still an ongoing challenge mainly due to the similarity among its letters and the variety of writing styles. In this paper, a novel approach is proposed that uses support vector machines (SVMs) with normalized poly kernel. The well-known Arabic handwritten database, IFN/ENIT-database, which contains 936 city names with more than 32,492 instances, is used to test the proposed system. The results of this novel approach are compared with the results of two different studies. The comparison shows that a higher accuracy rate is obtained using the proposed system.

Off-line handwritten arabic word recognition using SVMs with normalized poly kernel

Radial Charlier moments as discrete orthogonal moments in the polar coordinate are better descriptor in image processing applications and pattern recognition. However, the translation and scale invariant property of these moments have not been studied due to its complexity of the problem. In this paper, we present a method to construct a set of rotation invariants extracted from radial Charlier moments, named radial Charlier moment invariants (RCMI). Experimental results show the efficiency and the robustness to reconstruction error (MSE), peak signal to noise ratio (PSNR) of the proposed method.

Radial Charlier moment invariants for 2D object/image recognition

This paper proposes a new approach investigating the application of Krawtchouk moment method to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. The first step (pre-processing) of the proposed method takes into account the discriminative properties of invariant Krawtchouk moments. The second step (recognition) is achieved by using Multilayer Feedforward Neural Network (MFNN) as a classifier with the stochastic back propagation as a learning algorithm. Finite vectors obtained as a result in the pre-processing phase are then fed into the neural network system. We demonstrate experimentally that the choice of a Krawtchouk moment subset which contains sufficient and discriminative information about the input pattern is crucial in the convergence of the neural network training algorithm to a satisfactory performance level. The proposed method has been tested on the well known IFN/ENIT database of Arabic handwritten words. It produces excellent and encouraging results by reducing the computational burden of the recognition system and presenting a high recognition rate with a good generalization ability.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

/pdf/krawtchouk-moment-feature-extraction-for-neural-arabic-320ops0g59.pdf

Krawtchouk moment feature extraction for neural Arabic handwritten words recognition

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Radial Charlier moment invariants for 2D object/image recognition

Krawtchouk moment feature extraction for neural Arabic handwritten words recognition