• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.10
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• pp.446-452
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• 2002

For most of pattern recognition applications, it is required to correctly recognize patterns even if they have translation variations. In this paper, to achieve the goal of translation invariant pattern recognition, we propose a new generalized translation invariant second-order neural network using a constraint on the weights. The weight constraint is implemented using generalized translation invariant features which are accumulated sums of pixel combinations. Simulation results will be given to demonstrate that the proposed second-order neural network has the generalized translation invariant property.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.11 s.116
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• pp.1149-1157
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• 2006

Donoho et al. suggested a wavelet thresholding denoising method based on discrete wavelet transform. This paper proposes an improved denoising method using a new thresholding function based on translation-invariant wavelet for underwater acoustic measurement. The conventional wavelet thresholding denoising method causes Pseudo-Gibbs phenomena near singularities due to the lack of translation-invariant of the wavelet basis. To suppress Pseudo-Gibbs phenomena, a denoising method combining a new thresholding function based on the translation-invariant wavelet transform is proposed in this paper. The new thresholding function is a modified hard-thresholding to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian noise. The experimental results show that the proposed method can effectively eliminate noise, extract characteristic information of radiated noise signals.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.3
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• pp.281-286
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• 2003

Wavelet Transform can effectively represent the local characteristics of a signal in the space-frequency domain. However, the feature vector extracted using wavelet transform is not translation invariant. This paper describes a new feature extraction method using wavelet transform, which is translation-invariant. Based on this translation-invariant feature extraction, the iris recognition method, based on this feature extraction method, is robust to noises. Experimentally, we show that the proposed method produces super performance in iris recognition.

• The Journal of the Acoustical Society of Korea
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• v.25 no.7
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• pp.333-338
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• 2006

This paper proposes an improved do-noising method using multi-thresholding function based on translation-invariant (W) wavelet proposed by Donoho et al. for underwater radiated noise measurement. The traditional wavelet thresholding de-noising method causes Pseudo-Gibbs phenomena near singularities due to discrete wavelet transform. In order to suppress Pseudo-Gibbs Phenomena, a do-noising method combining multi-thresholding function with the translation-invariant wavelet transform is proposed in this paper. The multi-thresholding function is a modified soft-thresholding to each node according to the discriminated threshold so as to reject かon external noise and white gaussian noise. It is verified by numerical simulation. And the experimental results are confirmed through sea-trial using multi-single sensors.

• Journal of the HCI Society of Korea
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• v.1 no.2
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• pp.27-33
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• 2006

This paper presents a new approach to translation and rotation invariant feature extraction for image texture retrieval. For the rotation invariant feature extraction, we invent angular projection along angular frequency in Polar coordinate system. The translation and rotation invariant feature vector for representing texture images is constructed by the averaged magnitude and the standard deviations of the magnitude of the Fourier transform spectrum obtained by the proposed angular projection. In order to easily implement the angular projection, the Radon transform is employed to obtain the Fourier transform spectrum of images in the Polar coordinate system. Then, angular projection is applied to extract the feature vector. We present our experimental results to show the robustness against the image rotation and the discriminatory capability for different texture images using MPEG-7 data set. Our Experiment result shows that the proposed rotation and translation invariant feature vector is effective in retrieval performance for the texture images with homogeneity, isotropy and local directionality.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.88-100
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• 2000

The design of an automatic aircraft recognition system involves two parts. The first part is extraction of invariant features independent of scale, rotation and translation. The second part is determination of optimal decision procedures, which are needed in the classification process. In this research, we extracted invariant aircraft features regardless of size, rotation and translation using Fourier Descriptors and Zernike Moments and classified using neural networks.

• Journal of the Korean Geophysical Society
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• v.9 no.2
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• pp.99-103
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• 2006

3D object recognition independent of translation and rotation using an ultrasonic sensor array, invariant moment vectors and SOFM(Self Organizing Feature Map) neural networks is presented. Using invariant moment vectors of the acquired 16×8 pixel data of square, rectangular, cylindric and regular triangular blocks, 3D objects could be classified by SOFM neural networks. Invariant moment vectors are constant independent of translation and rotation. The recognition rates for the training and testing data were 95.91% and 92.13%, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.4 no.3
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• pp.21-26
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• 2003

A new method to realize 3-dimensional object pattern recognition system using Fourier-based feature extractor has been proposed. The procedure to obtain the invariant feature vector is as follows ; A closed surface is generated by tracing the surface of object using the 3-dimensional polar coordinate. The centroidal distances between object's geometrical center and each closed surface points are calculated. The distance vector is translation invariant. The distance vector is normalized, so the result is scale invariant. The Fourier spectrum of each normalized distance vector is calculated, and the spectrum is rotation invariant. The Fourier-based feature generating from above procedure completely eliminates the effect of variations in translation, scale, and rotation of 3-dimensional object with closed-surface. The experimental results show that the proposed method has a high accuracy.

• Honam Mathematical Journal
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• v.26 no.2
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• pp.137-145
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• 2004

we shall study some properties of a new class ($\sqrt[\kappa]{P}$) (defined below). Also we show that T may not be normaloid when $T{\in}(\sqrt[\kappa]{P})$, and that the class ($\sqrt{H}$) may not have the translation-invariant propety.

• Bulletin of the Korean Mathematical Society
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• v.33 no.4
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• pp.545-551
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• 1996

It is well known in the theory of distributions and proved in [GS, S] that $$ (i) (Bochner-Schwartz) Every positive definite (tempered) distribution is the Fourier transform of a positive tempered measure \mu. $$ $$ (ii) (Schwartz kernel theorem) Let B(\varphi, \psi) be a bilinear distribution. Then for some u \in D'(R^n \times R^n) B(\varphi, \psi) = u(\varphi(x)\bar{\psi}(y)) for every \varphi, \psi \in C_c^\infty. $$ $$ (iii) A translation invariant positive definite bilinear distribution B(\varphi, \psi) is of the form B(\varphi, \psi) = \smallint \varphi(x)\psi(x) d\mu(x) for every \varphi, \psi \in C_c^\infty (R^n), where \mu is a positive tempered measure.