• ETRI Journal
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• v.34 no.5
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• pp.795-798
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• 2012

This letter proposes a new low-complexity motion estimation method. The proposed method classifies various nonmatching pixel pairs into several categories and assigns an appropriate weight for each category in the matching stage. As a result, it can significantly improve performance compared to that of the conventional methods by adding only one 1-bit addition and two Boolean operations per pixel.

• Korean Journal of Mathematics
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• v.30 no.3
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• pp.459-465
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• 2022

We exhibit various properties of the weighted Berezin operator T_α and its iteration T^k_α on L^p(𝜏), where α > -1 and 𝜏 is the invariant measure on the complex unit ball B_n. Iterations of T_α on L¹_R(𝜏) the space of radial integrable functions have performed important roles in proving 𝓜-harmonicity of bounded functions with invariant mean value property. We show differences between the case of 1 < p < ∞ and p = 1, ∞ under the infinite iteration of T_α or the infinite summation of iterations, most of which are extensions or related assertions to the propositions of the previous results.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1615-1623
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• 2008

This paper proposes methods to classify scanned insect footprints. We propose improved SOM and ART2 algorithms for extracting segments, basic areas for feature extraction, and utilize Trace transform and fuzzy weighted mean methods for extracting feature values for classification of the footprints. In the proposed method, regions are extracted by a morphological method in the beginning, and then improved SOM and ART2 algorithms are utilized to extract segments regardless of kinds of insects. Next, A Trace transform method is used to find feature values suitable for various kinds of deformation of insect footprints. In the Trace transform method, Triple features from reconstructed combination of diverse functions, are used to classify the footprints. In general, it is very difficult to decide automatically whether the extracted footprint segment is meaningful for classification or not. So we use a fuzzy weighted mean method for not excluding uncertain footprint segments because the uncertain footprint segments may be possible candidates for classification. We present experimental results of footprint segment extraction and segment classification by the proposed methods.

• Journal of the Korean Professional Engineers Association
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• v.16 no.4
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• pp.15-19
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• 1983

Therefore the use of Hadamard transform in the image processing requires only the real number operations and results in the computational advantages. Recently, however, certain degradation aspects have been reported. In this paper we propose a WH matrixtwhich retains the main properties of Hadamard matrix. The actual improvement of the image transmission in the inner part of the picture has been demonstrated by the computer simulated image developments. The orthogonal transforms such as Hadamard transform offers a useful facility in the digital signal processing. As the size of the transmission block increases, however, the assignment of bits for each data must increase exponentially. Thus the SNR of the image tends to decline accordingly. As an attempt to increase the SNR, we propose the WH matrix whose elements are made of ${\pm}$1, ${\pm}$2, ${\pm}$3, ${\pm}$4, and the unitform is 8${\times}$8 matrix.

• Kyungpook Mathematical Journal
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• v.45 no.2
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• pp.255-272
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• 2005

This paper is concerned with studying the weighted $L^P$ boundedness of a class of maximal operators related to homogeneous singular integrals with rough kernels. We obtain appropriate weighted $L^P$ bounds for such maximal operators. Our results are extensions and improvements of the main theorems in [2] and [5].

• Bulletin of the Korean Mathematical Society
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• v.41 no.2
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• pp.307-317
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• 2004

In the setting of the half-plane of the complex plane, we show that for $r\geq0$, the dual space of the weighted bergman spaces B$^{1}{\gamma}$ is the Bloch space of the half-plane and we study some bounded linear operators induced by radial derivatives.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.3
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• pp.240-248
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• 2013

In this paper, we propose a super-resolution algorithm using an adaptive weighted interpolation(AWI) and discrete wavelet transform(DWT). In general, super-resolution algorithms for single-image, probability based operations have been used for searching high-frequency components. Consequently, the complexity of the algorithm is increased and it causes the increase of processing time. In the proposed algorithm, we first find high-frequency sub-bands by using DWT. Then we apply an AWI to the obtained high-frequency sub-bands to make them have the same size as the input image. Now, the interpolated high-frequency sub-bands and input image are properly combined and perform the inverse DWT. For the experiments, we use the down-sampled version of the original image($512{\times}512$) as a test image($256{\times}256$). Through experiment, we confirm the improved efficiency of the proposed algorithm comparing with interpolation algorithms and also save the processing time comparing with the probability based algorithms even with the similar performance.

• Journal of the Korean Professional Engineers Association
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• v.22 no.2
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• pp.19-25
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• 1989

Utilizing a cosine transform in image compression has several recognized performance benefits, resulting in the ability to attain large compression ratio with small quality loss. Also, various models incorporating Human Visual System (HVS) to Discrete Cosine Trans-form (DCT) scheme are considered. Using the exact frequency components of DCT basis function, the optimum modulation transfer function (MTF) is obtained analytically. The errors at a block boundary which is important factor in transform coder are criteria for error measurement. The HVS weight coding results in perceptually higher quality images compared with the unweighted scheme.

• Journal of Digital Convergence
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• v.14 no.4
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• pp.277-283
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• 2016

This study proposed new methods to classify normal and epileptic seizure signals from EEG signals using peaks extracted by wavelet transform(WT) and Hilbert transform(HT) based on a neural network with weighted fuzzy membership functions(NEWFM). This study has the following three steps for extracting inputs for NEWFM. In the first step, the WT was used to remove noise from EEG signals. In the second step, the HT was used to extract peaks from the wavelet coefficients. We also selected the peaks bigger than the average of peaks to extract big peaks. In the third step, statistical methods were used to extract 16 features used as inputs for NEWFM from peaks. The proposed methodology shows that accuracy, specificity, and sensitivity are 99.25%, 99.4%, 99% with 16 features, respectively. Improvement in feature selection method in view to enhancing the accuracy is planned as the future work for selecting good features from 16 features.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.169-180
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• 2007

This paper discusses a new unsupervised XML document clustering technique based on the function transform and FFT(Fast Fourier Transform). An XML document is transformed into a discrete function based on the hierarchical nesting structure of the elements. The discrete function is, then, transformed into vectors using FFT. The vectors of two documents are compared using a weighted Euclidean distance metric. If the comparison is lower than the pre specified threshold, the two documents are considered similar in the structure and are grouped into the same cluster. XML clustering can be useful for the storage and searching of XML documents. The experiments were conducted with 800 synthetic documents and also with 520 real documents. The experiments showed that the function transform and FFT are effective for the incremental and unsupervised clustering of XML documents similar in structure.