• Journal of Korea Multimedia Society
• /
• v.6 no.6
• /
• pp.1008-1014
• /
• 2003

In this paper, we propose the image retrieval method based on object regions using bidirectional round filter in the wavelet transform domain. A conventional method that includes unnecessary background information reduce retrieval efficiency, because of the extraction of feature vectors from the whole region of subband. On proposed method, it extracts accurate feature vectors and keep certainly retrieval efficiency in case of reduced feature vectors, because of the extraction of feature vectors from the only extracted object region. Furthermore, it improve retrieval efficiency by removing unnecessary background information. Consequently, the retrieval efficiency is improved with 2.5%∼5.5% values, which have a little chances to vary according to characteristics of image.

• Journal of Korea Multimedia Society
• /
• v.7 no.1
• /
• pp.98-105
• /
• 2004

In this paper, we proposed a watermarking algorithm for embedding visually recognizable pattern into the middle-frequency part:; of image. Binary image as watermark is embedded in the LH2, HL2 and HH2 band of wavelet transformed domain for copyright protection of image data. To evaluate robustness of the proposed method, we applied some basic algorithm of image processing such as scaling, filtering, cropping, histogram equalizing and lossy compression(JPEG, gif). As a result of experiment, the proposed method has the good image quality and the high perceptibility of watermark. It was demonstrated by experiments that the proposed algorithm can provide an excellent protection under various attacks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2003.10a
• /
• pp.691-694
• /
• 2003

This paper proposes an 3D face image watermarking method based on discrete wavelet transform(DWT). First, 3D face image are transformed by DWT and inserted gaussian watermark into frequency domain. To increase the robustness and perceptual invisibility of watermark, the proposed algorithm is combined with the characteristics of 3D face image and human visual system. The proposed method is invisible and blind watermarking which the original image is not required. Simulation results show that the proposed method is robust to the general attack such as JPEG compression, enhancement, noise, cropping, and filtering etc.

• The Journal of the Korea Contents Association
• /
• v.4 no.3
• /
• pp.107-113
• /
• 2004

This paper improved the performance of the face recognition system using the PCA/LDA hybrid method based on the facial geometric feature and the Wavelet transform. Because the previous PCA/LDA methods have measured the similarity according to the formal dispersion, they could not reflect facial boundaries exactly In order to recover this defect, this paper proposed the method using the distance between eyes and mouth. If the difference of the measured distances on the query and the training images is over the given threshold, then the method reorders the candidate images according to energy feature vectors of eyes, a nose, and a chin. To evaluate the performance of the proposed method the computer simulations have been performed with four hundred facial images in the ORL database. The results showed that our method improves about 4％ recognition rate over the previous PCA/LDA method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.1C
• /
• pp.68-78
• /
• 2008

The transmission of multimedia information over error-prone channels such as wireless networks has become an important area of research. In this paper, we propose two Error Concealment(EC) schemes for wireless transmission of JPEG2000 image. The Multiple Representation(MR) is employed as the preprocessing in our schemes, whereas the main error concealing operation is applied in wavelet domain at receiver side. The compressed code-stream of several subsampled versions of original image is transmitted over a single channel with random bit errors. In the decoder side, the correctly reconstructed wavelet coefficients are utilized to recover the corrupted coefficients in other sub-images. The recovery is carried out by proposed basic(MREC-BS) or enhanced(MREC-ES) methods, both of which can be simply implemented. Moreover, there is no iterative processing during error concealing, which results a big time saving. Also, the simulation results confirm the effectiveness and efficiency of our proposed schemes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• v.9 no.1
• /
• pp.1165-1168
• /
• 2005

This paper proposes an digital image watermarking method based on discrete wavelet transform(DWT) and spatial perceptual properties of human visula system. first, an digital image is transformed by DWT and inserted gaussian watermark into the frequency domain. To increase the robustness and perceptual invisibility of watermark, the proposed algorithm is combined with the characteristics of digital image and human visual system. The proposed method is invisible and blind watermarking which the original image is not required to detect watermarks. Simulation results show that the proposed method is robust to the general attack such as JPEG compression, enhancement, noise, cropping, and filtering etc.

• Journal of Electrical Engineering and Technology
• /
• v.11 no.4
• /
• pp.951-960
• /
• 2016

Tower insulators in electric power transmission network play a crucial role in preserving the reliability of the system. Electrical utilities frequently face the problem of flashover of insulators due to pollution deposition on their surface. Several research works based on leakage current (LC) measurement has been already carried out in developing diagnostic techniques for these insulators. Since the LC signal is highly intermittent in nature, estimation of pollution severity based on LC signal measurement over a short period of time will not produce accurate results. Reports on the measurement and analysis of LC signals over a long period of time is scanty. This paper attempts to use Random Forest (RF) classifier, which produces accurate results on large data bases, to analyze the pollution severity of high voltage tower insulators. Leakage current characteristics over a long period of time were measured in the laboratory on porcelain insulator. Pollution experiments were conducted at 11 kV AC voltage. Time domain analysis and wavelet transform technique were used to extract both basic features and histogram features of the LC signal. RF model was trained and tested with a variety of LC signals measured over a lengthy period of time and it is noticed that the proposed RF model based pollution severity classifier is efficient and will be helpful to electrical utilities for real time implementation.

• Journal of the Society of Naval Architects of Korea
• /
• v.53 no.6
• /
• pp.482-493
• /
• 2016

The identification of modal damping of a segmented hull model with torsional response is difficult task due to the coupling of modal response. This is because the 1st and 2nd torsional vibration modes are closely spaced in frequency domain leading to the situation that the modal decomposition is difficult to achieve by simple band-pass filter. Present study applied several different modal decomposition methods to derive the damping ratio of different modes. The modal decomposition methods considered in this study are simple band-pass filter, Hilbert vibration decomposition, Wavelet transform and proper orthogonal decomposition. Coupled free decay signal obtained from the torsional hammering test on a segmented hull model was processed with four different methods and the derived damping ratios were compared with each other. Discussions also have been made on the pros and cons of the different methodologies.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12spc
• /
• pp.556-564
• /
• 2021

Numerous spatial and transform-domain-based conventional denoising algorithms struggle to keep critical and minute structural features of the image, especially at high noise levels. Although neural network approaches are effective, they are not always reliable since they demand a large quantity of training data, are computationally complicated, and take a long time to construct the model. A new framework of enhanced hybrid filtering is developed for denoising color images tainted by additive white Gaussian Noise with the goal of reducing algorithmic complexity and improving performance. In the first stage of the proposed approach, the noisy image is refined using a high-dimensional non-local means filter based on Principal Component Analysis, followed by the extraction of the method noise. The wavelet transform and SURE Shrink techniques are used to further culture this method noise. The final denoised image is created by combining the results of these two steps. Experiments were carried out on a set of standard color images corrupted by Gaussian noise with multiple standard deviations. Comparative analysis of empirical outcome indicates that the proposed method outperforms leading-edge denoising strategies in terms of consistency and performance while maintaining the visual quality. This algorithm ensures homogeneous noise reduction, which is almost independent of noise variations. The power of both the spatial and transform domains is harnessed in this multi realm consolidation technique. Rather than processing individual colors, it works directly on the multispectral image. Uses minimal resources and produces superior quality output in the optimal execution time.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.7
• /
• pp.127-132
• /
• 2021

This paper applies a machine learning model to forecasting freight rates in dry bulk and tanker markets with wavelet decomposition and empirical mode decomposition because they can refect both information scattered in the time and frequency domain. The decomposition with wavelet is outperformed for the dry bulk market, and EMD is the more proper model in the tanker market. This result provides market players with a practical short-term forecasting method. This study contributes to expanding a variety of predictive methodologies for one of the highly volatile markets. Furthermore, the proposed model is expected to improve the quality of decision-making in spot freight trading, which is the most frequent transaction in the shipping industry.