• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.177-183
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• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1657-1662
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• 2017

Facial expression recognition systems using video devices have emerged as an important component of natural human-machine interfaces which contribute to various practical applications such as security systems, behavioral science and clinical practices. In this work, we present a new method to analyze, represent and recognize human facial expressions using a sequence of facial images. Under our proposed facial expression recognition framework, the overall procedure includes: accurate face detection to remove background and noise effects from the raw image sequences and align each image using vertex mask generation. Furthermore, these features are reduced by principal component analysis. Finally, these augmented features are trained and tested using Hidden Markov Model (HMM). The experimental evaluation demonstrated the proposed approach over two public datasets such as Cohn-Kanade and AT&T datasets of facial expression videos that achieved expression recognition results as 96.75% and 96.92%. Besides, the recognition results show the superiority of the proposed approach over the state of the art methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.3
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• pp.307-316
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• 2016

Reversible data hiding is a technique to hide any data without affecting the original image. Zhang proposed the encryption of original image and a data hiding scheme in encrypted image. First, the encrypted image is decrypted and uses the fluctuation function which exploits the spatial correlation property of decrypted image to extract hidden data. In this paper, the new fluctuation function is proposed to reduce errors which arise from the process extracting hidden data and the performance is verified by simulation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.4 s.304
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• pp.39-48
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• 2005

This paper proposes novel texture segmentation method using Bayesian estimation method and neural networks. We use multi-scale wavelet coefficients and the context information of neighboring wavelets coefficients as the input of networks. The output of neural networks is modeled as a posterior probability. The context information is obtained by HMT(Hidden Markov Tree) model. This proposed segmentation method shows better performance than ML(Maximum Likelihood) segmentation using HMT model. And post-processed texture segmentation results as using multi-scale Bayesian image segmentation technique called HMTseg in each segmentation by HMT and the proposed method also show that the proposed method is superior to the method using HMT.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.31-37
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• 2004

Wavelet transform is a useful tool for analysis and process of image. This showed good performance in image compression and noise reduction. Wavelet coefficients can be effectively modeled by hidden Markov tree(HMT) model. However, in application of HMT model to image interpolation, training procedure is needed. Moreover, the parameters obtained from training procedure do not match input image well. In this paper, the structure of HMT is used for image interpolation, and the parameters of HMT are obtained from statistical characteristics across wavelet subbands without training procedure. In the proposed method, wavelet coefficient is modeled as Gaussian mixture model(GMM). In GMM, state transition probabilities are determined from statistical transition characteristic of coefficient across subbands, and the variance of each state is estimated using the property of exponential decay of wavelet coefficient. In simulation, the proposed method shows improvement of performance compared with conventional bicubic method and the method using HMT model with training.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.205-211
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• 2019

Steganalysis is a technique that aims to detect and recover data hidden by steganography. Steganalytic methods detect hidden data by analyzing visual and statistical distortions caused during data embedding. However, for recovering the hidden data, they need to know which steganographic methods the hidden data has been embedded by. Therefore, we propose a hierarchical convolutional neural network (CNN) structure that identifies a steganographic method applied to an input image through multi-level classification. We trained four base CNNs (each is a binary classifier that determines whether or not a steganographic method has been applied to an input image or which of two different steganographic methods has been applied to an input image) and connected them hierarchically. Experimental results demonstrate that the proposed hierarchical CNN structure can identify four different steganographic methods (LSB, PVD, WOW, and UNIWARD) with an accuracy of 79%.

• Korean Journal of Computational Design and Engineering
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• v.11 no.6
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• pp.455-463
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• 2006

Hidden line removal(HLR) algorithms can be devised either in the image space or in the object space. This paper describes a hidden line removal algorithm in the object space specifically for the CAD viewer data. The approach is based on the Appel's 'Quantitative Invisibility' algorithm and fundamental concept of 'back face culling'. Input data considered in this algorithm can be distinguished from those considered for HLR algorithm in general. The original QI algorithm can be applied for the polyhedron models. During preprocessing step of our proposed algorithm, the self intersecting surfaces in the view direction are divided along the silhouette curves so that the QI algorithm can be applied. By this way the algorithm can be used for any triangulated freeform surfaces. A major advantage of this algorithm is the applicability to general CAD models and surface-based visualization data.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.863-867
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• 2021

In this paper, in order to hide the watermark in the LSB of the color image, a color image watermarking technique with high security is proposed by using the adjacent pixels of the image and the spatial encryption technique. According to the technique proposed in this paper, the quality of the stego-image generated by hiding the watermark in the LSB of the color image is so excellent that the difference from the original image cannot be recognized, and the original watermark can be extracted from the stego-image without loss. If the watermark is hidden in the image using the proposed technique, the security of the watermark is maintained very high because the watermark hidden in the stego-image is multi-encrypted. The proposed watermarking technique can be used in applications such as military and intellectual property protection that require high security.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• Journal of Digital Contents Society
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• v.10 no.2
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• pp.291-298
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• 2009

In this paper we proposed a vision based realtime hand gesture recognition method. To extract skin color, we translate RGB color space into YCbCr color space and use CbCr color for the final extraction. To find the center of extracted hand region we apply practical center point extraction algorithm. We use Kalman filter to tracking hand region and use HMM(Hidden Markov Model) algorithm (learning 6 type of hand gesture image) to recognize it. We demonstrated the effectiveness of our algorithm by some experiments.