• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.32-40
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• 2011

Reversible data hiding, which can recover the original image without any distortion after the extraction of the hidden data, has drawn considerable attention in recent years. However, underflow and overflow problems have occurred occasionally in the embedded image. To overcome these problems, we propose a new reversible data hiding algorithm which embeds a compressed location map used to identify these underflow and overflow points. In addition, the proposed algorithm allows for multilevel data hiding to increase the hiding capacity. The simulation results demonstrate that the proposed algorithm generates good performances in the PSNR, the embedding capacity, and the size of side information.

• Journal of the Institute of Convergence Signal Processing
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• v.15 no.2
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• pp.24-29
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• 2014

Digital mammography is the most common technique for the early detection of breast cancer. To diagnose the breast cancer in early stages and treat efficiently, many image enhancement methods have been developed. This paper presents a multi-scale contrast enhancement method in the Laplacian pyramid for the digital mammogram. The proposed method decomposes the image into the contrast measures by the Gaussian and Laplacian pyramid, and the pyramid coefficients of decomposed multi-resolution image are defined as the frequency limited local contrast measures by the ratio of high frequency components and low frequency components. The decomposed pyramid coefficients are modified by the contrast measure for enhancing the contrast, and the final enhanced image is obtained by the composition process of the pyramid using the modified coefficients. The proposed method is compared with other existing methods, and demonstrated to have quantitatively good performance in the contrast measure algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.4
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• pp.1248-1266
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• 2012

This paper presents a multilevel reversible data hiding method based on histogram shifting which can recover the original image losslessly after the hidden data has been extracted from the stego-image. The method of prediction is adopted in our proposed scheme and prediction errors are produced to explore the similarity of neighboring pixels. In this article, we propose two different predictors to generate the prediction errors, where the prediction is carried out using the center prediction method and the JPEG-LS median edge predictor (MED) to exploit the correlation among the neighboring pixels. Instead of the original image, these prediction errors are used to hide the secret information. Moreover, we also present an improved method to search for peak and zero pairs and also talk about the analogy of the same to improve the histogram shifting method for huge embedding capacity and high peak signal-to-noise ratio (PSNR). In the one-level hiding, our method keeps image qualities larger than 53 dB and the ratio of embedding capacity has 0.43 bpp (bit per pixel). Besides, the concept with multiple layer embedding procedure is applied for obtaining high capacity, and the performance is demonstrated in the experimental results. From our experimental results and analytical reasoning, it shows that the proposed scheme has higher PSNR and high data embedding capacity than that of other reversible data hiding methods presented in the literature.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.822-823
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• 2014

The usual method of generating an image for a multiview display system requires acquiring a color image and depth information of a reference camera. Then, intermediate images, generated using DIBR method, will be captured at a number of different viewpoints and composed to construct an multiview image. When such intermediate views are generated, several holes would be shown because some hidden parts are shown when the screenshot is taken at different angle. Previous research tried to solve this problem by creating a new hole-filling algorithm or enhancing the depth information. This paper describes a new method of enhancing the intermediate view images by applying the Ball Pivoting algorithm, which constructs meshes from a point cloud. When the new method is applied to the Microsoft's "Ballet" and "Break Dancer" data sets, PSNR comparison shows that about 0.18~1.19 increasement. This paper will explaing the new algorithm and the experiment method and results.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.33-40
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• 2022

With the recent increase in the development of military drones, they are adopted and used as the combat system of battalion level or higher. However, it is difficult to use drones that can be used in battles below the platoon level due to the current conditions for the formation of units in the Korean military. In this paper, therefore, we developed a program drones equipped with a thermal imaging camera and LiDAR sensor for reconnaissance and exploration that can be applied in battles below the platoon level. Using these drones, we studied the possibility and feasibility of drones for small-scale combats that can find hidden enemies, search for an appropriate detour through image processing and conduct reconnaissance and search for battlefields, hiding and cover-up through image processing. In addition to the purpose of using the proposed drone to search for an enemies lying in ambush in the battlefield, it can be used as a function to check the optimal movement path when a combat unit is moving, or as a function to check the optimal place for cover-up or hiding. In particular, it is possible to check another route other than the route recommended by the program because the features of the terrain can be checked from various viewpoints through 3D modeling. We verified the possiblity of flying by designing and assembling in a form of adding LiDAR and thermal imaging camera module to a drone assembled based on racing drone parts, which are open source hardware, and developed autonomous flight and search functions which can be used even by non-professional drone operators based on open source software, and then installed them to verify their feasibility.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.69-75
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• 2023

In recent years, there has been a growing interest in deep learning-based animal pose estimation, especially in the areas of animal behavior analysis and healthcare. However, existing animal pose estimation techniques do not perform well when body parts are occluded or not present. In particular, the occlusion of dog tail or ear might lead to a significant degradation of performance in pet behavior and emotion recognition. In this paper, to solve this intractable problem, we propose a simple yet novel framework for pet pose estimation where pet pose is predicted on an outpainted image where some body parts hidden outside the input image are reconstructed by the image inpainting network preceding the pose estimation network, and we performed a preliminary study to test the feasibility of the proposed approach. We assessed CE-GAN and BAT-Fill for image outpainting, and evaluated SimpleBaseline for pet pose estimation. Our experimental results show that pet pose estimation on outpainted images generated using BAT-Fill outperforms the existing methods of pose estimation on outpainting-less input image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.3
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• pp.8-14
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• 2009

A Hidden Markov Model(HMM)-based information extraction method is proposed in this paper. The proposed extraction method is applied to extraction of products' prices. The input of the proposed IESHMM is the URLs of a search engine's interface, which contains the names of the product types. The output of the system is the list of extracted slots of each product: name, price, image, and URL. With the observation data set Maximum Likelihood algorithm and Baum-Welch algorithm are used for the training of HMM and The Viterbi algorithm is then applied to find the state sequence of the maximal probability that matches the observation block sequence. When applied to practical problems, the proposed HMM-based system shows improved results over a conventional method, PEWEB, in terms of recall ration and accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2121-2128
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• 2012

This paper proposed the real time face area detection using Wavelet transform and the strong detection algorithm that satisfies the efficiency of computation and detection performance at the same time was proposed. The detected face image recognizes the face by configuring the low-dimensional face symbol through the principal component analysis. The proposed method is well suited for real-time system construction because it doesn't require a lot of computation compared to the existing geometric feature-based method or appearance-based method and it can maintain high recognition rate using the minimum amount of information. In addition, in order to reduce the wrong recognition or recognition error occurred during face recognition, the input symbol of Hidden Markov Model is used by configuring the feature values projected to the unique space as a certain symbol through clustering algorithm. By doing so, any input face will be recognized as a face model that has the highest probability. As a result of experiment, when comparing the existing method Euclidean and Mahananobis, the proposed method showed superior recognition performance in incorrect matching or matching error.

• Journal of Korea Multimedia Society
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• v.21 no.4
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• pp.450-463
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• 2018

Sound based machine fault diagnosis is the process consisting of detecting automatically the damages that affect the machines by analyzing the sounds they produce during their operating time. The collected sounds being inevitably corrupted by random disturbance, the most important part of the diagnosis consists of discovering the hidden elements inside the data that can reveal the faulty patterns. This paper presents a novel feature extraction methodology that combines various digital signal processing and pattern recognition methods for the analysis of the sounds produced by the drills. Using the Fourier analysis, the magnitude spectrum of the sounds are extracted, converted into two-dimensional vectors and uniformly normalized in such a way that they can be represented as 8-bit grayscale images. Histogram equalization is then performed over the obtained images in order to adjust their very poor contrast. The obtained contrast enhanced images will be used as the features of our diagnosis system. Finally, principal component analysis is performed over the image features for reducing their dimensions and a nonlinear classifier is adopted to produce the final response. Unlike the conventional features, the results demonstrate that the proposed feature extraction method manages to capture the hidden health patterns of the sound.

• Journal of KIISE
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• v.45 no.1
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• pp.22-29
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• 2018

Over the decades, neural networks have been successfully used in numerous applications from speech recognition to image classification. However, these neural networks cannot explain their results and one needs to know how and why a specific conclusion was drawn. Most studies focus on extracting binary rules from neural networks, which is often impractical to do, since data sets used for machine learning applications contain continuous values. To fill the gap, this paper presents an algorithm to extract logic rules from a trained neural network for data with continuous attributes. It uses hyperplane-based linear classifiers to extract rules with numeric values from trained weights between input and hidden layers and then combines these classifiers with binary rules learned from hidden and output layers to form non-linear classification rules. Experiments with different datasets show that the proposed approach can accurately extract logical rules for data with nonlinear continuous attributes.