• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.5
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• pp.155-161
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• 2009

This paper proposes a high capacity data hiding method using modulus function of pixel-value differencing (PVD) and least significant bit (LSB) replacement method. Many novel data hiding methods based on LSB and PVD methods were presented to enlarge hiding capacity and provide an imperceptible quality. A small difference value for two consecutive pixels is belonged to a smooth area and a large difference one is located on an edge area. In our proposed method, the secret data are hidden on the smooth area by the LSB substitution method and PVD method on the edge area. From the experimental results, the proposed method sustains a higher capacity and still a good quality compared with other LSB and modified PVD methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.13-19
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• 2014

In general, if a secret of company is owned by a person, the secret is the most vulnerable to attack of hacking. Secret sharing is a solution to solve such a problem. To share the secret to many people not one, it is possible to restore secret when the secret is being stolen by someone. That is, secret sharing, a strong method, was proposed to keep secret information from the robbery. Until now, most secret sharing schemes were based on spatial domain. The hidden data based on spatial domain is easily deleted since a transformation of digital formats (i.e., jpeg to bmp or vise versa). In this paper, we proposed our scheme for complement to resist various attack of cover image as distributing secrets based on DCT of JPEG using exclusive-or operation. The result of experiments proved that the proposed scheme restore original secret.

• Smart Structures and Systems
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• v.20 no.4
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• pp.397-413
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• 2017

Precast concrete (PC) members are currently being employed for general construction or partial replacement to reduce construction period. As assembly work in PC construction requires connecting PC members accurately, measuring the 6-DOF (degree of freedom) relative displacement is essential. Multiple planar markers and camera-based displacement measurement systems can monitor the 6-DOF relative displacement of PC members. Conventional methods, such as direct linear transformation (DLT) for homography estimation, which are applied to calculate the 6-DOF relative displacement between the camera and marker, have several major problems. One of the problems is that when the marker is partially hidden, the DLT method cannot be applied to calculate the 6-DOF relative displacement. In addition, when the images of markers are blurred, error increases with the DLT method which is employed for its estimation. To solve these problems, a hybrid method, which combines the advantages of the DLT and MCL (Monte Carlo localization) methods, is proposed. The method evaluates the 6-DOF relative displacement more accurately compared to when either the DLT or MCL is used alone. Each subsystem captures an image of a marker and extracts its subpixel coordinates, and then the data are transferred to a main system via a wireless communication network. In the main system, the data from each subsystem are used for 3D visualization. Thereafter, the real-time movements of the PC members are displayed on a tablet PC. To prove the feasibility, the hybrid method is compared with the DLT method and MCL in real experiments.

• Journal of Korean Foot and Ankle Society
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• v.19 no.4
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• pp.156-160
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• 2015

Purpose: We compared plain radiographs with computed tomography (CT) images to evaluate the usefulness of preoperative CT in acute ankle malleolar fracture in terms of accuracy of diagnosis and planning of operative strategy. Materials and Methods: A retrospective analysis was conducted on 210 cases of malleolar fracture treated at our institute for which plain radiograph and CT were obtained preoperatively. Observers had reviewed plain radiographs and recorded fracture classification, anatomical diagnosis, extent and configuration of fractures and then subsequently reviewed CT images. Records from each image were compared and information regarding the differences in fractures was assessed. Results: Fractures were notably changed in appearance in 88 cases (41.9%) and diagnosis changed in 30 cases (14.3%). According to the change of diagnosis and fracture appearances, the operative strategy was changed in 15 cases (7.1%) including incision, order of reduction, and target of fixation. Conclusion: CT could be a useful adjunctive imaging tool in addition to the plain radiograph in planning of operative treatment for acute malleolar fracture in terms of estimating exact configuration, extent of fractures and even newly revealed hidden fractures.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.244-249
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• 2017

Steganalysis is to detect information hidden by steganography inside general data such as images. There are stegoanalysis techniques that use machine learning (ML). Existing ML approaches to steganalysis are based on extracting features from stego images and modeling them. Recently deep learning-based methodologies have shown significant improvements in detection accuracy. However, all the existing methods, including deep learning-based ones, have a critical limitation in that they can only detect stego images that are created by a specific steganography method. In this paper, we propose a generalized steganalysis method that can model multiple types of stego images using deep learning. Through various experiments, we confirm the effectiveness of our approach and envision directions for future research. In particular, we show that our method can detect each type of steganography with the same level of accuracy as that of a steganalysis method dedicated to that type of steganography, thereby demonstrating the general applicability of our approach to multiple types of stego images.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.117-140
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• 2013

Hindi is the most widely spoken language in India, with more than 300 million speakers. As there is no separation between the characters of texts written in Hindi as there is in English, the Optical Character Recognition (OCR) systems developed for the Hindi language carry a very poor recognition rate. In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. One of the major reasons for the poor recognition rate is error in character segmentation. The presence of touching characters in the scanned documents further complicates the segmentation process, creating a major problem when designing an effective character segmentation technique. Preprocessing, character segmentation, feature extraction, and finally, classification and recognition are the major steps which are followed by a general OCR. The preprocessing tasks considered in the paper are conversion of gray scaled images to binary images, image rectification, and segmentation of the document's textual contents into paragraphs, lines, words, and then at the level of basic symbols. The basic symbols, obtained as the fundamental unit from the segmentation process, are recognized by the neural classifier. In this work, three feature extraction techniques-: histogram of projection based on mean distance, histogram of projection based on pixel value, and vertical zero crossing, have been used to improve the rate of recognition. These feature extraction techniques are powerful enough to extract features of even distorted characters/symbols. For development of the neural classifier, a back-propagation neural network with two hidden layers is used. The classifier is trained and tested for printed Hindi texts. A performance of approximately 90% correct recognition rate is achieved.

• Journal of the Korean Society of Costume
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• v.64 no.7
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• pp.57-67
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• 2014

This study aims to provide visual data from analysis of the Depaysement approaches with new viewpoints to inspire and develop new fashion window design ideas. The literature and existing researches related to Depaysement were analyzed for theoretical review, and Depaysement expression approaches were identified by expression characteristics. Theme concepts using traditional Korean images, which could be applied to fashion window displays in Korea, were established, and K(Korean)-fashion design was created to develop fashion window display design. Then, the Depaysement fashion window display was executed using Adobe Illustrator and Photoshop. The results of this study are summarized below. 'Change of forms and materials' could visualize the factors inducing curiosity, which can directly stimulate the consumption sentiment lying latent in the mind of observers by assigning new values to fashion goods displayed inside windows. Unconscious experience and remarkable stories, which are not possible to encounter in an everyday setting, can be visualized through the window display in 'heterogeneous combination of objects.' 'The location change of an object' could express the refreshing and shocking scene to give weird anxiety and mental contradiction to observers by fashion window display, which could break fixed idea of human beings. 'The change of object awareness' could express contradiction and denial, which could liberate the unconsciousness lying latent inside observers through fashion window display. 'Change of spatial awareness' could create the design which maximized the fashion images of goods displayed by helping the observers to change the space of their unconsciousness selectively at their will through the fashion window display with hidden, strange, ambiguous and variable image like a riddle.

• Journal of KIISE:Software and Applications
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• v.35 no.4
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• pp.265-279
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• 2008

The idea of using hand gestures for human-computer interaction is not new and has been studied intensively during the last dorado with a significant amount of qualitative progress that, however, has been short of our expectations. This paper describes a dynamic Bayesian network or DBN based approach to both two-hand gestures and one-hand gestures. Unlike wired glove-based approaches, the success of camera-based methods depends greatly on the image processing and feature extraction results. So the proposed method of DBN-based inference is preceded by fail-safe steps of skin extraction and modeling, and motion tracking. Then a new gesture recognition model for a set of both one-hand and two-hand gestures is proposed based on the dynamic Bayesian network framework which makes it easy to represent the relationship among features and incorporate new information to a model. In an experiment with ten isolated gestures, we obtained the recognition rate upwards of 99.59% with cross validation. The proposed model and the related approach are believed to have a strong potential for successful applications to other related problems such as sign languages.

• The Journal of the Korea Contents Association
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• v.10 no.8
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• pp.11-17
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• 2010

In this paper, we propose HMM-based upper-body gesture. First, to recognize gesture of space, division about pose that is composing gesture once should be put priority. In order to divide poses which using interface, we used two IR cameras established on front side and side. So we can divide and acquire in front side pose and side pose about one pose in each IR camera. We divided the acquired IR pose image using SVM's non-linear RBF kernel function. If we use RBF kernel, we can divide misclassification between non-linear classification poses. Like this, sequences of divided poses is recognized by gesture using HMM's state transition matrix. The recognized gesture can apply to existent application to do mapping to OS Value.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.155-164
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• 2004

Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user and is able to doface recognition, which is vital for many surveillance-based systems. The advantage of face recognition compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to the decreasing in dimension from image acquisition step and various changes associated with face pose and background. There are many factors that deteriorate performance of face recognition such as thedistance from camera to the face, changes in lighting, pose change, and change of facial expression. In this paper, we implement a new sliding active camera system to prevent various pose variation that influence face recognition performance andacquired frontal face images using PCA and HMM method to improve the face recognition. This proposed face recognition algorithm can be used for intelligent surveillance system and mobile robot system.