• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.664-672
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• 2004

Widespread popularity of wireless data communication devices, coupled with the availability of higher bandwidths, has led to an increased user demand for content-rich media such as images and videos. Since such content often tends to be private, sensitive, or paid for, there exists a requirement for securing such communication. However, solutions that rely only on traditional compute-intensive security mechanisms are unsuitable for resource-constrained wireless and embedded devices. In this paper, we propose a selective partial image encryption scheme for image data hiding , which enables highly efficient secure communication of image data to and from resource constrained wireless devices. The encryption scheme is invoked during the image compression process, with the encryption being performed between the quantizer and the entropy coder stages. Three data selection schemes are proposed： subband selection, data bit selection and random selection. We show that these schemes make secure communication of images feasible for constrained embed-ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of ded devices. In addition we demonstrate how these schemes can be dynamically configured to trade-off the amount of data hiding achieved with the computation requirements imposed on the wireless devices. Experiments conducted on over 500 test images reveal that, by using our techniques, the fraction of data to be encrypted with our scheme varies between 0.0244％ and 0.39％ of the original image size. The peak signal to noise ratios (PSNR) of the encrypted image were observed to vary between about 9.5㏈ to 7.5㏈. In addition, visual test indicate that our schemes are capable of providing a high degree of data hiding with much lower computational costs.

• 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.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.161-170
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• 2017

Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

• The Journal of the Acoustical Society of Korea
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• v.21 no.1
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• pp.19-26
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• 2002

This paper defines the visual basic speech units, visemes and investigates various visual features of a lip for the effective Korean lipreading. First, we analyzed the visual characteristics of the Korean vowels from the database of the lip image sequences obtained from the multi-speakers, thereby giving a definition of seven Korean vowel visemes. Various spatio-temporal features of a lip are extracted from the feature points located on both inner and outer lip contours of image sequences and their classification performances are evaluated by using a hidden Markov model based classifier for effective lipreading. The experimental results for recognizing the Korean visemes have demonstrated that the feature victor containing the information of inner and outer lip contours can be effectively applied to lipreading and also the direction and magnitude of the movement of a lip feature point over time is quite useful for Korean lipreading.

• 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 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.

• The Journal of the Acoustical Society of Korea
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• v.34 no.6
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• pp.487-492
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• 2015

A short-range underwater target detection and identification techniques using mid- and high-frequency bands have been highly developed. However, nowadays the long-range detection using the low-frequency band is requested and one of the most challengeable issues. The waveform inversion technique is widely used and the hottest technology in both academia and industry of the seismic exploration. It is based on the numerical analysis tool, and could construct more than a few kilometers of the subsurface structures and model-parameters such as P-wave velocity using a low-frequency band. By applying this technique to the underwater acoustic circumstance, firstly application of underwater target detection is verified. Furthermore, subsurface structures and it's parameters of the war-field are well reconstructed. We can confirm that this technique greatly reduces the false-alarm rate for the underwater targets because it could accurately reproduce both the shape and the model-parameters at the same time.

• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.643-648
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• 2007

유비쿼터스 컴퓨팅 기술이 문화예술 분야에 접목되면서 수동적이었던 전시 관람 형태가 능동적인 관람 형태로 바뀌고 있다. 특히, 지능형 가이드 시스템의 등장은 기존의 관람 문화를 크게 변화시켰다. 지능형 가이드 시스템이란 사용자에게 전시물에 대한 정보 및 전시장의 위치 정보를 제공해주는 시스템을 말한다. 현재 상용화되고 있는 지능형 가이드 시스템은 크게 휴대폰, PDA, 게임기 등의 휴대형 장치 기반의 가이드 시스템과 HMD와 같은 착용형 장치 기반의 가이드 시스템으로 나뉠 수 있다. 본 논문에서는 이러한 현재 상용화된 시스템들의 한계(예를 들어, 특정 장치를 직접 착용 혹은 소지해야 함)를 서술하고, 이를 보완하는 프로젝터 기반의 가이드 시스템에서 더 나아가 임의의 공간에 원하는 전시물 구성, 설치 등을 신속, 정확하게 수행하는 지능형 가이드 시스템을 제안한다. 프로젝터 기반의 지능형 가이드 시스템은 기반 기술로 지능형 프로젝션 기술을 필요로 하는데, 이는 임의의 환경에서 임의의 위치에 다수의 사용자에게 고화질, 대화면 영상 정보를 제공해 준다. 그러나, 기존의 지능형 프로젝션 기술은 성능 및 안정성을 위해 대부분 가시적인 패턴 및 마커를 사용하는데, 이는 사용자에게 제공되는 정보를 관찰하는 데 방해가 될 수 있다. 본 논문에서는 사용자의 관점에서 유용한 비간섭 지능형 프로젝션 기술을 사용한다. 즉, 본 논문에서는 마커나 패턴을 사용함으로써 정확성이나 안정성은 보장하지만, 마커나 패턴을 은닉하여 사용자의 눈에 띄지 않도록 함으로써, 사용자는 원하는 정보를 아무런 방해 없이 제공받을 수 있다. 제안된 시스템을 미술 작품 감상을 위한 가이드 시스템으로 적용해 본 결과, 사용자는 자유로운 환경에서 자신의 위치나 작품에 대한 설명을 대화면으로 제공받으면서, 편안하게 그림을 감상할 수 있었다.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.938-949
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• 2015

In this paper, we propose a robust speed-limit sign recognition system which is durable to any sign changes caused by exterior damage or color contrast due to light direction. For recognition of speed-limit sign, we apply CNN which is showing an outstanding performance in pattern recognition field. However, original CNN uses multiple hidden layers to extract features and uses fully-connected method with MLP(Multi-layer perceptron) on the result. Therefore, the major demerit of conventional CNN is to require a long time for training and testing. In this paper, we apply randomly-connected classifier instead of fully-connected classifier by combining random forest with output of 2 layers of CNN. We prove that the recognition results of CNN with random forest show best performance than recognition results of CNN with SVM (Support Vector Machine) or MLP classifier when we use eight speed-limit signs of GTSRB (German Traffic Sign Recognition Benchmark).