• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.17-25
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• 2022

Hash functions are widely used for fast equality analysis of video files because of their fixed small output sizes regardless of their input sizes. However, the hash function has the possibility of a hash collision in which different inputs derive the same output value, so there is a problem that different video files may be mistaken for the same file. In this paper, we propose an equality analysis scheme in which different video files always derive different output values using identifier information and double hash. The scheme first extracts the identifier information of an original video file, and attaches it into the end of the original file with a steganography method. Next the scheme calculates two hash output values of the original file and the extended file with attached identifier information. Finally the scheme utilizes the identifier information, the hash output value of the original file, and the hash output value of the extended file for the equality analysis of video files. For evaluation, we implement the proposed scheme into a practical software tool and show that the proposed scheme performs well the equality analysis of video files without hash collision problem and increases the resistance against the malicious hash collision attack.

• Journal of Broadcast Engineering
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• v.22 no.5
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• pp.643-651
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• 2017

Steganography is one of information hiding technologies and discriminated from cryptography in that it focuses on avoiding the existence the hidden information from being detected by third parties, rather than protecting it from being decoded. In this paper, as an image steganography method which uses images as media, we propose a new block matching method that embeds information into the discrete wavelet transform (DWT) domain. The proposed method, based on a statistical analysis, reduces loss of embedding capacity due to inequable use of candidate blocks. It works in such a way that computes the variance of each candidate block, preserves candidate blocks with high frequency components while reducing candidate blocks with low frequency components by compressing them exploiting the k-means clustering algorithm. Compared with the previous block matching method, the proposed method can reconstruct secret images with similar PSNRs while embedding higher-capacity information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2530-2538
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• 2015

The pixel value of depth image is depth value so that different objects which are placed on nearby position have similar pixel value. Moreover, the pixels of depth image have distinct pixel values compared to adjacent pixels while those of color image has very similar values. Accordingly distorted depth image of multiview video plus depth (MVD) needs proper error concealment methods considering the characteristics of depth image when transmission errors are happened. In this paper, classifying regions of depth image to consider edge directions and then applying adaptive error concealment methods to each region are proposed. Recovered depth images utilize with multiview video data to synthesize intermediate-view point video. The synthesized view is evaluated by objective quality metrics to demonstrate proposed method performance.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1491-1500
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• 2008

This paper proposes an intelligent surveillance system to recognize suspicious patterns of the human behavior by using the Hidden Markov Model. First, the method finds foot area of the human by motion detection algorithm from image sequence of the surveillance camera. Then, these foot locus form observation series of features to learn the HMM. The feature that is position of the human foot is changed to each code that corresponds to a specific label among 16 local partitions of image region. Therefore, specific moving patterns formed by the foot locus are the series of the label numbers. The Baum-Welch algorithm of the HMM learns each suspicious and specific pattern to classify the human behaviors. To recognize the inputted human behavior pattern in a test image, the probabilistic comparison between the learned pattern of the HMM and foot series to be tested decides the categorization of the test pattern. The experimental results show that the method can be applied to detect a suspicious person prowling in corridor.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.683-691
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• 2004

To transmit MPEG-2 video on an errorneous channel, a number of error control techniques are needed. Especially, error concealment techniques which can be implemented on receivers independent of transmitters are essential to obtain good video quality. In this paper, prediction of motion vector and an adaptive boundary matching algorithm are presented for temporal error concealment. Before the complex BMA, we perform error concealment by a motion vector prediction using neighboring motion vectors. If the candidate of error concealment is not satisfied, search range and reliable boundary pixels are selected by the temporal activity or motion vectors and a damaged macroblock is concealed by applying an adaptive BMA. This error concealment technique reduces the complexity and maintains a PSNR gain of 0.3∼0.7㏈ compared to conventional BMA.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.841-851
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• 2013

The infectious information hiding system(IIHS) is proposed for secure distribution of high quality video contents by applying optimized watermark embedding and detection algorithms to video codecs. And the watermark as infectious information is transmitted while target video is displayed or edited by codecs. This paper proposes a fast and effective reversible watermarking and infectious information generation for IIHS. Our reversible watermarking scheme enables video decoder to control video quality and watermark strength actively for by adding control code and expiration date with the watermark. Also, we designed our scheme with low computational complexity to satisfy it's real-time processing in a video codec, and to prevent time or frame delay during watermark detection and video restoration, we embedded one watermark and one side information within a macro-block. Experimental results verify that our scheme satisfy real-time watermark embedding and detection and watermark error is 0% after reversible watermark detection. Finally, we conform that the quality of restored video contens is almost same with compressed video without watermarking algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.6
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• pp.1-8
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• 2021

The performance of hidden camera cameras is improving day by day due to miniaturization and advanced technology integration according to the speed of technological development of smartphones. As this external networking computing environment is advanced and diversified, exposure to hidden cameras in addition to general safety cameras is also increasing. On the other hand, the technology for detecting and preventing hidden cameras is not keeping up with the development and speed of these hidden cameras. Therefore, in this study, the heat of the hidden camera was detected using infrared thermal detection technology based on general image and thermal image synthesis technology, and the reflectance of each wavelength according to the difference in ambient temperature was analyzed to reduce the false positive rate.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.45-51
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• 2011

In this paper, we propose an unsupervised learning method for modeling motion trajectory patterns effectively. In our approach, observations of an object on a trajectory are treated as words in a document for latent dirichlet allocation algorithm which is used for clustering words on the topic in natural language process. This allows clustering topics (e.g. go straight, turn left, turn right) effectively in complex scenes, such as crossroads. After this procedure, we learn patterns of word sequences in each cluster using Baum-Welch algorithm used to find the unknown parameters in a hidden markov model. Evaluation of abnormality can be done using forward algorithm by comparing learned sequence and input sequence. Results of experiments show that modeling of semantic region is robust against noise in various scene.

• Journal of KIISE:Software and Applications
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• v.35 no.12
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• pp.780-788
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• 2008

Modeling hand poses and tracking its movement are one of the challenging problems in computer vision. There are two typical approaches for the reconstruction of hand poses in 3D, depending on the number of cameras from which images are captured. One is to capture images from multiple cameras or a stereo camera. The other is to capture images from a single camera. The former approach is relatively limited, because of the environmental constraints for setting up multiple cameras. In this paper we propose a method of reconstructing 3D hand poses from a 2D input image sequence captured from a single camera by means of Belief Propagation in a graphical model and recognizing a finger clicking motion using a hidden Markov model. We define a graphical model with hidden nodes representing joints of a hand, and observable nodes with the features extracted from a 2D input image sequence. To track hand poses in 3D, we use a Belief Propagation algorithm, which provides a robust and unified framework for inference in a graphical model. From the estimated 3D hand pose we extract the information for each finger's motion, which is then fed into a hidden Markov model. To recognize natural finger actions, we consider the movements of all the fingers to recognize a single finger's action. We applied the proposed method to a virtual keypad system and the result showed a high recognition rate of 94.66% with 300 test data.

• Journal of the Korean Society of Radiology
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• v.16 no.1
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• pp.1-6
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• 2022

Deep learning is a collection of algorithms that enable learning by summarizing the key contents of large amounts of data; it is being developed to diagnose lesions in the medical imaging field. To evaluate the accuracy of the cerebral hemorrhage diagnosis, we used a convolutional neural network (CNN) to derive the diagnostic accuracy of cerebral parenchyma computed tomography (CT) images and the cerebral parenchyma CT images of areas where cerebral hemorrhages are suspected of having occurred. We compared the accuracy of CNN with different numbers of hidden layers and discovered that CNN with more hidden layers resulted in higher accuracy. The analysis results of the derived CT images used in this study to determine the presence of cerebral hemorrhages are expected to be used as foundation data in studies related to the application of artificial intelligence in the medical imaging industry.