• Journal of IKEEE
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• v.24 no.4
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• pp.1109-1116
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• 2020

The matching score evaluating human copying ability can be a good measure to check children's developmental stages, or sports movements like golf swing and dance, etc. It also can be used as HCI for AR, VR applications. This paper presents a method to evaluate the motion similarity between demonstrator who initiates movement and participant who follows the demonstrator action. We present a quantification method of the similarity which utilizes Euclidean L2 distance of Openpose keypoins vector similarity. The proposed method adapts DTW, thus can flexibly cope with the time delayed motions.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.574-584
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• 2006

Recently, ISO/IEC 23000 (also known as 'MPEG-A') has proposed a new file format called 'MAF(Multimedia Application File Format)[1]' which provides a capability of integrating/storing the widely-used compression standards for audio and video and the metadata in MPEG-7 form into a single file format. However, it is still very hard to verify the usefulness of MPEG-A in the real applications because there is still no real system that fully implements this standard. In this thesis, a design and implementation of a multimedia retrieval system based on MPEG-A standard on PC and mobile device is presented. Furthermore, an extension of MPEG-A for describing the metadata for video is also proposed. It is selected and defined as a subset of MPEG-7 MDS[4] and TV-anytime[5] for video that is useful and manageable in the mobile environments. In order to design the multimedia retrieval system based on MPEG-A, we define the system requirements in terms of portability, extensibility, compatibility, adaptability, efficiency. Based on these requirements, we design the system which composed of 3 layers: Application Layer, Middleware Layer, Platform Layer. The proposed system consists of two sub-parts, client-part and server-part. The client-part consists of MAF authoring tool, MAP player tool and MAF searching tool which allow users to create, play and search the MAF files, respectively. The server-part is composed of modules to store and manage the MAF files and metadata extracted from MAF files. We show the usefulness of the proposed system by implementing the client system both on MS-Windows platform on desk-top computer and WIPI platform on mobile phone, and validate whether it to satisfy all the system requirements. The proposed system can be used to verify the specification in the MPEG-A, and to proves the usefulness of MPEG-A in the real application.

• Economic and Environmental Geology
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• v.49 no.5
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• pp.381-388
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• 2016

The purpose of this study is to integrate and visualize using mapping techniques based on precise seabed geomorphology, seafloor backscattering images and high-resolution underwater images of the nearshore area around the Dokdo, in the East Sea. We have been obtained the precise topography map using multibeam echosounder system around the nearshore area(~50 m) of the southern part of the Seodo. Side scan sonar survey for analysis seafloor backscattering images was carried out in the same area of topography data. High-resolution underwater images(zone(a), zone(b), zone(c)) were taken in significant habitat scope of the nearshore area of the southern part of the Seodo. Using the results of bathymetry, seafloor backscattering images, high-resolution underwater images, we performed an integrated visualization about the nearshore area of the Dokdo. The integrated visualizing techniques are possible to make the seabed characteristic mapping results of the nearshore area of the Dokdo. The integrated visualization results present more complex and reliable information than separate geological products for seabed environmental mapping study and it is useful to understand the relation between seafloor characteristics and topographic environments of the study area. The integrated visualizing techniques and mapping analysis need to study sustainably and periodically, for effective monitoring of the nearshore ecosystem of the Dokdo.

• Journal of Internet Computing and Services
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• v.8 no.5
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• pp.67-79
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• 2007

As the Web technologies and wired/wireless network are improved and various new multimedia services are introduced recently, need for searching multimedia including video data has been much increasing, The previous approaches for multimedia retrieval, however, do not make use of the relationships among semantic concepts contained in multimedia contents in an efficient way and provide only restricted search results, This paper proposes a multimedia retrieval system exploiting semantic relevancy of multimedia contents based on a domain ontology, We show the effectiveness of the proposed system by experiments on a prototype system we have developed. The proposed multimedia retrieval system can extend a given search keyword based on the relationships among the semantic concepts in the ontology and can find a wide range of multimedia contents having semantic relevancy to the input keyword. It also presents the results categorized by the semantic meaning and relevancy to the keyword derived from the ontology. Independency of domain ontology with respect to metadata on the multimedia contents is preserved in the proposed system architecture.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.799-806
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• 2020

The rapid growth of smartphone-to-Internet of Things (IoT) connections and the explosive demand for data usage centered on mobile video are increasing day by day, and this increase in data usage creates many problems in the IP system. In a full-based environment, in which information requesters focus on information providers to receive information from specific servers, problems arise with bottlenecks and large data processing. To address this problem, CCN networking technology, a future network technology, has emerged as an alternative to CCN networking technology, which reduces bottlenecks that occur when requesting popular content through caching of intermediate nodes and increases network efficiency, and can be applied to military information and communication networks to address the problem of traffic concentration and the use of various surveillance equipment in full-based networks, such as scientific monitoring systems, and to provide more efficient content.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.19-26
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• 2012

The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.353-360
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• 2004

The study on scene change detection in the compressed MPEG videos has been done by various approaches. However, most of these approacher accomplished scene change detection by carrying out decoding processes and then by comparing pixels with pixels. This approach it not suitable for real time applications owing to much computing time of decoding processes. Recently, the study on scene change detection algorithms using only information of compressed domain is becoming Increasingly important. In this paper, we propose a sequential access method as an efficient scene change detection algorithm in the compressed domain. According to the type of pictures in the compressed MPEG video streams (divided in I-blocks and each I-block into P-blocks), the proposed algorithm provides effective scene change detection by applying sequential access and block by block mechanism. The proposed sequential access method provides fast and accurate detection operation by reducing checking procedures of unnecessary pictures due to molt of operations in compressed domain and checking by block units. Also, this approach uses optimal algorithm to provide fast and accurate detection operation.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.33-41
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• 2014

The purpose of this study is to provide statistical analysis based suggestions on effective methods to take advantage of social network service (SNS) for educational purposes based on investigations of undergraduate students with high utilization of SNS. To achieve this, we conducted an open questionnaire survey and qualitatively analyzed the data using NVivo 10 program. Additionally, to visualize the analysis by education subject of the SNS educational methods, we have divided the data into 3 categories of teacher, student and common area. As a result, our analysis showed that the exchange opinions and discussions, information sharing and learning of contents of questions and answers, surveys, intimacy formation, counseling etc. were of the common area. Furthermore, teacher area included the notice announcement, teaching materials sharing, real-time assessment (quiz), attendance check, assignment grading and feedback, etc. Finally the student area was of project activities (group/individual), prepare and review the shared data, video self learning, peer review and feedback of projects, lecture evaluation, etc.

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.570-579
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• 2005

Facial expression recognition technology that has potentialities for applying various fields is appling on the man-machine interface development, human identification test, and restoration of facial expression by virtual model etc. Using sequential facial images, this study proposes a simpler method for detecting human facial expressions such as happiness, anger, surprise, and sadness. Moreover the proposed method can detect the facial expressions in the conditions of the sequential facial images which is not rigid motion. We identify the determinant face and elements of facial expressions and then estimates the feature regions of the elements by using information about color, size, and position. In the next step, the direction patterns of feature regions of each element are determined by using optical flows estimated gradient methods. Using the direction model proposed by this study, we match each direction patterns. The method identifies a facial expression based on the least minimum score of combination values between direction model and pattern matching for presenting each facial expression. In the experiments, this study verifies the validity of the Proposed methods.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.95-108
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• 2010

In this paper, we present a hybrid neural network model for dynamic hand gesture recognition. The model consists of two modules, feature extraction module and pattern classification module. We first propose a modified CNN(convolutional Neural Network) a pattern recognition model for the feature extraction module. Then we introduce a weighted fuzzy min-max(WFMM) neural network for the pattern classification module. The data representation proposed in this research is a spatiotemporal template which is based on the motion information of the target object. To minimize the influence caused by the spatial and temporal variation of the feature points, we extend the receptive field of the CNN model to a three-dimensional structure. We discuss the learning capability of the WFMM neural networks in which the weight concept is added to represent the frequency factor in training pattern set. The model can overcome the performance degradation which may be caused by the hyperbox contraction process of conventional FMM neural networks. From the experimental results of human action recognition and dynamic hand gesture recognition for remote-control electric home appliances, the validity of the proposed models is discussed.