• Journal of the Korean Society for Library and Information Science
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• v.50 no.3
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• pp.361-381
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• 2016

This study proposed two topical relevance models, simple and complex models, using EEG/ERP techniques. In the simple model regarding simple search tasks, N300 and P3b components are used. The N300 is specific to the semantic processing of pictures and the P3b reflects mechanisms involved in the decision about whether an external stimulus matches or does not match an internal representation of a specific category. In the complex model regarding complex search tasks, on the other hand, N400 and P600 components are used. The N400 reflects activation of an amodel system that integrates both image-based and conceptual representations into a context, whereas the P600 is related to complex cognitive processes. Our research results can be used as a source to design an EEG-based interactive multimedia system.

• Journal of IKEEE
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• v.19 no.3
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• pp.342-348
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• 2015

High speed implementation of digital filters is required in high data rate applications such as hard-wired wide band modem and high resolution video codec. Since the critical path of the digital filter is the MAC (multiplication and accumulation) circuit, the filter coefficient with sparse non-zero bits enables high speed implementation with adders of low hardware cost. Compressive sensing has been reported to be very successful in sparse representation and sparse signal recovery. In this paper a filter design method for digital FIR filters with CSD (canonic signed digit) coefficients using compressive sensing technique is proposed. The sparse non-zero signed bits are selected in the greedy fashion while pruning the mistakenly selected digits. A few design examples show that the proposed method can be utilized for designing sparse CSD coefficient digital FIR filters approximating the desired frequency response.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.1
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• pp.1-14
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• 2010

With the rapid growth of 3D content industry fields, 3D content-based hashing (or hash function) has been required to apply to authentication, trust and retrieval of 3D content. A content hash can be a random variable for compact representation of content. But 3D content-based hashing has been not researched yet, compared with 2D content-based hashing such as image and video. This paper develops a robust 3D content-based hashing based on key-dependent 3D surface feature. The proposed hashing uses the block surface coefficient using shape coordinate of 3D SSD and curvedness for 3D surface feature and generates a binary hash by a permutation key and a random key. Experimental results verified that the proposed hashing has the robustness against geometry and topology attacks and has the uniqueness of hash in each model and key.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.117-124
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• 2017

To access PDF documents on an electronic book, PDF documents need to be converted into EPUB which is a standard format of the electronic book. When converting a PDF document into EPUB format, we need to convert color representations from CMYK into RGB representation. It is possible to give a visual effect and a user interaction using a video and JavaScript supported by EPUB format. The schemes for converting from PDF to EPUB are studied in this paper. (1) The first study is to carry out not to lose the color conversion effect using an ICC profile. (2) The second one is a layout configuration in the conversion process. (3) The third one is to highlight a specific content such as quiz platform to provide interactive visual effect for electronic book readers. Finally, in this paper we will show the usability of EPUB based eBook converting scheme through user study.

• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.9-20
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• 2014

Making good predictions about the outcome of one's actions would seem to be essential in the context of social interaction and decision-making. This paper proposes a computational model for learning articulated motion patterns for action recognition, which mimics biological-inspired visual perception processing of human brain. Developed model of cortical architecture for the unsupervised learning of motion sequence, builds upon neurophysiological knowledge about the cortical sites such as IT, MT, STS and specific neuronal representation which contribute to articulated motion perception. Experiments show how the model automatically selects significant motion patterns as well as meaningful static snapshot categories from continuous video input. Such key poses correspond to articulated postures which are utilized in probing the trained network to impose implied motion perception from static views. We also present how sequence selective representations are learned in STS by fusing snapshot and motion input and how learned feedback connections enable making predictions about future input sequence. Network simulations demonstrate the computational capacity of the proposed model for motion recognition.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.1-10
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• 2019

Object-based image matching algorithms have been widely used in the image processing and computer vision fields. A variety of applications based on image matching algorithms have been recently developed for object recognition, 3D modeling, video tracking, and biomedical informatics. One prominent example of image matching features is the Scale Invariant Feature Transform (SIFT) scheme. However many applications using the SIFT algorithm have implemented based on stand-alone basis, not client-server architecture. In this paper, We initially implemented based on client-server structure by using SIFT algorithms to identify and match objects in biomedical images to provide useful information to the user based on the recently released Mobile platform. The major methodological contribution of this work is leveraging the convenient user interface and ubiquitous Internet connection on Mobile device for interactive delineation, segmentation, representation, matching and retrieval of biomedical images. With these technologies, our paper showcased examples of performing reliable image matching from different views of an object in the applications of semantic image search for biomedical informatics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.622-629
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• 2004

Recently, as digital broadcasting and internet are spreaded out of the world, we can easily use informations with less restrictions of time and space. According to the current trends, concerns for the ways of representing multimedia data has been rapidly increased, and users demand the services with integrated document that takes not only simple text and image but also time varying audio-visual data. Therefore, in 1998, W3C presented an international standard, SMIL in order to solve multimedia object representation and synchronization problems. By using SMIL, various multimedia elements can be integrated as a multimedia document with proper view in a space and time. Using this SMIL document, we can create new internet radio broadcasting service that delivers not only audio data but also various text, image and video. In this paper, we describe on a SMIL document editor for the common users to be able to represent time varying multimedia data with special layout and synchronization of time and space.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.347-350
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• 2010

With the advent of new media era we are living in the world of motion picture image is not an exaggeration. With the development of the mass media with images and moments that passed the day feeling like part of it is communication. In this era a sensual and shapeable image more than the logical and realistic thinking may be passed. In the act of seeing a moving image, prior to linguistic and conceptual meaning, color and screen configuration delivery and formative element in the change they produce is first recognized. Particular kind of video animation having the narrative structure, this territory effect greatly. the film even more significant effect on the area and linking the image of the scene transitions in the scheme also features a simple means of natural communication functions are being expanded. These transitional effects provide visual pleasure from a simple transition traditional methods. And In terms of narrative functions performed to help a narrative flow of availability, visual representation should continue.

• Journal of the Semiconductor & Display Technology
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• v.17 no.4
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• pp.21-26
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• 2018

Recently, the emergence of 3D cameras, 3D scanners and various cameras including Lidar is expected to be applied to applications such as AR, VR, and autonomous mobile vehicles that deal with 3D data. In Particular, the 3D point cloud data consisting of tens to hundreds of thousands of 3D points is rapidly increased in capacity compared with 2D data, Efficient encoding / decoding technology for smooth service within a limited bandwidth, and efficient service provision technology for differentiating the area of interest and the surrounding area are needed. In this paper, we propose a new quality parameter considering characteristics of 3D point cloud instead of quality change based on assumed video codec in MPEG V-PCC used in 3D point cloud compression, 3D Grid division method and representation for selectively transmitting 3D point clouds according to user's area of interest, and propose a new 3D Manifesto. By using the proposed technique, it is possible to generate more bitrate images, and it is confirmed that the efficiency of network, decoder, and renderer can be increased while selectively transmitting as needed.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.