• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.95-110
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• 2021

This study aims to explore the Internet personal broadcasting quality factors that influence viewer satisfaction and dissatisfaction based on the motivation-hygiene theory. Specifically, the quality factors that affect viewer satisfaction of Internet personal broadcasting are derived from the perspectives of extrinsic (contents usefulness and media usability), intrinsic (emotional/cognitive/behavioral enjoyment and creator characteristics), and social motivation (visibility, subjective norm, image, sociality). The data of 200 respondents was used to analyze the relative impact of satisfaction and dissatisfaction with the Kano model, which assumes that viewer satisfaction at both functional and emotional levels varies over quality attributes. In the empirical analysis, the quality factors were classified into attractive, one-dimensional, must-be, and indifferent quality. In addition, it was found that the customer satisfaction coefficient was high in the order of uniqueness, differentiation, and visibility. On the other hand, as a result of applying the dissatisfaction coefficient, it was identified in the order of donation, content reliability, and creator responsiveness.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1441-1456
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• 2015

The bidimensional empirical mode decomposition (BEMD) algorithm with high adaptability is more suitable to process multiple image fusion than traditional image fusion. However, the advantages of this algorithm are limited by the end effects problem, multiscale integration problem and number difference of intrinsic mode functions in multiple images decomposition. This study proposes the multiscale self-coordination BEMD algorithm to solve this problem. This algorithm outside extending the feather information with the support vector machine which has a high degree of generalization, then it also overcomes the BEMD end effects problem with conventional mirror extension methods of data processing,. The coordination of the extreme value point of the source image helps solve the problem of multiscale information fusion. Results show that the proposed method is better than the wavelet and NSCT method in retaining the characteristics of the source image information and the details of the mutation information inherited from the source image and in significantly improving the signal-to-noise ratio.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5955-5977
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• 2018

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.229-237
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• 2004

In this paper. we propose a new autocalibration algorithm. 3D-based image synthesis is roughly divided into two methods. One is using autocalibration method, and the other is using real 3D data like pattern information. The former is more progressive method. because there is no constraint or information about the scenes. Therefore autocalibration method has very difficult progress dealing with complicate non-lineal equations. Nowadays, constraints of camera intrinsic parameters are used in many researches. Therefore we solve the linear equations instead of complicate non-lineal equations. For example, to fix principal point of camera is a representative method.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.475-479
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• 2005

Such changes that the digital paradigm brought propels a change of the value system itself in the existing society so that a way of thinking that differentiates oneself from others is getting much stronger. As public interests are higher in the image production on the internet, such authoring environments as development of related technologies, expansibility of information, and systematization of available information continue to change. Therefore, a variety of unique image presentations that fit to the changed environments are necessary and an expression method is indispensable for such image production. It suggests digital image application methods and examples that contain not just simple image distortion or variation but intrinsic significance. It is believed that it will be of good use to produce a variety of unique and innate digital images in the end.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2607-2627
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• 2017

Nonnegative matrix factorization (NMF) has received considerable attention due to its effectiveness of reducing high dimensional data and importance of producing a parts-based image representation. Most of existing NMF variants attempt to address the assertion that the observed data distribute on a nonlinear low-dimensional manifold. However, recent research results showed that not only the observed data but also the features lie on the low-dimensional manifolds. In addition, a few hard priori label information is available and thus helps to uncover the intrinsic geometrical and discriminative structures of the data space. Motivated by the two aspects above mentioned, we propose a novel algorithm to enhance the effectiveness of image representation, called Dual graph-regularized Constrained Nonnegative Matrix Factorization (DCNMF). The underlying philosophy of the proposed method is that it not only considers the geometric structures of the data manifold and the feature manifold simultaneously, but also mines valuable information from a few known labeled examples. These schemes will improve the performance of image representation and thus enhance the effectiveness of image classification. Extensive experiments on common benchmarks demonstrated that DCNMF has its superiority in image classification compared with state-of-the-art methods.

• Korean Journal of Remote Sensing
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• v.26 no.6
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• pp.629-644
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• 2010

A real-time image georeferencing system requires all inputs to be determined in real-time. The intrinsic camera parameters can be identified in advance from a camera calibration process while other control information can be derived instantaneously from real-time GPS/INS data. The bottleneck process is tie point acquisition since manual operations will be definitely obstacles for real-time system while the existing extraction methods are not fast enough. In this paper, we present a fast-and-automated image matching technique based on the KLT tracker to obtain a set of tie-points in real-time. The proposed work accelerates the KLT tracker by supplying the initial guessed tie-points computed using the GPS/INS data. Originally, the KLT only works effectively when the displacement between tie-points is small. To drive an automated solution, this paper suggests an appropriate number of depth levels for multi-resolution tracking under large displacement using the knowledge of uncertainties the GPS/INS data measurements. The experimental results show that our suggested depth levels is promising and the proposed work can obtain tie-points faster than the ordinary KLT by 13% with no less accuracy. This promising result suggests that our proposed algorithm can be effectively integrated into the real-time image georeferencing for further developing a real-time surveillance application.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2000.04a
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• pp.129-133
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• 2000

It is reasonable to use the stereo vision and image processing technique to digitize 3D coordinates of grid points and to evaluate surface strains on a sheet metal parts. However this method has its intrinsic problems such as the difficulty in enhancement of bad images inevitable error due to digital image resolution of camera and frame grabber unreliability of strains and thickness evaluated from coarse grid on the corner area with large curvature and the limitation of the area that can be measured at a time. Therefore it is still hard to measure strain distribution over the entire surface of a medium,- or large-sized stamped part at a time even by using an automated strain measurement system. In this study the curvature correction algorithm based on the grid refinement and the geometry assembling algorithm based on the global error minimization (GEM) scheme are suggested. Several applications are presented to show the reliability and efficiency of these algorithms.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.883-885
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• 1995

Edges are one of the most important features used in various computer vision applications. Most of the known edge detection techniques are categorized into three gropus: First two approaches are to find gray level changes using first-order or second-order differentiation. The third method uses intrinsic propoeties of edges such as the result shown during scale space filtering. In this paper, we study various kind of edge detection techniques. Two images (Lenna image and a certain image which is composed of step, ramp, roof, and other artificial edge patterns) are used to compare different edge detection techniques and to verify the advantages and disadvantage of each techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.91-108
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• 2022

In most cases, optical images have been used as training data of DL (Deep Learning) models for object detection, recognition, identification, classification, semantic segmentation, and instance segmentation. However, properties of 3D objects in the real-world could not be fully explored with 2D images. One of the major sources of the 3D geospatial information is DSM (Digital Surface Model). In this matter, characteristic information derived from DSM would be effective to analyze 3D terrain features. Especially, man-made objects such as buildings having geometrically unique shape could be described by geometric elements that are obtained from 3D geospatial data. The background and motivation of this paper were drawn from concept of the intrinsic image that is involved in high-level visual information processing. This paper aims to extract buildings after classifying terrain features by training DL model with DSM-derived information including slope, aspect, and SRI (Shaded Relief Image). The experiments were carried out using DSM and label dataset provided by ISPRS (International Society for Photogrammetry and Remote Sensing) for CNN-based SegNet model. In particular, experiments focus on combining multi-source information to improve training performance and synergistic effect of the DL model. The results demonstrate that buildings were effectively classified and extracted by the proposed approach.