• Journal of KIISE:Software and Applications
• /
• v.34 no.1
• /
• pp.59-69
• /
• 2007

In the video with a various environment, background modeling is important for extraction and recognition the moving object. For this object recognition, many methods of the background modeling are proposed in a process of preprocess. Among these there is a Kumar method which represents the Queue-based background modeling. Because this has a fixed period of updating examination of the frame, there is a limit for various system. This paper use a background modeling based on the queue. We propose the method that major parameters are decided as adaptive by background model. They are the queue size of the sliding window, the sire of grouping by the brightness of the visual and the period of updating examination of the frame. In order to determine the factors, in every process, RCO (Ratio of Correct Object), REO (Ratio of Error Object) and UR (Update Ratio) are considered to be the standard of evaluation. The proposed method can improve the existing techniques of the background modeling which is unfit for the real-time processing and recognize the object more efficient.

• Journal of KIISE
• /
• v.42 no.4
• /
• pp.451-458
• /
• 2015

Previous multimodal learning methods focus on problem-solving aspects, such as image and video search and tagging, rather than on knowledge acquisition via content modeling. In this paper, we propose the Multimodal Concept Hierarchy (MuCH), which is a content modeling method that uses a cartoon video dataset and a character-based subtitle generation method from the learned model. The MuCH model has a multimodal hypernetwork layer, in which the patterns of the words and image patches are represented, and a concept layer, in which each concept variable is represented by a probability distribution of the words and the image patches. The model can learn the characteristics of the characters as concepts from the video subtitles and scene images by using a Bayesian learning method and can also generate character-based subtitles from the learned model if text queries are provided. As an experiment, the MuCH model learned concepts from 'Pororo' cartoon videos with a total of 268 minutes in length and generated character-based subtitles. Finally, we compare the results with those of other multimodal learning models. The Experimental results indicate that given the same text query, our model generates more accurate and more character-specific subtitles than other models.

• Journal of KIISE
• /
• v.43 no.3
• /
• pp.370-379
• /
• 2016

Recently personal media were produced in a variety of ways as a lot of smart devices have been spread and services using these data have been desired. Therefore, research has been actively conducted for the media analysis and recognition technology and we can recognize the meaningful object from the media. The system using the media ontology has the disadvantage that can't classify the media appearing in the video because of the use of a video title, tags, and script information. In this paper, we propose a system to automatically classify video using the objects shown in the media data. To do this, we use a description logic-based reasoning and a rule-based inference for event processing which may vary in order. Description logic-based reasoning system proposed in this paper represents the relation of the objects in the media as activity ontology. We describe how to another rule-based reasoning system defines an event according to the order of the inference activity and order based reasoning system automatically classify the appropriate event to the category. To evaluate the efficiency of the proposed approach, we conducted an experiment using the media data classified as a valid category by the analysis of the Youtube video.

• The KIPS Transactions:PartB
• /
• v.12B no.1 s.97
• /
• pp.47-56
• /
• 2005

A moving object has a various features that its spatial location, shape, and size are changed as time goes. In addition, the moving object has both temporal feature and spatial feature. It is one of the highly interested feature information in video data. In this paper, we propose an efficient content-based multimedia information retrieval system, so tailed ECoMOT which enables user to retrieve video data by using a trajectory information of moving objects in video data. The ECoMOT includes several novel techniques to achieve content-based retrieval using moving objects' trajectories : (1) Muitiple trajectory modeling technique to model the multiple trajectories composed of several moving objects; (2) Multiple similar trajectory retrieval technique to retrieve more similar trajectories by measuring similarity between a given two trajectories composed of several moving objects; (3) Superimposed signature-based trajectory indexing technique to effectively search corresponding trajectories from a large trajectory databases; (4) convenient trajectory extraction, query generation, and retrieval interface based on graphic user interface

• Proceedings of the Korea Contents Association Conference
• /
• 2003.05a
• /
• pp.393-398
• /
• 2003

Video which contains the multiple data such as text, images, audio and motion of objects is typical multimedia data. Multimedia retrieval system using XML is essential for efficient rep. of multimedia data. Therefore, multimedia retrieval system for retrieval and structural understanding is needed to retrieve the multimedia data. This Paper Proposes the multimedia retrieval system based on XML Meta-data modeling techniques.

• International Journal of Contents
• /
• v.2 no.2
• /
• pp.1-5
• /
• 2006

The modeling and animation of natural phenomena have received much attention from the computer graphics community. Synthetic of natural phenomena are required for such diverse applications as flight simulators, special effects, video games and other virtual realty. In special effects industry there is a high demand to convincingly mimic the appearance and behavior of natural phenomena such as smoke, waterfall, rain, and fire. Particle systems are methods adequate for modeling fuzzy objects of natural phenomena. This paper presents particle system API(Application Program Interfaces) for generating special effects in virtual reality applications. The API are a set of functions that allow C++ programs to simulate the dynamics of particles for special effects in interactive and non-interactive graphics applications, not for scientific simulation.

• Journal of the Korean Society for Precision Engineering
• /
• v.15 no.2
• /
• pp.61-67
• /
• 1998

Dynamic stability in rotation of the head drum of digital VCR is very important due to the nature of high rotation speed and small angular inertia. Therefore special considerations on reducing the unbalance and assuring the stability are required the design and manufacturing process. In this paper, newly developed digital head drum is introduced. And advanced methods in analyzing and reducing the unbalance is suggested. LDV(Laser Doppler Vibrometer) was used as a measurement system verifying our modeling and new method for balancing. Experiments show that the theoretical data estimated by modeling of shaft bending caused by unbalance mass and the measured data are almost identical. The deflection was reduced to 30％ by applying the suggested balancing method.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2017.07a
• /
• pp.372-373
• /
• 2017

본 논문에서는 도래한 4차산업사회 속에서 문화콘텐츠인 캐릭터를 무료 3D모델링 도구를 이용하여 디자인하고 3D 프린팅 기술을 접목하여 개인맞춤형 캐릭터를 제작 및 판매 하고자 하는 사업에 대한 로드맵을 제시해보고자 한다. 이를 위해 첫 단계로서 3D 모델링 도구의 전반적인 특성을 살펴보고 3D 캐릭터 모델링 도구들 중에서 무료로 사용할 수 있고 사용하기 편한 도구들을 정리하여 제시해보고자 한다. 제작하고자 하는 캐릭터의 특성에 맞는 도구를 선택하고 학습하여 성공적인 창업을 할 수 있도록 방향을 제시하고자 한다.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.124-131
• /
• 2020

As users of internet-based software applications increase, functional and non-functional problems for software applications are quickly exposed to user reviews. These user reviews are an important source of information for software improvement. User review mining has become an important topic of intelligent software engineering. This study proposes a user review mining method for software improvement. User review data collected by crawling on the app review page is analyzed to check user satisfaction. It analyzes the sentiment of positive and negative that users feel with a machine learning method. And it analyzes user requirement issues through topic analysis based on structural topic modeling. The user review mining process proposed in this study conducted a case study with the a non-face-to-face video conferencing app. Software improvement through user review mining contributes to the user lock-in effect and extending the life cycle of the software. The results of this study will contribute to providing insight on improvement not only for developers, but also for service operators and marketing.

• Journal of KIBIM
• /
• v.13 no.2
• /
• pp.29-36
• /
• 2023

This study explores the potential of utilizing video-based data analysis and machine learning techniques to estimate the number of occupants within a building. The research methodology involves developing a sophisticated counting system capable of detecting and tracking individuals' entry and exit patterns. The proposed method demonstrates promising results in various scenarios; however, it also identifies the need for improvements in camera performance and external environmental conditions, such as lighting. The study emphasizes the significance of incorporating machine learning in architectural and urban planning applications, offering valuable insights for the field. In conclusion, the research calls for further investigation to address the limitations and enhance the system's accuracy, ultimately contributing to the development of a more robust and reliable solution for building occupancy estimation.