• Korean Journal of Construction Engineering and Management
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• v.24 no.4
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• pp.60-73
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• 2023

The need for adopting automation technologies to improve inefficiencies in interior finishing modeling work is increasing during the Building Information Modeling (BIM) design stage. As a result, the use of visual programming languages (VPL) for practical applications is growing. However, undefined or incorrect space designations in BIM models can hinder the development of automated finishing modeling processes, resulting in erroneous corrections and rework. To address this challenge, this study first developed a rule-based automated interior finishing detailing module for floors, walls, and ceilings. In addition, an automated space integrity checking module with 86.69% ACC using the Multi-Layer Perceptron (MLP) model was developed. These modules were integrated into a design automation module for interior finishing, which was then verified for practical utility. The results showed that the automation module reduced the time required for modeling and integrity checking by 97.6% compared to manual work, confirming its utility in assisting BIM model development for interior finishing works.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.516-516
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• 2002

With the wide usage of LiDAR data and high-resolution satellite image, 3D modeling of buildings in urban areas has become an important research topic in the photogrammetry and computer vision field for many years. However the previous modeling has its limitations of merely texturing the image to the DSM surface of the study area and does not represent the relief of building surfaces. This study is focused on presenting a system of realistic 3D building modeling from consecutive stereo image sequences using digital camera. Generally when acquiring images through camera, various parameters such as zooming, focus, and attitude are necessary to extract accurate results, which in certain cases, some parameters have to be rectified. It is, however, not always possible or practical to precisely estimate or rectify the information of camera positions or attitudes. In this research, we constructed the collinearity condition of stereo images through extracting the distinctive points from stereo image sequence. In addition, we executed image matching with Graph Cut method, which has a very high accuracy. This system successfully performed the realistic modeling of building with a good visual quality. From the study, we concluded that 3D building modeling of city area could be acquired more realistically.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.59-69
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• 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 the Ergonomics Society of Korea
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• v.26 no.2
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• pp.149-155
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• 2007

Recently, various devices requiring text input such as mobile phone IPTV, PDA and UMPC are emerging. The frequency of text entry for them is also increasing. This study was focused on the evaluation of Korean text entry interface. Various models to evaluate text entry interfaces have been proposed. Most of models were based on human cognitive process for text input. The cognitive process was divided into two components; visual scanning process and finger movement process. The time spent for visual scanning process was modeled as Hick-Hyman law, while the time for finger movement was determined as Fitts' law. There are three questions on the model-based evaluation of text entry interface. Firstly, are human cognitive processes (visual scanning and finger movement) during the entry of text sequentially occurring as the models. Secondly, is it possible to predict real text input time by previous models. Thirdly, does the human cognitive process for text input vary according to users' text entry speed. There was time gap between the real measured text input time and predicted time. The time gap was larger in the case of participants with high speed to enter text. The reason was found out investigating Eye-Hand Coordination during text input process. Differently from an assumption that visual scan on the keyboard is followed by a finger movement, the experienced group performed both visual scanning and finger movement simultaneously. Arrival Lead Time was investigated to measure the extent of time overlapping between two processes. 'Arrival Lead Time' is the interval between the eye fixation on the target button and the button click. In addition to the arrival lead time, it was revealed that the experienced group uses the less number of fixations during text entry than the novice group. This result will contribute to the improvement of evaluation model for text entry interface.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.392-412
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• 2018

Due to the semantic gap problem across different modalities, automatically retrieval from multimedia information still faces a main challenge. It is desirable to provide an effective joint model to bridge the gap and organize the relationships between them. In this work, we develop a deep image annotation and classification by fusing multi-modal semantic topics (DAC_mmst) model, which has the capacity for finding visual and non-visual topics by jointly modeling the image and loosely related text for deep image annotation while simultaneously learning and predicting the class label. More specifically, DAC_mmst depends on a non-parametric Bayesian model for estimating the best number of visual topics that can perfectly explain the image. To evaluate the effectiveness of our proposed algorithm, we collect a real-world dataset to conduct various experiments. The experimental results show our proposed DAC_mmst performs favorably in perplexity, image annotation and classification accuracy, comparing to several state-of-the-art methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.617-629
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• 2001

The mathematical models for the receptive field of simple cells in the human visual system have been developed in the areas of psychophysics, physiology and neuroscience. The various models used in the fields of digital image processing and computer vision include Gator complex, Gaussian derivatives and Hermite functions. In this paper, the effective widths for the models are derived based on the space-frequency uncertainty principle. The center frequency and parameters related to the models are determined in accordance with the human visual filters, and resultant bandwidths are analyzed. Furthermore, the characteristics of space and frequency for the models is analyzed and compared to the experimental data obtained from psychophysics.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.159-161
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• 2004

Many motion object detection algorithms rely on the process of background subtraction, an important technique that is used for detecting changes from a model of the background scene. This paper propose a novel method to update the background model image of a visual surveillance system which is not stationary. In order to do this, we use a background model based on statistical qualities of monitored images and another background model that excluded motions. By comparing each changed area computed from the two background model images and current monitored image, the areas that will be updated are decided.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.398-403
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• 2002

We developed a Off-Line Graphic Simulator which can simulate a robot model in 3D graphics space in Windows 95 version. 4 axes SCARA robot was adopted as an objective model. Forward kinematics, inverse kinematics and robot dynamics modeling were included in the developed program. The interface between users and the off-line program system in the Windows 95's graphic user interface environment was also studied. The developing language is Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D graphics.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.376-380
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• 2002

In this paper, we developed a Widows 98 version off-line programming system which can simulate a track vehicle model in 3D graphics space. The track vehicle was adopted as an objective model. The interface between users and the off-line program system in the Widows 98's graphic user interface environment was also studied. The developing language is Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.3
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• pp.19-25
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• 2001

In this paper, we developed a Windows 98 version Off-Line Programming System which can simulate a Robot model in 3D Graphics space. 4 axes SCARA Robot (especially FARA SM5) was adopted as an objective model. Forward kinemat-ics, inverse kinematics and robot dynamics modeling were included in the developed program. The interface between users and the OLP system in the Windows 98s GUI environment was also studied. The developing is Microsoft Visual C++. Graphic libraries, OpernGL, by silicon Graphics, Inc. were utilized for 3D Graphics.