• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.493-501
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• 1993

본 논문에서는 한글의 자소간 흘림의 연속 필기를 허용하는 원고 작성기의 구현을 연구하였다. 이러한 온라인 한글 필기의 응용에서는 신속한 인식속도를 갖는 인식방법이 요구되며, 인식중에도 계속적인 필기가 가능하도록 하여 사용자에게 편의를 제공할 수 있어야 한다. 본 논문에서는 이와같은 요구사항을 만족시키기 위하여 스트링 정합방법에 기반한 신속한 인식 방법을 사용한다. 또한, 글자인식과 필기데이타 수집이 병행적으로 처리되도록 구성됨으로써 원고작성시에 자유로운 필기동작이 가능하도록 하였다. 실험결과 50명이 쓴 21,076자에 대하여 88.96%의 인식률을 제공하였으며, 제안하는 구현 방법이 원고작성 응용에 적합하게 동작함을 알 수 있었다.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.841-844
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• 2005

Computer vision technology can be used to interpret the images captured by web cameras installed on construction sites to automatically quantify the results. This information can be used for quality control, productivity measurement and to direct construction. Steel frame construction is particularly well suited for automatic monitoring as all structural members can be viewed from a small number of camera locations, and three-dimensional computer models of steel structures are frequently available in a standard electronic format. A system is being developed that interprets the 3-D model and directs a camera to look for individual members as regular intervals to determine when each is in place and report the results. Results from a simple lab-scale system are presented along with preliminary full-scale development.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.3
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• pp.168-174
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• 1993

A new digital image processing method for measuring of the flank wear of cutting tool is presented. The method is based on computer vision technology in which the tool is illuminated by two halogen lamps and the wear zone is visualized using a CCD camera. The image is converted into digital pixel and processed to detect the wearland width. As a conclusion, it has been proved that the average wearland area and mzximum peak values of the flank wear width can monitored effectively to a measuring resolution of 0.01mm.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.55-56
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• 2020

The construction industry has the highest occupational fatality and injury rates related to accidents of any industry. Accordingly, safety managers closely monitor to prevent accidents in real-time by installing surveillance cameras at construction sites. However, due to human cognitive ability limitations, it is impossible to monitor many videos simultaneously, and the fatigue of the person monitoring surveillance cameras is also very high. Thus, to help safety managers monitor work and reduce the occupational accident rate, a study on object recognition in construction sites was conducted through surveillance cameras. In this study, we applied to the instance segmentation to identify the classification and location of objects and extract the size and shape of objects in construction sites. This research considers ways in which deep learning-based computer vision technology can be applied to safety management on a construction site.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.647-650
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• 1988

This paper proposes and sketches out a new parallel architecture of transputer-based pyramidal parallel array computer (TPPAC) used to process computationally intensive problems for geometric processing applications such as computer vision, image processing etc. It explores how efficiently the pyramid computer architecture is designed using transputer chips, and poses a new interconnection scheme for TPPAC without using additional transputers.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.129-134
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• 2024

Object Detection is an emerging technology in the field of Computer Vision and Image Processing that deals with detecting objects of a particular class in digital images. It has considered being one of the complicated and challenging tasks in computer vision. Earlier several machine learning-based approaches like SIFT (Scale-invariant feature transform) and HOG (Histogram of oriented gradients) are widely used to classify objects in an image. These approaches use the Support vector machine for classification. The biggest challenges with these approaches are that they are computationally intensive for use in real-time applications, and these methods do not work well with massive datasets. To overcome these challenges, we implemented a Deep Learning based approach Convolutional Neural Network (CNN) in this paper. The Proposed approach provides accurate results in detecting objects in an image by the area of object highlighted in a Bounding Box along with its accuracy.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.577-584
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• 1998

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.300-306
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• 2000

This paper introduces a new concept of automation for bio-production with tele-operative system. The proposed system showed practical and feasible way of automation for the volatile bio-production process. Based on the proposition, recognition of the job environment with object identification was performed using computer vision system. A man-machine interactive hybrid decision-making, which utilized a concept of tele-operation was proposed to overcome limitations of the capability of computer in image processing and feature extraction from the complex environment image. Identifying watermelons from the outdoor scene of the cultivation field was selected to realize the proposed concept. Identifying watermelon from the camera image of the outdoor cultivation field is very difficult because of the ambiguity among stems, leaves, shades, and especially fruits covered partly by leaves or stems. The analog signal of the outdoor image was captured and transmitted wireless to the host computer by R.F module. The localized window was formed from the outdoor image by pointing to the touch screen. And then a sequence of algorithms to identify the location and size of the watermelon was performed with the local window image. The effect of the light reflectance of fruits, stems, ground, and leaves were also investigated.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.81-92
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• 2023

According to the 2020 industrial accident reports of the Ministry of Employment and Labor, the number of fatal accidents in the construction industry over the past 5 years has been higher than in other industries. Of these more than 50% of fatal accidents are initially caused by fall accidents. The central government is intensively managing falling/jamming protection device and the use of personal protective equipment to eradicate the inappropriate factors disrupting safety at construction sites. In addition, although efforts have been made to prevent safety accidents with the proposal of the Special Act on Construction Safety, fatalities on construction sites are constantly occurring. Therefore, this study developed a model that automatically detects the wearing state of the worker's safety helmet and belt using computer vision technology. In considerations of conditions occurring at construction sites, we suggest an optimization method, which has been verified in terms of the accuracy and operation speed of the proposed model. As a result, it is possible to improve the efficiency of inspection and patrol by construction site managers, which is expected to contribute to reinforcing competency of safety management.