• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2486-2491
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• 2003

This paper presents a method to segment object ID marks on poor quality images under uncontrolled lighting conditions of FA inspection process. The method is based on multiple templates and normalized gray-level correlation (NGC) method. We propose a multiple template method, called as ATM (Active Template Model) which uses combinational relation of multiple templates from model templates to match and segment several characters of the inspection images. Conventional Snakes algorithm provides a good methodology to model the functional of ATM. To increase the computation speed to segment the ID mark regions, we introduce the Dynamic Programming based algorithm. Experimental results using images from real FA environment are presented.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.794-803
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• 2009

In this paper, we will present a container auto-landing system, the system use the stereo camera to measure the container depth information. And the container region can be detected by using its hough line feature. In the line feature detection algorithm, we will detect the parallel lines and perpendicular lines which compose the rectangle region. Among all the candidate regions, we can select the region with the same aspect-ratio to the container. The region will be the detected container region. After having the object on both left and right images, we can estimate the distance from camera to object and container dimension. Then all the detect dimension information and depth inform will be applied to reconstruct the virtual environment of crane which will be introduce in this paper. Through the simulation result, we can know that, the container detection rate achieve to 97% with simple background. And the estimation algorithm can get a more accuracy result with a far distance than the near distance.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.100.5-100
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• 2002

A core technology for implementation of Augmented Reality is to develop a merging algorithm between interesting 3-D objects and real images. In this paper, we present a 3-D object recognition method to decide viewing direction toward the object from camera. This process is the starting point to merge with real image and 3-D objects. Perspective projection between a camera and 3-dimentional objects defines a plane in 3-D space that is from a line in an image and the focal point of the camera. If no errors with perfect 3-D models were introduced in during image feature extraction, then model lines in 3-D space projecting onto this line in the image would exactly lie in this plane. This observa...

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.404-406
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• 2013

An image edge has been utilized as preprocessing procedure in various field such as object detection, object recognition. there are Sobel, Prewitt, Roberts, Laplacian as conventional edge detection methods. existing methods are implement is simple, but edge detection characteristics is insufficient in impulse noise area. Therefore, to compensate the defect of conventional methods, in this paper, an edge detection algorithm using modified mask is proposed.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.553-558
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• 2008

Advancement in information technology have enabled applying vision sensor to railway, such as CCTV. CCTV has been widely used in railway application, however the CCTV is a passive system that provide limited capability to maintain safety from boarding platform. The station employee should monitor continuously CCTV monitors. Therefore immediate recognition and response to the situation is difficultin emergency situation. Recently, urban transit operators are pursuing applying an unattended station operation system for their cost reduction. Therefore, an intelligent monitoring system is need for passenger's safety in railway. The paper proposes a vision based monitoring system and object detection algorithm for passenger's safety in railway platform. The proposed system automatically detects accident in platform and analyzes level of danger using image processing technology. The system uses stereo vision technology with multi-sensors for minimizing detection error in various railway platform conditions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.85-94
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• 2021

The LPR system's trigger sensor makes problem occasionally due to the heave weight of vehicle or the obsolescence equipment. If we replace the hardware sensor to the deep-learning based software sensor in order to generate the trigger signal, LPR system maintenance would be a lot easier. In this paper we proposed the deep-learning sliding window based object detection and tracking algorithm for the LPR system's trigger signal generation. The gate passing vehicle's license plate recognition results are combined into the normal tracking algorithm to catch the position of the vehicle on the trigger line. The experimental results show that the deep learning sliding window based trigger signal generating performance was 100% for the gate passing vehicles including the 5.5% trigger signal position errors due to the minimum bounding box location errors in the vehicle detection process.

• Tunnel and Underground Space
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• v.33 no.4
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• pp.281-298
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• 2023

Continuous research efforts are being devoted to unmanned mobile platforms for lunar exploration. There is an ongoing demand for real-time information processing to accurately determine the positioning and mapping of areas of interest on the lunar surface. To apply deep learning processing and analysis techniques to practical rovers, research on software integration and optimization is imperative. In this study, a foundational investigation has been conducted on real-time analysis of virtual lunar base construction site images, aimed at automatically quantifying spatial information of key objects. This study involved transitioning from an existing region-based object recognition algorithm to a boundary box-based algorithm, thus enhancing object recognition accuracy and inference speed. To facilitate extensive data-based object matching training, the Batch Hard Triplet Mining technique was introduced, and research was conducted to optimize both training and inference processes. Furthermore, an improved software system for object recognition and identical object matching was integrated, accompanied by the development of visualization software for the automatic matching of identical objects within input images. Leveraging satellite simulative captured video data for training objects and moving object-captured video data for inference, training and inference for identical object matching were successfully executed. The outcomes of this research suggest the feasibility of implementing 3D spatial information based on continuous-capture video data of mobile platforms and utilizing it for positioning objects within regions of interest. As a result, these findings are expected to contribute to the integration of an automated on-site system for video-based construction monitoring and control of significant target objects within future lunar base construction sites.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.441-443
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• 2018

This paper deals with the analysis of leg motion using RGB image. We used RGB image as gait analysis element by using BMC(Background Model Challenge) method and by using combining object recognition segmentation algorithm and attitude detection algorithm. It is considered that gait analysis incorporating image can be used as a parameter for classification of gait pattern recognition and abnormal gait.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.33-38
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• 2024

In this paper, we propose the implementation of a drone delivery system using artificial intelligence in a situation where the use of drones is rapidly increasing and human errors are occurring. This system requires the implementation of an accurate control algorithm, assuming that last-mile delivery is delivered to the apartment veranda. To recognize the delivery location, a recognition system using the YOLO algorithm was implemented, and a delivery system was installed on the drone to measure the distance to the object and increase the delivery distance to ensure stable delivery even at long distances. As a result of the experiment, it was confirmed that the recognition system recognized the marker with a match rate of more than 60% at a distance of less than 10m while the drone hovered stably. In addition, the drone carrying a 500g package was able to withstand the torque applied as the rail lengthened, extending to 1.5m and then stably placing the package down on the veranda at the end of the rail.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.