• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.172-174
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• 2022

Logistics processing companies in Korea are mostly handling various types of products in and out. In order to process various types of products, the sorting business is performed by hand. In this paper, we propose a real-time QR code detection method using a vision sensor to achieve high efficiency with a small amount of manpower. The limiting system uses a vision sensor to process QR code recognition of logistics in real time. The proposed system can quickly identify a large number of QR codes through multiple recognition rather than QR code recognition, which is a single part of logistics. In the study, the system was actually implemented and verified, and multiple QR recognition was confirmed in the image through the vision center.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.207-215
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• 2013

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person's head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.13-23
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• 2001

Most current machine vision systems for industrial inspection were developed with one specific task in mind. Hence, these systems are inflexible in the sense that they cannot easily be adapted to other applications. In this paper, a general vision system framework has been developed that can be easily adapted to a variety of industrial web inspection problems. The objective of this system is to automatically locate and identify \\\"defects\\\" on the surface of the material being inspected. This framework is designed to be robust, to be flexible, and to be as computationally simple as possible. To assure robustness this framework employs a combined strategy of top-down and bottom-up control, hierarchical defect models, and uncertain reasoning methods. To make this framework flexible, a modular Blackboard framework is employed. To minimize computational complexity the system incorporates a simple multi-thresholding segmentation scheme, a fuzzy logic focus of attention mechanism for scene analysis operations, and a partitioning if knowledge that allows concurrent parallel processing during recognition.cognition.

• Journal of International Society for Simulation Surgery
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• v.3 no.2
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• pp.39-45
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• 2016

The augmented reality is the environment which consists of real-world view and information drawn by computer. Since the image which user can see through augmented reality device is a synthetic image composed by real-view and virtual image, it is important to make the virtual image generated by computer well harmonized with real-view image. In this paper, we present reviews of several works about computer vision and graphics methods which give user realistic augmented reality experience. To generate visually harmonized synthetic image which consists of a real and a virtual image, 3D geometry and environmental information such as lighting or material surface reflectivity should be known by the computer. There are lots of computer vision methods which aim to estimate those. We introduce some of the approaches related to acquiring geometric information, lighting environment and material surface properties using monocular or multi-view images. We expect that this paper gives reader's intuition of the computer vision methods for providing a realistic augmented reality experience.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.634-640
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• 2015

This paper proposes a novel three-dimensional mapping algorithm in Omni-Directional Vision SLAM based on a fisheye image and laser scanner data. The performance of SLAM has been improved by various estimation methods, sensors with multiple functions, or sensor fusion. Conventional 3D SLAM approaches which mainly employed RGB-D cameras to obtain depth information are not suitable for mobile robot applications because RGB-D camera system with multiple cameras have a greater size and slow processing time for the calculation of the depth information for omni-directional images. In this paper, we used a fisheye camera installed facing downwards and a two-dimensional laser scanner separate from the camera at a constant distance. We calculated fusion points from the plane coordinates of obstacles obtained by the information of the two-dimensional laser scanner and the outline of obstacles obtained by the omni-directional image sensor that can acquire surround view at the same time. The effectiveness of the proposed method is confirmed through comparison between maps obtained using the proposed algorithm and real maps.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.8
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• pp.2749-2763
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• 2021

The desirable result of infrared (IR) and visible (VIS) image fusion should have textural details from VIS images and salient targets from IR images. However, detail information in the dark regions of VIS image has low contrast and blurry edges, resulting in performance degradation in image fusion. To resolve the troubles of fuzzy details in dark regions of VIS image fusion, we have proposed a method of reflectance estimation for IR and VIS image fusion. In order to maintain and enhance details in these dark regions, dark region approximation (DRA) is proposed to optimize the Retinex model. With the improved Retinex model based on DRA, quasi-Newton method is adopted to estimate the reflectance of a VIS image. The final fusion outcome is obtained by fusing the DRA-based reflectance of VIS image with IR image. Our method could simultaneously retain the low visibility details in VIS images and the high contrast targets in IR images. Experiment statistic shows that compared to some advanced approaches, the proposed method has superiority on detail preservation and visual quality.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1291-1296
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• 2005

In this paper, we prevent a display quality drop for image of characteristics brightness ununiformity depend on LED use to LED vision. It is about that method also a control system development equipped with brightness compensation function of LED vision which is done easily for LED set up of LED vision. Generally, It is calculate driving current value is attended by each brightness to brightness characteristics mathematical function establish by "Y=aX+b", When is doing brightness value for "Y", driving current value for "X", brightness compensation value by using time for "b", characteristics value for "a" ground with characteristics curve of LED. So much, First It is create brightness data of each pixel take a photograph red, green and blue of LED vision. Second It is get average error about each pixel which get average brightness value of entire. Last, It is handle a complicated for about gradationally regulation to color and brightness of image send to LED vision. Also It raise the whole average brightness value of vision adjust for "b" value to solve brightness drop problem of LED using the long time.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.12
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• pp.87-98
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• 1999

This study is concentrated on the development of real-time estimation model and vision control method as well as the experimental test. The proposed method permits a kind of adaptability not otherwise available in that the relationship between the camera-space location of manipulable visual cues and the vector of manipulator joint coordinates is estimate in real time. This is done based on a estimation model ta\hat generalizes known manipulator kinematics to accommodate unknown relative camera position and orientation as well as uncertainty of manipulator. This vision control method is roboust and reliable, which overcomes the difficulties of the conventional research such as precise calibration of the vision sensor, exact kinematic modeling of the manipulator, and correct knowledge of position and orientation of CCD camera with respect to the manipulator base. Finally, evidence of the ability of real-time vision control method for manipulator's position control is provided by performing the thin-rod placement in space with 2 cues test model which is completed without a prior knowledge of camera or manipulator positions. This feature opens the door to a range of applications of manipulation, including a mobile manipulator with stationary cameras tracking and providing information for control of the manipulator event.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.5
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• pp.422-428
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• 2013

Recently, many unmanned aerial vehicle (UAV) studies have focused on small UAVs, because they are cost effective and suitable in dangerous indoor environments where human entry is limited. Map building through distance measurement is a key technology for the autonomous flight of small UAVs. In many researches for unmanned systems, distance could be measured by using laser range finders or stereo vision sensors. Even though a laser range finder provides accurate distance measurements, it has a disadvantage of high cost. Calculating the distance using a stereo vision sensor is straightforward. However, the sensor is large and heavy, which is not suitable for small UAVs with limited payload. This paper suggests a low-cost distance measurement system using a laser pointer and a monocular vision sensor. A method to measure distance using the suggested system is explained and some experiments on map building are conducted with these distance measurements. The experimental results are compared to the actual data and the reliability of the suggested system is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1759-1772
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• 2023

Chinese named entity recognition (NER) is a challenging work that seeks to find, recognize and classify various types of information elements in unstructured text. Due to the Chinese text has no natural boundary like the spaces in the English text, Chinese named entity identification is much more difficult. At present, most deep learning based NER models are developed using a bidirectional long short-term memory network (BiLSTM), yet the performance still has some space to improve. To further improve their performance in Chinese NER tasks, we propose a new NER model, IDCNN-BiLSTM-Highway, which is a combination of the BiLSTM, the iterated dilated convolutional neural network (IDCNN) and the highway network. In our model, IDCNN is used to achieve multiscale context aggregation from a long sequence of words. Highway network is used to effectively connect different layers of networks, allowing information to pass through network layers smoothly without attenuation. Finally, the global optimum tag result is obtained by introducing conditional random field (CRF). The experimental results show that compared with other popular deep learning-based NER models, our model shows superior performance on two Chinese NER data sets: Resume and Yidu-S4k, The F1-scores are 94.98 and 77.59, respectively.