• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1314-1325
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• 1995

In this paper, we describe hardware implementation of VSB (Vestigial SideBand) mo-dulation equalization systems for HDTV (High Definition TeleVision). By modifying an adaptive equalization algorithm, we propose a hardware architecture with a low hardware cost and the performance close to floating-point operations. We also employ the pipeline concept to reduce the hardware cost. The effectiveness of the proposed hardware architecture is de- monstrated through computer simulation and the optimization result of VHDL circuit descriptions.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.160.1-160
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• 2001

In this study, control technique of two types of automatic snowplow was experimentally investigated. One is a remote-controlled snowplow used for removing snow around houses, and the other is an autonomous snowplow for use in wide, open spaces such as a parking lot of a large-scale retail store. A commercially available snowplow was modified to enable remote control by the use of a personal handy-phone system. The autonomous controller utilizes a vision sensor that consists of a CCD video camera and a computer for image processing. In addition, design of a practical landmark was examined.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.700-704
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• 1989

A robotic system with a 3-dimensional profile measuring sensor is developed in order to measure the complicated shape of the target body. Due to this 3-dimensional profile measuring sensor, a computer is able to adjust the posture of the robot hand so that complicated global profile of the target body can be recognized after several measurements from the variant directions. In order to enable fast data processing, a digital signal processor and a look-up table is introduced.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.36
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• pp.41-47
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• 1995

This study present a systematic and more economical anthropometric technique to acquire 3-D anthropometric data by the use of moire interferometry, image processing and computer vision techniques. An experiment was performed to measure in anthopometric variables (head and face), such as head length, head breath, length of ear to top of head, contained face areas, etc. We took fourty-five subjects with wide range of ages(18 years to 33 years old). The face area was calculated based on contour information. The results were then compared with plaster bandage methods. It turned out that the proposed method had 90.85% consistancy.

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.15-20
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• 2020

Software-related training can be considered essential in situations where software is an important factor in national innovation, growth and value creation. As one of the implementation methods for engineering education, various education through virtual simulations that can educate difficult situations in a similar environment are being conducted. Recently, the construction of smart factories at production and manufacturing sites is spreading, and product inspections using vision systems are being conducted. However, it has many difficulties due to lack of operation technology of vision system, but it requires a lot of cost to construct the system for education of vision system. In this paper, provide an educational virtual simulation model that integrates computer and physics engine camera functions and can extract and transmit video. It is possible to generate an image of 30Hz or more at an average of 35.4FPS of the experimental results of the proposed model, and it is possible to send and receive images in a time of 22.7ms, which can be utilized in an educational virtual simulation educational environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.11
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• pp.1437-1444
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• 2020

With the development of computer vision systems, many advances have been made in the fields of surveillance, biometrics, medical imaging, and autonomous driving. In the field of autonomous driving, in particular, the object detection technique using deep learning are widely used, and the paved road detection is a particularly crucial problem. Unlike the ROI detection algorithm used in general object detection, the structure of paved road in the image is heterogeneous, so the ROI-based object recognition architecture is not available. In this paper, we propose a deep neural network architecture for atypical paved road detection using Semantic segmentation network. In addition, we introduce the multi-scale semantic segmentation network, which is a network architecture specialized to the paved road detection. We demonstrate that the performance is significantly improved by the proposed method.

• Journal of Information Processing Systems
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• v.2 no.1
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• pp.52-57
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• 2006

One of the important functions of an Intelligent Transportation System (ITS) is to classify vehicle types using a vision system. We propose a method using machine-learning algorithms for this classification problem with 3-D object model fitting. It is also necessary to detect road lanes from a fixed traffic surveillance camera in preparation for model fitting. We apply a background mask and line analysis algorithm based on statistical measures to Hough Transform (HT) in order to remove noise and false positive road lanes. The results show that this method is quite efficient in terms of quality.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.460-464
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• 1990

Texture provides an important source of information about the local orientation of visible surfaces. An important task that arises in many computer vision systems is the reconstruction of three-dimensional depth information from two-dimensional images. The surface orientation of texel is classified by the Artificial Neural Network. The classification method to recognize the shape of 3D object with artificial neural network requires less developing time comparing to conventional method. The segmentation problem is assumed to be solved. The surface in view is smooth and is covered with repeated texture elements. In this study, 3D shape reconstruct using interpolation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2690-2701
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• 2013

Humans are able to recognize small number of people they know well by the way they walk. This ability represents basic motivation for using human gait as the means for biometric identification. Such biometric can be captured at public places from a distance without subject's collaboration, awareness or even consent. Although current approaches give encouraging results, we are still far from effective use in practical applications. In general, methods set various constraints to circumvent the influence factors like changes of view, walking speed, capture environment, clothing, footwear, object carrying, that have negative impact on recognition results. In this paper we investigate the influence of walking speed variation to different visual based gait recognition approaches and propose normalization based on geometric transformations, which mitigates its influence on recognition results. With the evaluation on MoBo gait dataset we demonstrate the benefits of using such normalization in combination with different types of gait recognition approaches.