• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.582-592
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• 2001

Video indexing plays an important role in the applications such as digital video libraries or web VOD which archive large volume of digital videos. Video indexing is usually based on video segmentation. In this paper, we propose a software tool called V2Web Studio which can generate video clips utilizing shot boundary detection algorithm. With the V2Web Studio, the process of clip generation consists of the following four steps: 1) Automatic detection of shot boundaries by parsing the video, 2) Elimination of errors by manually verifying the results of the detection, 3) Building a modeling structure of logical hierarchy using the verified shots, and 4) Generating multiple video clips corresponding to each logically modeled segment. The aforementioned steps are performed by shot detector, shot verifier, video modeler and clip generator in the V2Web Studio respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.164-169
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• 2010

Since the range and Doppler resolution of the synthetic aperture radar(SAR) image becomes very high, the target detection accuracy can be significantly increased, but the computational burden is also increased. The conventional single-cell based CFAR detector performs the target detection on every single cell basis, thus it causes the serious increment of the computational load. In this paper, the improved two-step MCA-CFAR detector is proposed for the improvement of the target detection as well as the reduction of computational load: the first step is to use the MCA-CFAR, and the second step is to use the single-cell based CFAR detection in the expected target area for final decision. The performance of the proposed algorithm is compared with the conventional single-cell based CFAR and MCA-CFAR on SAR images.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.1-11
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• 2009

To classifying and filtering of adult images, in recent the computer vision techniques are actively investigated because rapidly increase for the amount of adult images accessible on the Internet. In this paper, we investigate and develop the tool filtering of adult images using skin color model. The tool is consisting of two steps. In the first step, we use a skin color classifier to extract skin color regions from an image. In the nest step, we use a region feature classifier to determine whether an image is an adult image or not an adult image depending on extracted skin color regions. Using histogram color model, a skin color classifier is trained for RGB color values of adult images and not adult images. Using SVM, a region feature classifier is trained for skin color ratio on 29 regions of adult images. Experimental results show that suggested classifier achieve a detection rate of 92.80% with 6.73% false positives.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.19-26
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• 2010

This paper proposes a classifier based on rectangular feature to detect face in real time. The goal is to realize a strong detection algorithm which satisfies both efficiency in calculation and detection performance. The proposed algorithm consists of the following three stages: Feature creation, classifier study and real time facial domain detection. Feature creation organizes a feature set with the proposed five rectangular features and calculates the feature values efficiently by using SAT (Summed-Area Tables). Classifier learning creates classifiers hierarchically by using the AdaBoost algorithm. In addition, it gets excellent detection performance by applying important face patterns repeatedly at the next level. Real time facial domain detection finds facial domains rapidly and efficiently through the classifier based on the rectangular feature that was created. Also, the recognition rate was improved by using the domain which detected a face domain as the input image and by using PCA and KNN algorithms and a Class to Class rather than the existing Point to Point technique.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.5
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• pp.381-391
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• 2022

This paper is intended to find one of the prevailing deep learning models that are a type of AI (Artificial Intelligence) that helps rapidly detect damaged buildings where disasters occur. The models selected are SSD-512, RetinaNet, and YOLOv3 which are widely used in object detection in recent years. These models are based on one-stage detector networks that are suitable for rapid object detection. These are often used for object detection due to their advantages in structure and high speed but not for damaged building detection in disaster management. In this study, we first trained each of the algorithms on xBD dataset that provides the post-disaster imagery with damage classification labels. Next, the three models are quantitatively evaluated with the mAP(mean Average Precision) and the FPS (Frames Per Second). The mAP of YOLOv3 is recorded at 34.39%, and the FPS reached 46. The mAP of RetinaNet recorded 36.06%, which is 1.67% higher than YOLOv3, but the FPS is one-third of YOLOv3. SSD-512 received significantly lower values than the results of YOLOv3 on two quantitative indicators. In a disaster situation, a rapid and precise investigation of damaged buildings is essential for effective disaster response. Accordingly, it is expected that the results obtained through this study can be effectively used for the rapid response in disaster management.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.2
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• pp.1-9
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• 2020

A smart factory is a key industrial application introduced by the 4^th industrial revolution. The automatic guided vehicle (AGV) is one of the technology realizing smart factory, but the development cost is high due to its early stage of technology. Although developing a low-cost AGV requires a lot of data, it has limited data acquisition capability because of the limited storage and the AGV movement. Hence, we propose a development environment using Bluetooth to collect data and design a lane detector. The proposed lane detector shows a high lane detection ratio regardless of light variation and a shade.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.7
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• pp.258-266
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• 2013

We propose adaptive detection algorithm to reduce a false alarm by considering the characteristics of the random noise on the detection system based on a seismic sensor. The proposed algorithm consists of the first step detection using kernel function and the second step detection using detection classes. Kernel function of the first step detection is obtained from the threshold of the Neyman-Pearon decision criterion using the probability density functions varied along the noise from the measured signal. The second step detector consists of 4 step detection class by calculating the occupancy time of the footstep using the first detected samples. In order to verify performance of the proposed algorithm, the detection of the footsteps using measured signal of targets (walking and running) are performed experimentally. The detection results are compared with a fixed threshold detector. The first step detection result has the high detection performance of 95% up to 10m area. Also, the false alarm probability is decreased from 40% to 20% when it is compared with the fixed threshold detector. By applying the detection class(second step detector), it is greatly reduced to less than 4%.

• Journal of the Korean Society of Radiology
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• v.13 no.6
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• pp.841-846
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• 2019

Effect of misalignment on the performance was evaluated for the development of time-of-flight(TOF)-PET detector. A pair of TOF-PET detector consists of Lutetium-yttrium oxyorthosilicate(LYSO) scintillation crystal with a volume of 3 mm × 3 mm × 20 mm and Geiger-mode avalanche photodiodes(GAPD) photo-sensor with a active area of 3.07 mm × 3.07 mm. Analog output signals from TOF-PET detector were sent to the pre-amplifier and then fed into the gain adjust circuit for achievement of gain homogeneity for each detector. The amplified signals were recorded and digitized by data acquisition system based on oscilloscope. The effect of the detector misalignment between LYSO and GAPD was examined for four different alignment offsets of 0.0 mm, 0.5 mm, 1.0 mm and 1.5 mm for a pair of TOF-PET detector. The photopeak position decreased from ~400 mV to ~250 mV with increasing detector misalignment. the energy resolution and time resolution were degraded from 11.6% to 16.2%, and from 477 ps to 632 ps, respectively. This study demonstrated that PET detector performance was degraded considerably depending on the detector misalignment, which would be a critical issue for the development of TOF-PET detector.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.8 s.338
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• pp.33-40
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• 2005

This paper presents a structure of the searcher using a diversity in array antenna systems operating in the cdma2000 1x signal environments. The new technique exploits the fact that the In-phase and quadrature components of interferers can respectively be viewed as an independent gaussian noise at each antnna element in most practical cdma signal environments. The proposed PN acquisition scheme is a singles-dwell PN acquisition system consisting of two stages, that is, the searching stage and the verification stage. The searching stage independently correlates the receiver multiple signals with PN generator of each antenna element for obtaining the synchronous energy at the entire region. Then, the searching results of each antenna element are non-coherently combinind. The verification stage compares the searching energy with the optimal threshold, which is predesigned in the lock detector, and decides whether the acquisition is successful or fail. In this paper, we analyzed the effect of tile diversity order to determine the mean acquisition time. In general, it is known that the mean acquisition time significantly decrease as the number of antenna elements increases. But, as the diversity order goes up, the enhancement of the performance is saturated. Therefore, to decrease the mean acquisition time of the searcher, we must design the optimal array antenna systems by considering the operating SNR range of the receiver, the probability of detection $P_D$ and that of false alarm $P_{FA}$ . The Performance of the proposed PN acquisition scheme is analyzed in frequency selective Rayleigh fading channels. In this paper, the effect of the number of antenna elements on PN acquisition scheme is shown according to the probability of detection $P_D$ and that of false alarm $P_{FA}$.

• Journal of IKEEE
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• v.18 no.1
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• pp.165-171
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• 2014

This paper presents a low complexity QR decomposition (QRD) architecture for MIMO detector. In the proposed approach, various CORDIC-based QRD algorithms are efficiently combined together to reduce the computational complexity of the QRD hardware. Based on the computational complexity analysis on various QRD algorithms, a low complexity approach is selected at each stage of QRD process. The proposed QRD architecture can be applied to any arbitrary dimension of channel matrix, and the complexity reduction grows with the increasing matrix dimension. Our QR decomposition hardware was implemented using Samsung $0.13{\mu}m$ technology. The numerical results show that the proposed architecture achieves 47% increase in the QAR (QRD Rate/Gate count) with 28.1% power savings over the conventional Householder CORDIC-based architecture for the $4{\times}4$ matrix decomposition.