• Journal of Broadcast Engineering
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• v.6 no.2
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• pp.177-186
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• 2001

A digital watermark Is a perceptually unobtrusive signal embedded in some multimedia asset such as an Image for copyright protection. In many cases watermark detection amounts to thresholding a correlation vague between a watermark and a received image. Watermarking detection schemes can be classified into two types. Type 1 is based on a correlation process that is applied to the difference between an original image and an input Image to be tested. Type 2 is based on a correlation process that is directly applied to an input Image. The type 1 fails to prove the rightful ownership, while type 2 has an advantage in terms of rightful ownership compared with type 1. However, type 2 has a problem that doesnt appear in type 1. The problem is that correlation between a watermark and an original Image to be watermarked is trio significant to be ignored, when it Is normalized by watermarks energy. In this paper, based on the analysis of the correlation, we propose a novel watermarking scheme to minimize the effect and also verify the performance of the proposed scheme by experiments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.1
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• pp.77-82
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• 2004

In this paper we present a barcode reader to decode two-dimensional symbology PDF417 and propose a novel method to extract the bar-space Patterns directly from the gray-level barcode image, which employs the location and the distance between extreme points of each row or column of the barcode image. This algerian proves to be very robust from the high convolutional distortion environments such as defocussing and warping, even under badly illuminating condition. If the scanned barcode image is a result of the convolution of a Gaussian-shaped point spread function with a hi-level image, popular image segmentation methods such as image thresholding can not distinguish between very narrow bar-space patterns. The Proposed algorithm shows improved Performance over current barcode readers.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.932-938
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• 2013

Recently, the Fingerprint, which is one of the methods of indoor localization using WLAN, has been many studied owing to robustness about ranging error by the diffraction and refraction of radio waves. However, in the signal gathering process and comparison operation for the measured signals with the database, this method requires time consumption and computational complexity. In order to compensate for these problems, this paper presents, based on proposed modified database, WLAN indoor localization algorithm using selective operation of collected signal in real time. The proposed algorithm reduces the configuration time and the size of the data in the database through linear interpolation and thresholding according to the signal strength, the localization accuracy, while reducing the computational complexity, is maintained through selective operation of the signals which are measured in real time. The experimental results show that the accuracy of localization is improved to 17.8% and the computational complexity reduced to 46% compared to conventional Fingerprint in the corridor by using proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.39-45
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• 2018

Compressive sensing allows recovering an original signal which has a small dimension of the signal compared to the dimension of the entire signal in a short period of time through a small number of observations. In this paper, we proposed a method for detecting tonal signal which caused by the machinery component of a vessel such as an engine, gearbox, and support elements. The tonal signal can be modeled as the sparse signal in the frequency domain when it compares to whole spectrum range. Thus, the target tonal signal can be estimated by S-OMP (Simultaneous-Orthogonal Matching Pursuit) which is one of the sparse signal recovery algorithms. In simulation section, we showed that S-OMP algorithm estimated more precise frequencies than the conventional FFT (Fast Fourier Transform) thresholding algorithm in low SNR (Signal to Noise Ratio) region.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.6
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• pp.559-568
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• 2016

For the economic management of photovoltaic power plants, it is necessary to regularly monitor the panels within the plants to detect malfunctions. Thermal infrared image cameras are generally used for monitoring, since malfunctioning panels emit higher temperatures compared to those that are functioning. Recently, technologies that observe photovoltaic arrays by mounting thermal infrared cameras on UAVs (Unmanned Aerial Vehicle) are being developed for the efficient monitoring of large-scale photovoltaic power plants. However, the technologies developed until now have had the shortcomings of having to analyze the images manually to detect malfunctioning panels, which is time-consuming. In this paper, we propose an automatic photovoltaic panel area extraction algorithm for thermal infrared images acquired via a UAV. In the thermal infrared images, panel boundaries are presented as obvious linear features, and the panels are regularly arranged. Therefore, we exaggerate the linear features with a vertical and horizontal filtering algorithm, and apply a modified hierarchical histogram clustering method to extract candidates of panel boundaries. Among the candidates, initial panel areas are extracted by exclusion editing with the results of the photovoltaic array area detection. In this step, thresholding and image morphological algorithms are applied. Finally, panel areas are refined with the geometry of the surrounding panels. The accuracy of the results is evaluated quantitatively by manually digitized data, and a mean completeness of 95.0%, a mean correctness of 96.9%, and mean quality of 92.1 percent are obtained with the proposed algorithm.

• Journal of Biosystems Engineering
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• v.28 no.1
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• pp.45-52
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• 2003

Red ginsengs are inspected manually by examining those in the dark room with back light illumination. Manual inspection is often influenced by physical condition of inspectors. Sometimes. the best grade, heaven. has some inner holes though it was inspected by a specialist. In order to resolve this problem, this study was performed to develop image processing algorithm to detect the inner holes in the x-ray image of ginseng. Because of little gray value difference between background and ginseng in the image. simple thresholding method was not appropriate. Modified watershed algorithm was used to differentiate the inner holes from background and normal ginseng body. Inner hole edge region detected by watershed algorithm consists of many number of blobs including normal portions. With line profile analysis with scanning one line at a time beginning the starting point. it shelved two peaks both ends representing extracting each blobs. in which setting threshold value as of lower peak value enabled us to obtain inner hole image. Once this procedure has to be done till the finishing point it is completing inner hole detection for one blob. Thus. conducting ail blobs by this procedure is completing inner detection of one whole ginseng. Detection results of the inner holes fer various size of red ginsengs were good even though there was small detection variation. 6.2%. according to position of x-rat tube.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.4
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• pp.31-37
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• 2001

License plate recognition system for an exclusive bus-lane is made of 5 core parts which are vehicle detection, image acquisition individual character extraction, character recognition and data transmission. Among them, the accuracy of license plate extraction can bring effect significantly to the accuracy of a whole system recognition rate also the more exact extraction of license plate is required in various weather and environment conditions. Therefore in this paper we propose a plat extraction algorithm that makes pyramid structure to reduced the extraction processing time binarizes plate's template region using adaptive thresholding extracts candidate region containing plate, and verifies a final region using plate character distribution characteristics among the candidates. Experimenal results were exactly extracted the license plate region by using proposed method to the image obtained in an exclusive bus-lane with various weather and environment conditions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.3
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• pp.594-601
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• 2009

Shooting simulation systems not only reduce a great amount of expense and time for military exercises but also prevent accidents. In particular, the shooting simulation systems using laser beam have an advantage which is very similar to the shooting exercise that uses real bullets. However, real time technique for laser beam recognition in a target image is necessary. The method proposed in this paper takes a difference image from two adjacent image frames. Then a thresholding is applied on this difference image to discriminate laser beam from background. To decide the threshold value the intensity distribution of background points is modeled assuming normal distribution. Then a noise reduction and a region segmentation are applied on the binary image to find the position of a laser beam. The time complexity of this process depends on the size of an image multiplied by the size of a mask used in the noise reduction process. The experimental result showed that the accuracy of the system was 93.3%. Even in the inaccurate cases the beam was always found in the resultant region.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.06a
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• pp.293-298
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• 2013

This research develops an ultrasonic sensor to measure the distance in underwater. The ultrasonic transducer transmits an acoustic signal to an object and receives the echo signal reflected from the object. The ultrasonic driver calculates a distance by multiplying the acoustic speed to the time of flight(TOF) which is the time necessary for the acoustic signal to travel from the transducer to the object. We apply a thresholding and a cross correlation methods to detect the TOF and show their results. When an echo pulse is corrupted with noise and its shape is distorted, the cross correlation method is used to find the TOF based on the maximum similarity between the reference and the delayed echo signals. The echoes used for the reference signal are achieved at the different environments, which improves the performance of the sensor. This paper describes the driver of the acoustic sensor and analyzes the performance of sensors in different measurement environments.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.485-492
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• 2016

A role of the binarization is very important in separating the foreground and the background in the field of the computer vision. In this study, an adaptive fuzzy binarization is proposed. An ${\alpha}$-cut control ratio is obtained by the distribution of grey level of pixels in a sliding window, and binarization is performed using the value. To obtain the ${\alpha}$-cut, existing thresholding methods which execution speed is fast are used. The threshold values are set as the center of each membership function and the fuzzy intervals of the functions are specified with the distribution of grey level of the pixel. Then ${\alpha}$-control ratio is calculated using the specified function and binarization is performed according to the membership degree of the pixels. The experimental results show the proposed method can segment the foreground and the background well than existing binarization methods and decrease loss of the foreground.