• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.496-496
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• 2000

We propose a neuron computer for tracking motion of particles in multi-dimensional space. The neuron computer is constructed of neural networks and their connections, which is a simplified model of the brain. The neuron computer is assemblage of neural networks, it includes a control unit, and the actions of the unit are represented by instructions. We designed a neuron computer to recognize and predict motion of particles. The recognition unit is constructed of neuron-array, encoder, and control part. The neuron-array is a model of the retina, and particles crease an image on the array, where the image is binary. The encoder picks one particle from the array, and translates the particle's location to Cartesian coordinates, which is scaled in [0, 1] intervals. Next, the encoder picks another particle, and does same process. The ordering and reduction of complex processes are executed by instructions. The instructions are held in the control part. The prediction unit is constructed of a multi-layer neural network and a feedback loop, where real time learning is executed. The particles' future locations are forecasted by coordinate values. The neuron computer can chase maximum 100 particles that take evasions.

• Korean Journal of Remote Sensing
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• v.32 no.5
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• pp.539-550
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• 2016

Detection of water bodies in land surface is an essential part of disaster monitoring, such as flood, storm surge, and tsunami, and plays an important role in analyzing spatial and temporal variation of water cycle. In this study, a quantitative comparison of different thresholding-based methods for water body detection and their applicability to Kompsat-5 SAR data were presented. In addition, the effect of speckle filtering on the detection result was analyzed. Furthermore, the variations of threshold values by the proportion of the water body area in the whole image were quantitatively evaluated. In order to improve the binary classification performance, a new water body detection algorithm based on the bimodality test and the majority filtering is presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.9
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• pp.4549-4566
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• 2017

This work presents a novel facial descriptor, which is named as multiscale adaptive local directional texture pattern (MALDTP) and employed for expression recognition. We apply an adaptive threshold value to encode facial image in different scales, and concatenate a series of histograms based on the MALDTP to generate facial descriptor in term of Gabor filters. In addition, some dedicated experiments were conducted to evaluate the performance of the MALDTP method in a person-independent way. The experimental results demonstrate that our proposed method achieves higher recognition rate than local directional texture pattern (LDTP). Moreover, the MALDTP method has lower computational complexity, fewer storage space and higher classification accuracy than local Gabor binary pattern histogram sequence (LGBPHS) method. In a nutshell, the proposed MALDTP method can not only avoid choosing the threshold by experience but also contain much more structural and contrast information of facial image than LDTP.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.805-814
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• 1998

An efficient image processing algorithm is proposed to recognize both the height and width modulated barcodes which are rotated and printed at an arbitrary position. The main feature of this algorithm is to utilize the gradient information of a rotated barcode with a Sobel operator. The barcode area is extracted using the gradient information, and the barcode is decoded from the binary image of the extracted area. Theis algorithm is successfully applied to the 4 state and width modulated barcodes. It takes 0.86 secoden to process a letter, and the recognition rate reaches above 98% under various testing conditions. Since both the width and height modulated barcodes are processed with the proposed algorithm, it can be applied to postal service automation.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.5-8
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• 2001

We propose a realtime numeric caption recognition algorithm that automatically recognizes the numeric caption generated by computer graphics (CG) and displays the modified caption using the recognized resource only when a valuable numeric caption appears in the aimed specific region of the live sportscast scene produced by other broadcasting stations. We extract the mesh feature from the enhanced binary image as a feature vector after acquiring the sports broadcast scenes using a frame grabber in realtime and then recover the valuable resource from just a numeric image by perceiving the character using the neural network. Finally, the result is verified by the knowledge-based rule set designed for more stable and reliable output and is displayed on a screen as the converted CC caption serving our purpose. At present, we have actually provided the realtime automatic mile-to-kilometer caption conversion system taking up our algorithm f3r the regular Major League Baseball (MLB) program being broadcasted live throughout Korea over our nationwide network. This caption conversion system is able to automatically convert the caption in mile universally used in the United States into that in kilometer in realtime, which is familiar to almost Koreans, and makes us get a favorable criticism from the TV audience.

• International Journal of Naval Architecture and Ocean Engineering
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• v.7 no.1
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• pp.87-99
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• 2015

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle $A^*$ algorithm ($FAA^*$), an advanced $A^*$ algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.1
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• pp.75-82
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• 2009

This paper propose a method to hide not only biometric features in the biometric image such as face and fingerprint for protecting them from unauthorized entity but also information of responsible person expressed as binary image which can be used to identify the responsibility of divulgence. For this, we investigate the recognition rates and bit error rates of extracted responsible person information watermark for the cases of using face and fingerprint images as cover images for fingerprint and face recognition which are the most popular biometric techniques. From these experiments, we confirm that the proposed method can be used for various application requiring to protect personal biometric information

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1696-1705
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• 1996

The theory of stack filtering, which is a generalization of median filtering, is used to the detection of intensity edges in noisey images. The proposed approach, called the Difference of Estimates(DoE) approach, is a new formulation of a morphological scheme which has been very sensitive to impulse noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the binary edge map. We find that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, burt works much better when the noise is impulsive.

• ETRI Journal
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• v.37 no.3
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• pp.595-605
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• 2015

This paper proposes a robust, imperceptible block-based digital video watermarking algorithm that makes use of the Speeded Up Robust Feature (SURF) technique. The SURF technique is used to extract the most important features of a video. A discrete multiwavelet transform (DMWT) domain in conjunction with a discrete cosine transform is used for embedding a watermark into feature blocks. The watermark used is a binary image. The proposed algorithm is further improved for robustness by an error-correction code to protect the watermark against bit errors. The same watermark is embedded temporally for every set of frames of an input video to improve the decoded watermark correlation. Extensive experimental results demonstrate that the proposed DMWT domain video watermarking using SURF features is robust against common image processing attacks, motion JPEG2000 compression, frame averaging, and frame swapping attacks. The quality of a watermarked video under the proposed algorithm is high, demonstrating the imperceptibility of an embedded watermark.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.133-140
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• 2018

In electronic warfare(EW), low probability of intercept(LPI) radar signal is a survival technique. Accordingly, identification techniques of the LPI radar waveform have became significant recently. In this paper, classification and extracting parameters techniques for 7 intrapulse modulated radar signals are introduced. We propose a technique of classifying intrapulse modulated radar signals using Convolutional Neural Network(CNN). The time-frequency image(TFI) obtained from Choi-William Distribution(CWD) is used as the input of CNN without extracting the extra feature of each intrapulse modulated radar signals. In addition a method to extract the intrapulse radar modulation parameters using binary image processing is introduced. We demonstrate the performance of the proposed intrapulse radar waveform identification system. Simulation results show that the classification system achieves a overall correct classification success rate of 90 % or better at SNR = -6 dB and the parameter extraction system has an overall error of less than 10 % at SNR of less than -4 dB.