• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1099-1101
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• 2003

Change detection technology, which discovers the change information on the surface of the earth by comparing and analyzing multi-temporal satellite images, can be usefully applied to the various fields, such as environmental inspection, urban planning, forest policy, updating of geographical information and the military usage. In this paper, we introduce a change detection system that can extract and analyze change elements from high-resolution satellite imagery as well as low- or middle-resolution satellite imagery. The developed system provides not only 7 pixelbased methods that can be used to detect change from low- or middle-resolution satellite images but also a float window concept that can be used in manual change detection from highresolution satellite images. This system enables fast access to the very large image, because it is constituted by OGC grid coverage components. Also new change detection algorithms can be easily added into this system if once they are made into grid coverage components.

• Korean Journal of Remote Sensing
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• v.34 no.3
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• pp.535-543
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• 2018

Recently, level meters have been associated with the safety and maintenance of industrial sites and require a wide measurement range. Generally, to ensure the measurement range of the level meter, the measurement environment is improved to reduce the noise or to compensate the distortion of the signal through signal processing. The noise of FMCW (Frequency Modulated Continuous Wave) radar level meter or the distortion of the signal affects the near region characteristics of the level gauge, resulting in a reduction of the minimum detection distance. In this paper, an equalizer filter considering characteristics of window function and bit spectrum is applied to remove the noise in the near region of the level meter to improve the minimum detection distance performance and to improve the measurement reliability in the vicinity of the level meter, which is relatively difficult to detect, we want to improve the detection range.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.1
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• pp.333-342
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• 2012

This paper presents a vowel onset point (VOP) detection method based on the human auditory system. This method maps the "perceptual" frequency scale, i.e. Mel scale onto a linear acoustic frequency, and then establishes a series of Triangular Mel-weighted Filter Bank simulate the function of band pass filtering in human ear. This nonlinear critical-band filter bank helps greatly reduce the data dimensionality, and eliminate the effect of harmonic waves to make the formants more prominent in the nonlinear spaced Mel spectrum. The sum of mel spectrum peaks energy is extracted as feature for each frame, and the instinct at which the energy amplitude starts rising sharply is detected as VOP, by convolving with Gabor window. For the single-word database which contains 12 vowels articulated with different kinds of consonants, the experimental results showed a good average detection rate of 72.73%, higher than other vowel detection methods based on short-time energy and zero-crossing rate.

• Journal of IKEEE
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• v.24 no.1
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• pp.147-153
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• 2020

Conventional lane detection algorithms have problems in that the detection rate is lowered in road environments having a large change in curvature and illumination. The probabilistic Hough transform method has low lane detection rate since it exploits edges and restrictive angles. On the other hand, the method using a sliding window can detect a curved lane as the lane is detected by dividing the image into windows. However, the detection rate of this method is affected by road slopes because it uses affine transformation. In order to detect lanes robustly and avoid obstacles, we propose driving assist system using semantic segmentation based on deep learning. The architecture for segmentation is SegNet based on VGG-16. The semantic image segmentation feature can be used to calculate safety space and predict collisions so that we control a vehicle using adaptive-MPC to avoid objects and keep lanes. Simulation results with CARLA show that the proposed algorithm detects lanes robustly and avoids unknown obstacles in front of vehicle.

• The KIPS Transactions:PartA
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• v.19A no.4
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• pp.175-180
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• 2012

This paper describes the design of high-performance pedestrian and vehicle detection circuit using the Haar-like features. The proposed circuit uses a sliding window for every image frame in order to extract Haar-like features and to detect pedestrians and vehicles. A total of 200 Haar-like features per sliding window is extracted from Haar-like feature extraction circuit and the extracted features are provided to AdaBoost classifier circuit. In order to increase the processing speed, the proposed circuit adopts the parallel architecture and it can process two sliding windows at the same time. We described the proposed high-performance pedestrian and vehicle detection circuit using Verilog HDL and synthesized the gate-level circuit using the 130nm standard cell library. The synthesized circuit consists of 1,388,260 gates and its maximum operating frequency is 203MHz. Since the proposed circuit processes about 47.8 $640{\times}480$ image frames per second, it can be used to provide the real-time detection of pedestrians and vehicles.

• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.6
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• pp.568-576
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• 2010

It is widely known that the leak locating of underground plastic pipelines is much more difficult than that of cast iron pipelines. The precision of the leak locating depends upon the speed of leak signal and the time delay estimation between the two sensors on the pipeline. In this paper, six different windowing filters are considered to improve the time delay estimation especially for the plastic pipelines. The time delay is usually estimated from the peak time of cross-correlation functions. The filtering windows including rectangle, Roth, Wiener, SCOT, PHAT and maximum likelihood are applied to derive the generalized cross-correlation function and compared each other. Experimental results for the actual plastic underground water supply pipeline show that the introduction of the filtering windows improved the precision of time delay estimation. Some window functions provide excellent leak locating capability for the plastic pipe of 98 m long, which is less than 1 % of the pipe lengths. Also a new probabilistic approach that the combinations of all results from each filtering window is suggested for the better leak locating.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.99-106
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• 2007

In this paper, we present an AAW(Adaptive Attention Window) based cell image segmentation method. For semantic AAW detection we create an initial Attention Window by using a luminance map. Then the initial AW is reduced to the optimal size of the real ROI(Region of Interest) by using a quad tree segmentation. The purpose of AAW is to remove the background and to reduce the amount of processing time for segmenting ROIs. Experimental results show that the proposed method segments one or more ROIs efficiently and gives the similar segmentation result as compared with the human perception.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.474-477
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• 2012

현대사회는 시각장애인에 대한 복지가 부족하다. 예를 들어 유도블럭의 홰손, 지폐의 점자처리의 모호함 등 시각장애인을 위해 만들어진 복지조차 사용하기 어려운게 현실이다. 그래서 우리는 무선카메라와 Window Phone을 이용하여 상기 불편함을 해소하기 위하여 이 프로젝트를 시작하였다. Guide Line Detection은 앞을 못 보는 시각장애인에게 무선카메라에 보이는 영상에서 유도블럭을 찾아 시각장애인과의 거리를 음성으로 알려준다. Bill Recognition은 지폐를 인식하여 음성으로 알려준다. 길 안내 기능은 길을 찾아가지 못하는 시각장애인에게 특정 지점마다 길 안내정보를 등록하고, 등록된 정보는 시각장애인이 실시간으로 길 안내를 받을 수 있다. 음성인식은 기기를 사용하기 힘든 시각장애인들에 대한 접근성을 높이기 위해 WinPhone Application이 제공하는 모든 기능을 흔들기와 음성만으로 사용할 수 있도록 제공한다. 무선카메라의 화질과 Window Phone의 GPS 불규칙적인 오차 때문에 많은 시행착오가 있었지만 무선카메라는 웹 캠으로, GPS오차는 BingMap API의 GPS 가상 좌표로 대체하여 프로젝트를 마칠 수 있었다.

• The Journal of Korea Robotics Society
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• v.5 no.2
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• pp.85-92
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• 2010

In this paper, we propose a digital image stabilization technique using edge detection and Lucas-Kanade optical flow in order to minimize the motion of the shaken image. The accuracy of motion estimation based on block matching technique depends on the size of search window, which results in long calculation time. Therefore it is not applicable to real-time system. In addition, since the size of vector depends on that of block, it is difficult to estimate the motion which is bigger than the block size. The proposed method extracts the trust region using edge detection, to estimate the motion of some critical points in trust region based on Lucas-Kanade optical flow algorithm. The experimental results show that the proposed method stabilizes the shaking of motion image effectively in real time.