• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1678-1680
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• 2002

Stereo matching technique is used in many practical fields like satellite image analysis and computer vision. In this paper, we suggest a method to extract a target object image from a complicated background. For example, human face image can be extracted from random background. This method can be applied to computer vision such as security system, dressing simulation by use of extracted human face, 3D modeling, and security system. Many researches about stereo matching have been performed. Conventional approaches can be categorized into area-based and feature-based method. In this paper, we start from area-based method and apply area tracking using scanning window. Coarse depth information is used for area merging process using area searching data. Finally, we produce a target object image.

• Journal of Electrical Engineering and Technology
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• v.7 no.2
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• pp.255-263
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• 2012

This paper presents the smart interacting multiple model (SIMM) using the concept of predicted point and maximum noise level. Maximum noise level means the largest value of the mere noises. We utilize the positional difference between measured point and predicted point as acceleration. Comparing this acceleration with the maximum noise level, we extract the acceleration to recognize the characteristics of the target. To estimate the acceleration, we propose an optional algorithm utilizing the proposed method and the Kalman filter (KF) selectively. Also, for increasing the effect of estimation, the weight given at each sub-filter of the interacting multiple model (IMM) structure is varying according to the rate of noise scale. All the procedures of the proposed algorithm can be implemented by an on-line system. Finally, an example is provided to show the effectiveness of the proposed algorithm.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.830-835
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• 2003

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Journal of Korea Multimedia Society
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• v.25 no.6
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• pp.807-818
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• 2022

In order to further improve the accuracy and time efficiency of behavior recognition in intelligent monitoring scenarios, a human behavior recognition algorithm based on YOLO combined with LSTM and CNN is proposed. Using the real-time nature of YOLO target detection, firstly, the specific behavior in the surveillance video is detected in real time, and the depth feature extraction is performed after obtaining the target size, location and other information; Then, remove noise data from irrelevant areas in the image; Finally, combined with LSTM modeling and processing time series, the final behavior discrimination is made for the behavior action sequence in the surveillance video. Experiments in the MSR and KTH datasets show that the average recognition rate of each behavior reaches 98.42% and 96.6%, and the average recognition speed reaches 210ms and 220ms. The method in this paper has a good effect on the intelligence behavior recognition.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.34-39
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• 2002

Road information is very important for topographic mapping, transportation application, urban planning and other related application fields. Therefore, automatic detection of road networks from spatial imagery, such as aerial photos and satellite imagery can play a central role in road information acquisition. In this paper, we use least squares correlation matching alone for road center tracking and show that it works. We assumed that (bright) road centerlines would be visible in the image. We further assumed that within a same road segment, there would be only small differences in brightness values. This algorithm works by defining a template around a user-given input point, which shall lie on a road centerline, and then by matching the template against the image along the orientation of the road under consideration. Once matching succeeds, new match proceeds by shifting a matched target window further along road orientation at the target window. By repeating the process above, we obtain a series of points, which lie on a road centerline successively. A 1m resolution IKONOS images over Seoul and Daejeon were used for tests. The results showed that this algorithm could extract road centerlines in any orientation and help in fast and exact he ad-up digitization/vectorization of cartographic images.

• Journal of Information Processing Systems
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• v.16 no.1
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• pp.155-170
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• 2020

Passive ranging is a critical part of machine vision measurement. Most of passive ranging methods based on machine vision use binocular technology which need strict hardware conditions and lack of universality. To measure the distance of an object placed on horizontal plane, we present a passive ranging method based on monocular vision system by smartphone. Experimental results show that given the same abscissas, the ordinatesis of the image points linearly related to their actual imaging angles. According to this principle, we first establish a depth extraction model by assuming a linear function and substituting the actual imaging angles and ordinates of the special conjugate points into the linear function. The vertical distance of the target object to the optical axis is then calculated according to imaging principle of camera, and the passive ranging can be derived by depth and vertical distance to the optical axis of target object. Experimental results show that ranging by this method has a higher accuracy compare with others based on binocular vision system. The mean relative error of the depth measurement is 0.937% when the distance is within 3 m. When it is 3-10 m, the mean relative error is 1.71%. Compared with other methods based on monocular vision system, the method does not need to calibrate before ranging and avoids the error caused by data fitting.

• Journal of Korea Multimedia Society
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• v.18 no.1
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• pp.42-51
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• 2015

In this paper, we analyze the quality status of Health and Welfare division's standardized log and asses the characteristics of the institutions' logs analysis to establish the criteria to minimize hazards and control the quality of log's institutional details to limit extraction. As a result, extraction condition's proposed development direction to adequately assess and control health and welfare abuses privacy control target log. This improvement over the status and quality of information shared with relation to institutional work of the log quality characteristics is made possible. In addition, quality control and inspection standards were prepared in accordance with the institutional log characteristics. Future research will include performing continuous analysis and improvement activities on the quality of logs with integrated control of sharing personal information and distributing information about logs' quality to proactively target organ. Therefore, we expect that correcting proactive personal information misuse and leakage is possible to achieve.

• Journal of Korean Society of Environmental Engineers
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• v.27 no.8
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• pp.787-798
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• 2005

The objectives of this study were to assess extraction kinetics of heavy metals with extraction times and to assess extraction efficiencies of heavy metals with concentrations and kinds of washing solutions. Target materials were obtained from disused metal mines. Washing solutions were water, HCl(0.1, 0.3, 1.0 N), EDTA(0.01, 0.05, 0.1 M), and sodium dodecyl sulfate(SDS, 0.1. 0.5, 1.0%). Extraction efficiencies of heavy metals by water and SDS were below 1%, and extraction efficiencies of Zn and Cd were higher than those of Pb and Cu. As results, water and SDS were not effective in extracting heavy metals from mine tailings as washing solution, but extraction efficiencies of Pb and Cu with SDS solution increased as extraction time increased. Extraction kinetics of heavy metals with HCl and EDTA were faster than those with water and SDS. The majority of heavy metals were extracted within 6 hours, and extraction kinetics was almost independent of the solution concentration. Extraction kinetics of heavy metals after 6 hours was slow, but extraction kinetics was dependent on the solution concentration. Also, as concentrations of HCl and EDTA solution were stronger, heavy metals were extracted rapidly and extraction efficiencies were increased. The extraction efficiency was high in order of Cd>Pb>Zn>Cu in using 1.0 N HCl, and Pb>Cd>Zn>Cu in using 0.1 M EDTA. Consequently, extraction effectiveness was highest for Pb in using HCl, and for Pb and Cd in using EDTA with concentration increase. Extraction time of over 6 hours was not effective in extracting heavy metals.

• The Journal of the Acoustical Society of Korea
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• v.41 no.3
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• pp.326-334
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• 2022

In this paper, we propose speech enhancement algorithm as a pre-processing for robust speech recognition in noisy environments. Auxiliary-function-based Independent Vector Analysis (AuxIVA) is performed with weighted covariance matrix using time-varying variances with scaling factor from target masks representing time-frequency contributions of target speech. The mask estimates can be obtained using Neural Network (NN) pre-trained for speech extraction or diffuseness using Coherence-to-Diffuse power Ratio (CDR) to find the direct sounds component of a target speech. In addition, outputs for omni-directional noise are closely chained by sharing the time-varying variances similarly to independent subspace analysis or IVA. The speech extraction method based on AuxIVA is also performed in Independent Low-Rank Matrix Analysis (ILRMA) framework by extending the Non-negative Matrix Factorization (NMF) for noise outputs to Non-negative Tensor Factorization (NTF) to maintain the inter-channel dependency in noise output channels. Experimental results on the CHiME-4 datasets demonstrate the effectiveness of the presented algorithms.

• Bulletin of the Korean Chemical Society
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• v.27 no.5
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• pp.649-656
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• 2006

The proposed head-space supercritical fluid extraction (SFE) methodology as an alternative to an existing conventional procedure was explored for the determination of organochlorine compounds in aqueous matrix. In this study, polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) were utilized as target analytes. To enhance the recovery efficiency, the factors such as the $CO _2$ density, the extraction time, and the extraction mode were investigated. Furthermore, the analytical procedures and the results obtained were compared with those provided by the conventional method (the U.S. EPA method 8080). Under the optimized conditions, i.e., a combination of static with dynamic SFE mode at 2,000 psi and 40 ${^{\circ}C}$, the head-space SFE methodology gave equivalent or better to the conventional method in recovery efficiencies with clear advantages such as simple sample treatment and fast analysis time as well as reduced solvent and reagent consumption.