• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.191-196
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• 2021

In this paper, we implemented an image sensor module possible of detecting color temperature in an indoor environment. The color temperature information in the video information acquired by the image sensor was matched with a color difference illuminometer to produce an LUT. An algorithm was developed so that color temperature information according to the received RGB values can be automatically calculated. As a result of measuring the color temperature with an image sensor indoors, an accurate result of less than 5.91% was obtained compared to the reference value. It was confirmed that the uniformity of 23.5% or more was excellent compared to the color temperature measurement result using a color sensor.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.480-482
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• 2020

동결절편검사는 수술과 연계하여 암 전이 여부를 판단하기 위한 응급한 병리검사가 필요할 때 이용된다. 합성곱 신경망은 이미지 분류에 뛰어난 성능을 보이는 딥러닝 기법으로 본 논문에서는 이를 이용하여 유방암 전이 여부를 자동적으로 진단하는 방법을 제안한다. 실험과정은 전처리, 학습, 후처리의 과정으로 구성되어 있으며, 합성곱 신경망으로는 Resnet-18 모델을 사용하였다. 실험결과 예측 정확도 및 종양의 최대 길이 정합 여부를 점수로 환산하여 약 0.514 의 결과를 보였다.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.806-814
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• 2009

This paper reviews a study about sound reflection control using an active sound absorber. An active sound absorber includes sound transmitting and receiving piezocomposite sensor layers molded by water tight epoxy, and connected with a feedback controller. The multi-layer sensors and the controller consists a closed feedback loop, whose intrinsic characteristics shows excellent impedance matching performance within specified frequency band, and consequently, minimizes reflection waves. Multilayer sound transmission model is derived based on one dimensional model, and its performance is verified with experiment using a pulse tube setup.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.6
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• pp.545-554
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• 2019

This research aims to provide a building area extraction approach over the areas where data acquisition is impossible through field surveying, aerial photography and lidar scanning. Hence, high-resolution satellite images, which have high accessibility over the earth, are utilized for the automated building extraction in this study. 3D point clouds or DSM (Digital Surface Models), derived from the stereo image matching process, provides low quality of building area extraction due to their high level of noises and holes. In this regards, this research proposes a hybrid building area extraction approach which utilizes 3D point clouds (from image matching), and color and linear information (from imagery). First of all, ground and non-ground points are separated from 3D point clouds; then, the initial building hypothesis is extracted from the non-ground points. Secondly, color based building hypothesis is produced by considering the overlapping between the initial building hypothesis and the color segmentation result. Afterwards, line detection and space partitioning results are utilized to acquire the final building areas. The proposed approach shows 98.44% of correctness, 95.05% of completeness, and 1.05m of positional accuracy. Moreover, we see the possibility that the irregular shapes of building areas can be extracted through the proposed approach.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.31-39
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• 2011

The facial recognition algorithms using Gabor wavelet based face graph produce very good performance while they have some weakness such as a large amount of computation and an irregular result depend on initial location. We proposed a fully automatic facial recognition algorithm using a Gabor feature based geometric deformable face graph matching. The initial location and size of a face graph can be selected using Adaboost detection results for speed-up. To find the best face graph with the face model graph by updating the size and location of the graph, the geometric transformable parameters are defined. The best parameters for an optimal face graph are derived using an optimization technique. The simulation results show that the proposed algorithm can produce very good performance with recognition rate 96.7% and recognition speed 0.26 sec for FERET database.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.1-8
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• 2013

The accuracy of a real facial recognition system can be varied according to the accuracy of the eye detection algorithm when we design and implement a semi-automatic facial recognition algorithm depending on the eye position of a database. In this paper, a fully automatic facial recognition algorithm is proposed such that Gabor and LBP features are extracted from fiducial points of a face graph which was created by using fiducial points based on the eyes, nose, mouth and border lines of a face, fitted on the face image. In this algorithm, the recognition performance could be increased because a face graph can be fitted on a face image automatically and fiducial points based LPB features are implemented with the basic Gabor features. The simulation results show that the proposed algorithm can be used in real-time recognition for more than 1,000 faces and produce good recognition performance for each data set.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.410-416
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• 2010

This paper presents the auto-extraction method that searches for the Mapping points in the calibration algorithm of the image acquired by the wide angle CCD camera. In this algorithm, we remove the noise from the distorted image and then obtain the edge image. Proposed method extracts the distortion point, comparing the threshold value of the histogram of the horizontal and vertical pixel lines in edge image. This processing step can be directly applied to the original image of the wide angle CCD camera output. Proposed method results are compared with hand-worked result image using the two wide angle CCD cameras having different angles with the difference value of the result images respectively. Experimental results show that proposed method can allocate the distortion-calibration constant of the wide angle CCD camera regardless of lens type, distortion shape and image type.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2262-2270
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• 1999

In this paper, we describe gesture recognition algorithm using computer vision sensor and HMM. The automatic hand region extraction has been proposed for initializing the tracking of the tele-operation gestures. For this, distance informations(disparity map) as results of stereo matching of initial left and right images are employed to isolate the hand region from a scene. PDOE(positive difference of edges) feature images adapted here have been found to be robust against noise and background brightness. The KNU/KAERI(K/K) gesture instruction set is defined for tele-operation in atomic electric power stations. The composite recognition model constructed by concatenating three gesture instruction models including pre-orders, basic orders, and post-orders has been proposed and identified by discrete HMM. Our experimental results showed that consecutive orders composed of more than two ones are correctly recognized at the rate of above 97%.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.41-48
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• 2012

Using multi-sensor or multi-temporal high resolution satellite images together is essential for efficient applications in remote sensing area. The purpose of this paper is to estimate geometric difference of translations between high-resolution optical and SAR images automatically. The geometric and radiometric pre-processing steps were fulfilled to calculate the similarity between optical and SAR images by using Mutual Information method. The coarsest-level pyramid images of each sensor constructed by gaussian pyramid method were generated to estimate the initial translation difference of the x, y directions for calculation efficiency. The precise geometric difference of translations was able to be estimated by applying this method from coarsest-level pyramid image to original image in order. Yet even when considered only translation between optical and SAR images, the proposed method showed RMSE lower than 5m in all study sites.