• Review of KIISC
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• v.20 no.3
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• pp.48-57
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• 2010

본 고는 휴먼 객체 검출 및 분류를 위한 것으로서, 입력된 동영상에서 배경 이미지와의 차분 영상을 통해 객체 영역을 검출하고, 검출된 객체 영역에서 얼굴 즉 헤드 영역을 검출하는 방법에 대해서 설명한다. 실시간으로 녹화된 동영상에서 사람이 움직이는 위치와, 크기 등이 아주 다양하며, 또한 한 사람이 아닌 여러 사람 객체를 검출하기 위하여 다중의 사람객체 검출기를 이용한 캐스케이드 사람 객체 추출 방법을 제안한다. 얼굴 크기 등을 고려하여 헤드 영역의 shape 를 기반으로 하여 1차 검출을 수행하고, 검출되지 않은 영역에 대하여 히스토그램 기반의 얼굴 영역을 검출한다. 또한 중복된 영상에 대해 베이지안 얼굴 검출기를 통해 인증함으로써 성능을 향상시킬 수 있다.

• 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 KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.582-592
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• 2001

Video indexing plays an important role in the applications such as digital video libraries or web VOD which archive large volume of digital videos. Video indexing is usually based on video segmentation. In this paper, we propose a software tool called V2Web Studio which can generate video clips utilizing shot boundary detection algorithm. With the V2Web Studio, the process of clip generation consists of the following four steps: 1) Automatic detection of shot boundaries by parsing the video, 2) Elimination of errors by manually verifying the results of the detection, 3) Building a modeling structure of logical hierarchy using the verified shots, and 4) Generating multiple video clips corresponding to each logically modeled segment. The aforementioned steps are performed by shot detector, shot verifier, video modeler and clip generator in the V2Web Studio respectively.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.503-505
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• 1999

경계선 검출은 컴퓨터를 이용한 영상인식의 첫 단계로서, 인식의 성능에 큰 영향을 미치는 중요한 기술이다. 여러 가지 경계선 검출 기술들이 존재하지만, 이들은 모두 주어진 영사에 잡음이 존재하지 않거나 존재의 정도가 미약한 경우를 가정하여 개발되었다. 한편, 잡음이 심하게 삽입된 경우에는 경계선 검출기 적용 이전에 잡음제거 필터를 사용할 수 있다. 그러나 주어진 영상에 잡음이 존재하는지를 어떻게 컴퓨터 스스로 판단할 것인가\ulcorner 본 연구에서는 주어진 영상에 대하여 잡음의 존재 여부와 잡음의 정도 여부에 상관없이, 굳건한 경계선 검출 능력을 보이는 경계선 검출기를 개발하였다. 이를 위하여 이중 필터를 사용하였는데, 그 중 하나는 일반적으로 많이 사용되는 가우시안 필터이고, 다른 하나는 본 연구진에 의하여 개발된 웨이블릿 기반 필터이다. 실험결과, 본 논문의 경계선 검출기는 잡음의 정도에 크게 구애받지 않는 일정한 성능을 보여주었다.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.741-748
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• 2021

In this study, we evaluated image quality by changing collimator length and detector thickness using the Geant4 Application for Tomographic Emission (GATE) simulation tool. The gamma camera based on the Cadimium Zinc Telluride (CZT) and NaI detectors is modeled. In addition the images were acquired by setting 1, 2, 3, 4, 5, and 6 cm collimator length and 1, 3, 5, and 7 mm detector thickness using point source and phantom, which is designed by each diameter (4.45, 3.80, 3.15, 2.55 mm) with 447, 382, 317, and 256 Bq. The sensitivity (cps/MBq) for point source, and signal to noise ratio (SNR) and profile for phantom at the 4.45 mm by drwan the region of interests were used for quantitative analysis. Based on the results, the sensitivity according to collimator length is 2.3 ~ 48.6 cps/MBq for CZT detector, and 1.8 ~ 43.9 cps/MBq for NaI detector. The SNR using phantom is 3.6~9.8 for CZT detector, and 2.9~9.5 for NaI detector. As the collimator length is increased, the image resolution is also improved according to profile results based on the CZT and NaI detector. In addition, the senistivity for detector thickness is 0.04 ~ 0.12 cps/MBq for CZT detector, and 0.03 ~ 0.11 cps/MBq. The SNR using phnatom is 7.3~9.8 count for CZT detector, and 5.9~9.5 for NaI detector. As the detector thickness is increased, the image resolution is decreased according to profile results based on the CZT and NaI detector due to scatter ray. In conclusion, we need to set the geometric material such as detector and collimator to acuquire suitable image quality in nuclear medicine.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.10
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• pp.2335-2340
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• 2012

The detector is an uncooled detector system that functions inside the thermoelectric cooler (TEC) equipped with features instead of the cooler. The function of the thermoelectric device to control the temperature of the detector based on a function of temperature to prevent degradation of image quality to perform the role, the latest technology trend by removing the thermoelectric device size, cost a lot of effort to reduce has been studied. In this paper, It would be proposed of the actual test result using real chamber environment of for the best TECless algorithm as to minimize the degradation of image quality and obtain the low price of the uncooled detector.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.449-450
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• 2007

오래 전 부터 영상처리와 컴퓨터 비전은 많은 분야에 응용되고 발전 되어 왔다. 그러한 기술 중에 최근 각광 받고 있는 그래프 짓(Graph cut) 알고리즘은 에너지함수를 최소화 하는 가장 강력한 최적화 기법중 하나이다. 그리고 일반적으로 Sobel, Prewitt, Roberts, Canny 에지(edge) 검출기 등은 영상처리에서 영상상의 에지를 검출하기 위해 이미 널리 사용되고 발전되어 온 기술이다. 물체에서의 윤곽만 검출하기 위해서는 우리가 원하지 않는 영상 위의 에지도 검출되기 때문에 예지 검출기만으로는 물체의 윤곽만을 검출하는 것은 불가능하다. 우리는 물체의 윤곽만 검출하기를 원하기 때문에 그래프 컷과 에지 검출기의 알고리즘을 결합하면 이러한 문제를 해결 할 수 있다는 것을 제안한다. 이 논문에서는 그래프 컷 알고리즘과 에지 검출기에 관해 간략하게 기술하고 그 결과를 보일 것이다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.300-301
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• 2016

본 논문에서는 동영상에서 제한된 종류의 동적 객체를 자동적으로 검출하여 추적하는 기법을 제안한다. 제안하는 기법은 객체 검출 기법[1]과 객체 추적 기법[2]의 협업을 통해 이를 수행한다. 검출기는 매 장면마다 객체들을 검출하고 이 중 높은 신뢰도의 객체에 대해 추적을 시작한다. 추적기는 이전 장면에서 학습된 분류기에 기반하여 객체를 추적한다. 추적 결과와 겹치는 검출 결과를 분석하여 현재 장면에서 객체의 정확한 위치와 모양을 추정한다. 겹치는 검출 결과가 없을 때는 검출기로 부터 추적 결과의 신뢰도를 측정하고 문턱값에 따라 추적을 계속 진행하거나 종료한다. 실험 결과를 통해 제안하는 기법이 기존 검출 기법에 비해 우수한 검출 성능을 보임을 확인한다.

• Journal of KIISE:Software and Applications
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• v.36 no.7
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• pp.548-559
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• 2009

The cascade face detector learned by Adaboost algorithm, which was proposed by Viola and Jones, is state of the art face detector due to its great speed and accuracy. In spite of its great performance, it still suffers from false alarms, and more computation is required to reduce them. In this paper, we want to reduce false alarms with less computation using facial color. Using facial color information, proposed face detection model scans sub-window efficiently and adapts a fast face/non-face classifier at the first stage of cascade face detector. This makes face detection faster and reduces false alarms. For facial color filtering, we define a facial color membership function, and facial color filtering image is obtained using that. An integral image is calculated from facial color filtering image. Using this integral image, its density of subwindow could be obtained very fast. The proposed scanning method skips over sub-windows that do not contain possible faces based on this density. And the face/non-face classifier at the first stage of cascade detector rejects a non-face quickly. By experiment, we show that the proposed face detection model reduces false alarms and is faster than the original cascade face detector.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.102-109
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• 2009

Purpose: Flash 3D (pixon(R) method; 3D OSEM) was developed as a software program to shorten exam time and improve image quality through reconstruction, it is an image processing method that usefully be applied to nuclear medicine tomography. If perfoming brain diamox perfusion scan by reconstructing subtracted images by Flash 3D with shortened image acquisition time, there was a problem that SNR of subtracted image is lower than basal image. To increase SNR of subtracted image, we use LEAP collimators, and we emphasized on sensitivity of vessel dilatation than resolution of brain vessel. In this study, our purpose is to confirm possibility of application of LEAP collimators at brain diamox perfusion tomography, identify proper reconstruction factors by using Flash 3D. Materials and methods: (1) The evaluation of phantom: We used Hoffman 3D Brain Phantom with $^{99m}Tc$. We obtained images by LEAP and LEHR collimators (diamox image) and after 6 hours (the half life of $^{99m}Tc$: 6 hours), we use obtained second image (basal image) by same method. Also, we acquired SNR and ratio of white matters/gray matters of each basal image and subtracted image. (2) The evaluation of patient's image: We quantitatively analyzed patients who were examined by LEAP collimators then was classified as a normal group and who were examined by LEHR collimators then was classified as a normal group from 2008. 05 to 2009. 01. We evaluate the results from phantom by substituting factors. We used one-day protocol and injected $^{99m}Tc$-ECD 925 MBq at both basal image acquisition and diamox image acquisition. Results: (1) The evaluation of phantom: After measuring counts from each detector, at basal image 41~46 kcount, stress image 79~90 kcount, subtraction image 40~47 kcount were detected. LEAP was about 102~113 kcount at basal image, 188~210 kcount at stress image and 94~103 at subtraction image kcount were detected. The SNR of LEHR subtraction image was decreased than LEHR basal image about 37%, the SNR of LEAP subtraction image was decreased than LEAP basal image about 17%. The ratio of gray matter versus white matter is 2.2:1 at LEHR basal image and 1.9:1 at subtraction, and at LEAP basal image was 2.4:1 and subtraction image was 2:1. (2) The evaluation of patient's image: the counts acquired by LEHR collimators are about 40~60 kcounts at basal image, and 80~100 kcount at stress image. It was proper to set FWHM as 7 mm at basal and stress image and 11mm at subtraction image. LEAP was about 80~100 kcount at basal image and 180~200 kcount at stress image. LEAP images could reduce blurring by setting FWHM as 5 mm at basal and stress images and 7 mm at subtraction image. At basal and stress image, LEHR image was superior than LEAP image. But in case of subtraction image like a phantom experiment, it showed rough image because SNR of LEHR image was decreased. On the other hand, in case of subtraction LEAP image was better than LEHR image in SNR and sensitivity. In all LEHR and LEAP collimator images, proper subset and iteration frequency was 8 times. Conclusions: We could archive more clear and high SNR subtraction image by using proper filter with LEAP collimator. In case of applying one day protocol and reconstructing by Flash 3D, we could consider application of LEAP collimator to acquire better subtraction image.