• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.145-148
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• 2002

본 논문에서는 관상동맥 질환의 객관적 분석을 위해, 혈관단면영상에서의 ROI(Lumen, Media, Plaque)에 대한 정확한 분할과 분할한 영역에서 질병을 유발시키는 요소들에 대한 정량적 분석을 위한 소프트웨어를 개발하였다. 본 시스템은 Visual C++ 6.0을 이용하여 개발하였으며, 현미경으로부터 획득한 관상동맥 단면영상에 적용하여 Lumen, Media와 Plaque를 분할하고, 각 영역의 형태학적 특징을 추출하여 분석 결과를 파일로 저장할 수 있도록 구현하였다. 분석된 결과는 심장질환의 객관적 진단을 위한 보조판단근거로써 사용될 것으로 기대한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.86-97
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• 2011

This paper presents a novel algorithm to detect more accurate road lane with image sensor-based color classification and directional edge clustering. With treatment of road region and lane as a recognizable color object, the classification of color cues is processed by an iterative optimization of statistical parameters to each color object. These clustered color objects are taken into considerations as initial kernel information for color object detection and recognition. In order to improve the limitation of object classification using the color cues, the directional edge cures within the estimated region of interest in the lane boundary (ROI-LB) are clustered and combined. The results of color classification and directional edge clustering are optimally integrated to obtain the best detection of road lane. The characteristic of the proposed system is to obtain robust result to all real road environments because of using non-parametric approach based only on information of color and edge clustering without a particular mathematical road and lane model. The experimental results to the various real road environments and imaging conditions are presented to evaluate the effectiveness of the proposed method.

• Journal of Internet Computing and Services
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• v.19 no.1
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• pp.105-111
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• 2018

This paper presents the analysis of image quality with FHD(Full HD) resolution camera equipped DIC(Digital Image Correlation) platform for the measurement of the architectural structure's relative displacement. DIC platform was designed based on i.MX6 of Freescale. Displacement measurement based on DIC method, the error is affected by image quality factors as pixel number, brightness, contrast, and SNR[dB](Signal to Noise Ratio). The effect were analyzed. The displacement of ROI(Region Of Interest) area within the image was measured by sub-pixel units based on DIC method. The non-contact telemetry property of DIC method, it can be used to long distance non-contact measurement. The various displacement results was measured and analyzed with the image quality factor adjustment according to the distance(25m, 35m, 50m).

• 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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• 2015.04a
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• pp.770-772
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• 2015

어미로부터 생후 21일령 또는 28일령에 젖을 때는 이유자돈들만을 개별적인 돈사에서 합사하는 경우, 낯선 환경 및 새로운 동료들과의 서열 구분을 위한 공격적인 행동이 매우 빈번하게 발생한다. 이로 인한 돼지의 성장 저하는 농가의 소득 하락으로 이어져 국내 외 양돈 농가의 큰 문제로 인식되고 있다. 본 논문에서는 키넥트 카메라에서 취득할 수 있는 영상의 깊이정보를 이용하여 이유자돈들의 공격적인 행동을 조기 탐지할 수 있는 프로토타입 모니터링 시스템을 제안한다. 먼저 제안한 시스템은 키넥트의 적외선 센서에서 실시간으로 취득하는 깊이 정보로부터 움직임이 있는 객체들만을 탐지하고, 해당 객체들의 ROI를 설정한다, 둘째, ROI를 이용하여 5가지 특정 정보(객체의 평균, 최고, 최소 속도, 객체 속도의 표준편차, 두 객체 사이의 최소 거리)를 추출한다. 셋째, 취득한 특징 정보는 이진 클래스 분류 문제로 해석하여, 기계학습의 대표적인 모델인 SVM을 탐지기로 사용하였다. 실제 이유자돈사에서 취득한 키넥트 영상을 이용하여 모의 실험을 수행한 결과 안정적인 성능을 확인하였다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.40-41
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• 2023

This paper introduces the design of detection, recognition, and tracking algorithms for VTS radar image-based objects. The detection of objects in radar images utilizes artificial intelligence technology to determine the presence or absence of objects, and can classify the type of object using AI technology. Tracking involves the continuous tracking of detected objects over time, including technology to prevent confusion in the movement path. In particular, for land-based radar, there are unnecessary areas for detection depending on the terrain, so the function of detecting and recognizing vessels within the region of interest (ROI) set in the radar image is included. In addition, the extracted coordinate information is designed to enable various applications and interpretations by calculating speed, direction, etc.

• The Journal of the Korea Contents Association
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• v.9 no.10
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• pp.110-121
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• 2009

In this paper, we propose the new method that offer detailed-information through relax the system memory limitation about 3D model to user. That method based on making LOD(Level of Detail) model from huge 3D data of structure cultural assets. In our method as transformed AOSP algorithm, first of all it create the hierarchical structure space about 3D data, and create the LOD model by surface simplification. Then it extract the ROI(Region of Interest) of user in simplified LOD model, and then do rendering by original model and same surface detailed-information after process the local detailed in extracted region. To evaluate the proposed method, we have some experiment by using the precise 3D scan data of structure cultural assets. Our method can offer the detailed-information same as exist method, and moreover 45% reduced consumption of memory experimentally by forming mesh structure same as ROI of simplified LOD model. So we can check the huge structure cultural assets particularly in general computer environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.619-622
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• 2007

PDA provides an opportunity for users to study anytime and anywhere because it is portable and convenient thanks to its relatively small size. However, users may face difficulties to fully recognize the characters provided through lecture videos, due to its low resolution and small scaled screen. This thesis proposes a system of remote lecture in which the size of videos can be adjusted and transmitted on the basis of contents necessary for study, using detection of region-of-interest(ROI) image, and a method of image scaling in a bid to solve such a problem of PDAs. The experiment on 802.11b wireless network shows that the proposed system is able to provide more optimized lecture videos than in existing method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.313-315
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• 2023

본 논문에서는 ¹⁸F-FDG PET과 CT에서 추출한 영상인자를 이용하여 비소세포폐암의 전이를 예측하는 머신러닝 모델을 생성하였다. ¹⁸F-FDG는 종양의 포도당 대사 시 사용되며 이를 추적하여 환자의 암 세포를 진단하는데 사용되는 의료영상 기법 중 하나이다. PET과 CT 영상에서 추출한 이미지 특징은 종양의 생물학적 특성을 반영하며 해당 ROI로부터 계산되어 정량화된 값이다. 본 연구에서는 환자의 의료영상으로부터 image texture 프절 전이 예측에 있어 유의한 인자인지를 확인하기 위하여 AUC를 계산하고 단변량 분석을 진행하였다. PET과 CT에서 각각 4개(GLRLM_GLNU, SHAPE_Compacity only for 3D ROI, SHAPE_Volume_vx, SHAPE_Volume_mL)와 2개(NGLDM_Busyness, TLG_ml)의 image texture feature를 모델의 생성에 사용하였다. 생성된 각 모델의 성능을 평가하기 위해 accuracy와 AUC를 계산하였으며 그 결과 random forest(RF) 모델의 예측 정확도가 가장 높았다. 추출된 PET과 CT image texture feature를 함께 사용하여 모델을 훈련하였을 때가 각각 따로 사용하였을 때 보다 예측 성능이 개선됨을 확인하였다. 추출된 영상인자가 림프절 전이를 나타내는 바이오마커로서의 가능성을 확인할 수 있었으며 이러한 연구 결과를 바탕으로 개인별 의료 영상을 기반으로 한 비소세포폐암의 치료 전략을 수립할 수 있을 것이라 기대된다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.687-696
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• 2014

This paper presents a semantic object extraction method using user's stroke input, color, and depth information. It is supposed that a semantically meaningful object is surrounded with a few strokes from a user, and has similar depths all over the object. In the proposed method, deciding the region of interest (ROI) is based on the stroke input, and the semantically meaningful object is extracted by using color and depth information. Specifically, the proposed method consists of two steps. The first step is over-segmentation inside the ROI using color and depth information. The second step is semantically meaningful object extraction where over-segmented regions are classified into the object region and the background region according to the depth of each region. In the over-segmentation step, we propose a new marker extraction method where there are two propositions, i.e. an adaptive thresholding scheme to maximize the number of the segmented regions and an adaptive weighting scheme for color and depth components in computation of the morphological gradients that is required in the marker extraction. In the semantically meaningful object extraction, we classify over-segmented regions into the object region and the background region in order of the boundary regions to the inner regions, the average depth of each region being compared to the average depth of all regions classified into the object region. In experimental results, we demonstrate that the proposed method yields reasonable object extraction results.