• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.87-96
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• 2005

In this paper, a selective visual attention module based on motion stimuli is introduced for the purpose of detecting ROI(region of interest) or FOA(focus of attention) in motion pictures. Analysis of motion fields in our approach is in direct contrast to some of the previous studies of selective visual attention module. Motion that presents temporal visual saliency in an aspect between two successive frames is analyzed based on psychological studies in 'DORF(double opponent receptive fields)' and 'NF(noise filtration)' in MT(middle temporal cortex). Analyzed results are integrated based on the theory of 'motion integration' in MT to obtain a single conspicuous region. Experiments through a human subjective evaluation showed generally accepted results.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.939-946
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• 2018

In this paper, we implement a viewer program that can monitor effectively using omni-directional images. The program consists of four modes: Normal mode, ROI(Region of Interest) mode, Tracking mode, and Auto-rotation mode, and the results for each mode is displayed simultaneously. In the normal mode, the wide angle image is rendered as a spherical image to enable pan, tilt, and zoom. In ROI mode, the area is displayed expanded by selecting an area. And, in Auto-rotation mode, it is possible to track the object by mapping the position of the object with the rotation angle of the spherical image to prevent the object from deviating from the spherical image in Tracking mode. Parallel programming for processing of multiple modes is performed to improve the processing speed. This has the advantage that various angles can be seen compared with surveillance system having a limited angle of view.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.634-636
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• 2003

본 논문은 마스크 방식의 관심 영역(ROI, Region Of Interest) 부호 설계와 구현에 대하여 제시한다. 관심 영역에 대한 정지 영상 압축 알고리즘은 웨이블릿 변환과 사용자가 지정한 관심 영역을 결합하여 설계하였다. 즉, 사용자가 지정한 관심 영역을 이용하여 관심 영역 마스크를 생성한다. 양자화 과정에서 웨이블릿 계수들을 각 레벨과 서브밴드로 구분하고 생성된 관심 영역 마스크 정보를 이용하여 양자화 과정을 처리하여 부호화한다. 관심 영역에 대하여서는 높은 영상 품질과 그리고 전체 영상에 대하여서는 높은 압축을 동시에 실현시킬 수 있는 마스크 방식의 관심 영역 부호화 알고리즘을 설계하고 구현하였다.

• Journal of the Korean Society for Nondestructive Testing
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• v.23 no.3
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• pp.246-253
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• 2003

The initiation and growth of short fatigue cracks in the simulated aircraft structure with a series of rivet holes was detected by acoustic emission (AE). The location and the size of short tracks were determined by AE source location techniques and the measurement with traveling microscope. AE events increased intermittently with the initiation and growth of short cracks to form a stepwise increment curve of cumulative AE events. For the precise determination of AE source locations, a region-of-interest (ROI) was set around the rivet holes based on the plastic zone size in fracture mechanics. Since the signal-to-noise ratio (SNR) was very low at this early stage of fatigue cracks, the accuracy of source location was also enhanced by the wavelet transform do-noising. In practice, the majority of AE signals detected within the ROI appeared to be noise from various origins. The results showed that the effort of structural geometry and SNR should be closely taken into consideration for the accurate evaluation of fatigue damage in the structure.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.55-62
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• 2021

In this paper, we propose a human-content interface that allows users to quickly and efficiently search data through friction-based scrolling with ROI(Regions of interests). Our approach, conceived from the behavior of finding information or content of interest to users, efficiently calculates ROI for a given content. Based on the kernel developed by conceiving from GMM(Gaussian mixture model), information is searched by moving the screen smoothly and quickly to the location of the information of interest to the user. In this paper, linear interpolation is applied to make one softer inertia, and this is applied to scrolls. As a result, unlike the existing approach in which information is searched according to the user's input, our method can more easily and intuitively find information or content that the user is interested in through friction-based scrolling. For this reason, the user can save search time.

• Journal of radiological science and technology
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• v.45 no.2
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• pp.151-158
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• 2022

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert's processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.05a
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• pp.611-614
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• 2003

본 논문에서는 컨베이어 벨트상에서 이송되는 대용량 소포영상의 획득과정을 통해 ROI(Region of Interest) 고속추출하기 위한 개념모델을 제시하고, 바코드와 같은 정규패턴을 고속으로 추출하여 단계적으로 검증한 것이다. 불필요한 영역을 검사하기 위한 조건과 유사한 패턴을 단계적으로 제거하는 방법을 적용한 것이다. $4,096{\times}4,096$이상의 대용량 영상에서 여러 종류의 2차원 바코드 ROI를 추출에 대해 약 200msec 이내에 완료되고, 거의 100%에 가까운 신뢰도로 바코드 영역을 추출할 수 있도록 한 것이다.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.1 s.307
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• pp.27-34
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• 2006

In this paper, we propose a combined region-based image retrieval system using EHD(Edge Histogram Descriptor) and CLD(Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., $4{\times}4)$ non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between ELE and CLD, we need to take an $8{\times}8$ inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

• 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 Biomedical Engineering Research
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• v.23 no.2
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• pp.87-99
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• 2002

This paper describes a method for overcoming the motion artifacts inherent in synthetic aperture(SA) imaging. based on the investigation results as to the influence of a target motion on synthetic aperture techniques. This method uses a region-based motion compensation approach in which only the axial motion is estimated and compensated for a given region of interest(ROI) under the assumption that the whole ROI moves uniformly The estimated axial motion is calculated with a crosscorrelation(CC) method at the Point where the focused signal has the maximum energy within the ROI. We also presents a method for estimating the axial motion using the autocorrelation(AC) method that is widely used to estimate average Doppler frequency Both computer simulations and in vivo experiments show that the proposed methods can improve greatly the spatial resolution and SNR of ultrasound imaging by implementing the SA techniques for two-way dynamic focusing without motion artifacts. In addition the AC-barred motion compensation method provides almost the same results as the CC-based one, but with a dramatically reduced computational complexity.