• Journal of Broadcast Engineering
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• v.17 no.4
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• pp.670-675
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• 2012

If the region of interest (ROI) is set within the picture of image and video and the high quality is provided in ROI compared to Non ROI, then overall subjective picture quality can be increased. ROI extracted by the color camera only increases the calculation complexity and reduces the extraction accuracy. In this paper, we use depth camera to set the ROI and calculate the object distance from camera, then propose a method that the different picture quality is controlled by depending on the distance of an object. That is, we apply a high quantization step size to the far object, but relatively a low quantization step size to the close object, so better picture quality can be provided. Simulation results show that applying the differential quantization step size to the distance of objects by the proposed method can improve the subjective picture quality.

• Journal of Biomedical Engineering Research
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• v.35 no.5
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• pp.132-141
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• 2014

In an ordinary CT scan, a large number of projections with full field-of-view (FFOV) are necessary to reconstruct high resolution images. However, excessive x-ray dosage is a great concern in FFOV scan. Region-of-interest (ROI) CT or sparse-view CT is considered to be a solution to reduce x-ray dosage in CT scanning, but it suffers from bright-band artifacts or streak artifacts giving contrast anomaly in the reconstructed image. In this study, we propose an image reconstruction method to eliminate the bright-band artifacts and the streak artifacts simultaneously. In addition to the ROI scan for the interior projection data with relatively high sampling rate in the view direction, we get sparse-view exterior projection data with much lower sampling rate. Then, we reconstruct images by solving a constrained total variation (TV) minimization problem for the interior projection data, which is assisted by the exterior projection data in the compressed sensing (CS) framework. For the interior image reconstruction assisted by the exterior projection data, we implemented the proposed method which enforces dual data fidelity terms and a TV term. The proposed method has effectively suppressed the bright-band artifacts around the ROI boundary and the streak artifacts in the ROI image. We expect the proposed method can be used for low-dose CT scans based on limited x-ray exposure to a small ROI in the human body.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.976-977
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• 2015

본 논문에서 우리는 입력영상에서 ROI(Region Of Interest) 지정을 이용한 의상 추천시스템을 제안한다. 의상영역 추출에 있어 ROI의 지정은 매칭 오류를 감소시키면서 매칭 속도를 향상시킬 수 있다. 우리는 평가부분에서 제안된 방을 통해 수행된 매칭이 빠르며 성공적으로 이루어졌음을 보인다.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.336-338
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• 2010

장문인식은 손바닥 중앙부에 나타난 손금과 주름의 패턴을 이용하여 개인을 식별하는 것으로, 효과적인 장문인식을 위해서는 이러한 패턴이 나타나는 관심영역(ROI: region of interest)에 대한 안정적인 추출이 필요하다. 본 논문에서는 윤곽선의 형태 정보를 토대로 적응적으로 굴곡점의 위치를 찾아내고 이로부터 ROI를 추출하는 방법을 제안한다. 제안된 방법의 성능을 확인하기 위하여 유도 막대가 없는 자연스런 장문획득 장치에 의해 수집된 장문영상을 대상으로 실험을 수행하였다. 실험결과 제안된 방법은 손의 위치 변화나 회전에 무관하게 장문영상으로부터 안정적으로 ROI를 추출함을 확인할 수 있었다.

• Journal of the Institute of Convergence Signal Processing
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• v.10 no.4
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• pp.233-238
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• 2009

In this paper, we propose the methods to select the stable region for the detect region which is required in the system used the face to the ROI in the auto-focus digital camera. this method regards the face region as the ROI in the progressive input frame and focusing the region in the mobile camera embeded ISP module automatically. The learning algorithm to detect the face is the Adaboost algorithm. we proposed the method to detect the slanted face not participate in the train process and postprocessing method for the results of detection, and then we proposed the stabilization method to sustain the region not shake for the region. we estimated the capability for the stabilization algorithm using the RMS between the trajectory and regression curve.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.634-640
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• 2018

In this paper, we propose an automatic extraction algorithm of region of interest(ROI) based on medical x-ray images. The proposed algorithm uses segmentation, feature extraction, and reference image matching to detect lesion sites in the input image. The extracted region is searched for matching lesion images in the reference DB, and the matched results are automatically extracted using the Kalman filter based fitness feedback. The proposed algorithm is extracts the contour of the left hand image for extract growth plate based on the left x-ray input image. It creates a candidate region using multi scale Hessian-matrix based sessionization. As a result, the proposed algorithm was able to split rapidly in 0.02 seconds during the ROI segmentation phase, also when extracting ROI based on segmented image 0.53, the reinforcement phase was able to perform very accurate image segmentation in 0.49 seconds.

• Journal of KIISE:Software and Applications
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• v.34 no.3
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• pp.275-302
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• 2007

We propose a novel still image compression system which supports a high quality text compression function. The system segments the text from the image and compresses the text with a high quality. The system shows 48:1 high compression ratio using context-based adaptive binary arithmetic coding. The arithmetic coding performs the high compression by the codeblocks in the bitplane. The input of the system consists of a segmentation mode and a ROI(Region Of Interest) mode. In segmentation mode, the input image is segmented into a foreground consisting of text and a background consisting of the remaining region. In ROI mode, the input image is represented by the region of interest window. The high quality text compression function with a high compression ratio shows that the proposed system can be comparable with the JPEG2000 products. This system also uses gray coding to improve the compression ratio.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.54-65
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• 2006

In this paper, we propose a new functionality to Scalable Video Coding (SVC), which is the support of multiple ROIs for heterogeneous display resolution. Scalable video coding is targeted at giving temporal, spatial, and quality scalability for the encoded bit stream. Region of interest (ROI) is an area that is semantically important to a particular user, especially users with heterogeneous display resolutions. The bitstream containing the ROIs could to be extracted without any transcoding operations, which may be one of way to satisfy QoS. To define multiple ROI in SVC, we adapted FMO, a tool defined in H.264, and based on it, we propose a way to encode and decode ROIs. The proposed method is implemented on the JSVM1.0 and the functionality is verified using it.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.1
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• pp.152-164
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• 2015

A rapid development of semiconductors, sensors and mobile network technologies has enable that the embedded device includes high sensitivity sensors, wireless communication modules and a video processing module for vehicular environment, and many researchers have been actively studying the smart car technology combined on the high performance embedded devices. The vehicle is increased as the development of society, and the risk of accidents is increasing gradually. Thus, the advanced driver assistance system providing the vehicular status and the surrounding environment of the vehicle to the driver using various sensor data is actively studied. In this paper, we design and implement the smart vehicular camera device providing the V2X communication and gathering environment information. And we studied the method to create the metadata from a received video data and sensor data using video analysis algorithm. In addition, we invent S-ROI, D-ROI methods that set a region of interest in a video frame to improve calculation performance. We performed the performance evaluation for two ROI methods. As the result, we confirmed the video processing speed that S-ROI is 3.0 times and D-ROI is 4.8 times better than a full frame analysis.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.73-77
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• 2010

Purpose: The nuclear medicine technology has been changed. The hard ware is developed so much. Also the soft ware performs a meritorious deed for the development of nuclear medicine technology. We could use the automated region of interest (ROI) instead of manual ROI. We want to know that what difference of quantitative analysis is there between automated ROI and manual ROI Materials and Methods: There are three experimental to make results. The first is what comparing the renal automated ROI and manual ROI. The second is that we compared three threshold ROI that size is difference each others with visible decision. The third is that we compared full, half, quarter automated background, and survey relative function. Results: Although the first has statistically not significant difference, the second and third have significant difference. Threshold, setting smaller threshold then renal outline or bigger, has statistically significant difference (p<0.01). The third is performed with the first experimental. Full background has significant difference, comparing each three type background (p<0.05). Conclusion: The results that there is not significant difference between automated ROI and manual ROI will increase objectivity and operator's convenience. We could know that smaller threshold then renal out line has significant difference in the second experimental. And the third experimental has results because of a increased background nearby live and spleen.