• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.451-459
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• 2016

While an object detection algorithm plays a key role in many computer vision applications, it requires extensive computation to show robustness under varying lightning and geometrical distortions. Recently, some approaches formulate the problem in a classification framework and show improved performances in object recognition. Among them, random fern algorithm drew a lot of attention because of its simple structure and high recognition rates. However, it reveals performance degradation under the illumination changes and noise addition, since it computes patch features based only on pixel intensities. In this paper, we propose a new structure of combined random fern which incorporates depth information into the conventional random fern reflecting 3D structure of the patch. In addition, a new structure of object tracker which exploits the combined random fern is also introduced. Experiments show that the proposed method provides superior performance of object detection under illumination change and noisy condition compared to the conventional methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.52-55
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• 2013

본 논문은 Boosted Random Ferns 기반의 회전 불변 얼굴 검출 방법을 제안한다. 기존 Random Ferns 의 경우 특징값을 추출할 때 임의로 선택한 두 픽셀의 밝기값 비교를 통하여 이진 특징값을 추출한다. 이 경우 해당 픽셀의 밝기값에 잡음이 포함되면 특징값이 부정확하게 추출되는 문제가 있다. 본 논문에서는 이러한 문제를 해결하기 위하여 임의로 두 블록을 선택하고 해당 블록내 밝기값의 평균을 비교하여 이진 특징값을 추출하였다. 또한 픽셀 위치를 임의로 선택하여 ferns 를 구성하였던 기존의 방법 대신 최고의 분류 성능을 가지는 fern 들을 이용하여 분류기를 구성하기 위해, AdaBoost 의 방법을 Random Ferns 에 맞게 변경하였다. Boosted Random Ferns 를 트리 구조의 cascade 노드에 방향과 각도에 따라 배치하여 연산 속도를 향상시키고 false-positive를 줄이는 효과를 보았다. CMU Rotated Face Database 를 사용하여 평가하였을 때, 기존 Random Ferns 는 false-positive 의 수가 57 개 일 때 66%의 검출률을 보인 반면, Boosted Random Ferns 는 false-positive 의 수가 45 개 일 때 88%의 검출률을 보였다.

• Journal of Cardiovascular Imaging
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• v.30 no.3
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• pp.153-168
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• 2022

Recent studies have shown that sodium/glucose cotransporter 2 (SGLT2) inhibitors might exert favourable changes on cardiac parameters as observed on cardiovascular imaging. We conducted a systematic review and meta-analysis to determine the effects of SGLT2 inhibitors on cardiac imaging parameters. Four electronic databases (PubMed, Embase, Cochrane, Scopus) were searched for studies in which the effects of SGLT2 inhibitors on cardiac imaging parameters were examined. Studies in which a population was administered SGLT2 inhibitors and analysed by echocardiography and/or cardiac magnetic resonance (CMR) imaging were included. Random-effects pair-wise meta-analysis models were utilized to summarize the studies. A total of 11 randomized controlled trials was included with a combined cohort of 910 patients. Comparing patients receiving SGLT2 inhibitors with subjects receiving placebo, the mean change in CMR-measured left ventricular mass (LVM) was -3.87 g (95% confidence interval [CI], -7.77 to 0.04), that in left ventricular end-systolic volume (LVESV) was -5.96 mL (95% CI, -10.52 to -1.41) for combined LVESV outcomes, that in left atrial volume index (LAVi) was -1.78 mL/m² (95% CI, -3.01 to -0.55) for combined LAVi outcomes, and that in echocardiography-measured E/e' was -0.73 (95% CI, -1.43 to -0.03). Between-group differences were not observed in LVM and LVESV after indexation. The only between-group difference that persisted was for LAVi. Treatment with SGLT2 inhibitors resulted in reduction in LAVi and E/e' on imaging, indicating they might have an effect on outcomes associated with LV diastolic function.