• Annual Conference of KIPS
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• 2021.11a
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• pp.687-689
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• 2021

정맥을 이용한 생체 인식 방법은 신체의 노화에 영향을 받지 않고 높은 사용 편의성과 변조의 위험이 적어 인증 수단으로 폭넓게 활용되고 있다. 그러나 가짜 정맥 영상을 통한 스푸핑 공격 위험이 존재한다. 이러한 문제를 해결하기 위해 실제 정맥 영상과 가짜 정맥 영상을 구분하는 기술이 필요하다. 본 연구에서는 실제 정맥 데이터의 마디와 뼈의 밝기 차이를 이용해 진짜 정맥 영상과 가짜 정맥 영상을 구분하는 기술을 연구했다.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• Annual Conference of KIPS
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• 2018.05a
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• pp.553-554
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• 2018

최근 AI, 음성인식, 빅데이터, IoT의 발달에 의해 홈 스마트 비서에 대한 관심이 증대되고 있다. 이에 맞추어 국내외 대기업들은 청각 중심의 다양한 스마트 비서 제품을 출시하였다. 따라서 본 논문에서는 기존의 단점을 보완한 스마트-홈 비서 시스템을 제안한다. 스마트-홈 비서 시스템은 전방 상황 및 사용자의 행동을 인식할 수 있게 하는 영상 처리부, 카메라에서 획득한 정보에 따라 상황에 맞추어 Pseudo-Hologram 콘텐츠를 재생하는 영상 표출부로 구성되어 있다. Pseudo-Hologram을 활용하여 표출함으로써 사용자 UI/UX에 실감성을 더한 시각적인 스마트-홈 비서 시스템을 구현하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.285-286
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• 2020

전 세계적으로 유행하는 COVID-19(코로나19)로 인해 사람들은 대면 접촉을 피하게 되었고, 전염성이 높은 이유로 마스크의 착용이 의무화되고 있고, 이를 검사하는 업무가 증가하고 있다. 그래서, 인공지능 기술을 통해 업무를 도와줄 수 있는 출입자 통계와 출입자 마스크 착용 검사를 할 수 있는 시스템이 필요하다. 이를 위해 본 논문에서는 딥러닝 알고리즘을 활용한 출입자 통계와 마스크 착용 판별 시스템을 제시한다. 또한, 실시간 영상인식에 많이 활용되고 있는 YOLO-v3와 YOLO-v4, YOLO-Tiny 알고리즘을 데스크탑 PC와 Nvidia사의 Jetson Nano에 적용하여 알고리즘별 성능 비교를 통해 적합한 방법을 찾고 적용하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.675-681
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• 2022

Recently, with the development of IoT technology and AI, unmanned and automated in various fields, interest in video processing, which is the basis for automation such as object recognition and object classification, is increasing. Various studies have been conducted on noise removal in the video processing process, which has a significant impact on image quality and system accuracy and reliability, but there is a problem that it is difficult to restore images for areas with high impulse noise density. In this paper proposes a filter algorithm based on partial mask and Lagrange interpolation to restore the damaged area of impulse noise in the image. In the proposed algorithm, the filtering process was switched by comparing the filtering mask with the noise estimate and the purge weight was calculated based on the low frequency component and the high frequency component of the image to restore the image.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.302-313
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• 2023

This paper aims to develop a framework that can fully automate the quality management of training data used in large-scale Artificial Intelligence (AI) models built by the Ministry of Science and ICT (MSIT) in the 'AI Hub Data Dam' project, which has invested more than 1 trillion won since 2017. Autonomous driving technology using AI has achieved excellent performance through many studies, but it requires a large amount of high-quality data to train the model. Moreover, it is still difficult for humans to directly inspect the processed data and prove it is valid, and a model trained with erroneous data can cause fatal problems in real life. This paper presents a dataset reconstruction framework that removes abnormal data from the constructed dataset and introduces strategies to improve the performance of AI models by reconstructing them into a reliable dataset to increase the efficiency of model training. The framework's validity was verified through an experiment on the autonomous driving dataset published through the AI Hub of the National Information Society Agency (NIA). As a result, it was confirmed that it could be rebuilt as a reliable dataset from which abnormal data has been removed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.207-213
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• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of Intelligence and Information Systems
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• v.15 no.4
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• pp.53-64
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• 2009

This paper presents a performance evaluation of shape descriptors for gait analysis in case of silhouette sequence images. We used moment descriptors(MD), Fourier descriptors(FD) and Zernike descriptors(ZD) as a shape descriptor. To evaluate their performance, we firstly defined the performance index, that is, AI(asymmetry index) and PI(periodic index) based on the periodic property of the gait images. This is why they are represented by periodic parameters due to periodic gait images. This index means that how the shape is represented periodically. According to these indexes, we evaluated the data sets with periodic images, downloaded from internet. The results showed that Zernike descriptors had better performance of AI = 1.09 and PI = 2.21 than others. And in case of FD and ZD, it's efficient to implement the gait analysis with 5~10 parameters.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.385-391
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• 2022

Quality evaluation of beef carcasses is an important issue in the livestock industry. Recently, through the AI monitor system based on artificial intelligence, the quality manager can receive help in making accurate decisions based on the analysis of beef carcass images or result information. This artificial intelligence dataset is an important factor in judging performance. Existing datasets may have different surface orientation or resolution. In this paper, we proposed a two-stage classification model that can efficiently manage the grades of beef carcass image using deep learning. And to overcome the problem of the various conditions of the image, a new dataset of 1,300 images was constructed. The recognition rate of deep network for 5-grade classification using the new dataset was 72.5%. Two-stage evaluation is a method to increase reliability by taking advantage of the large difference between grades 1++, 1+, and grades 1 and 2 and 3. With two experiments using the proposed two stage model, the recognition rates of 73.7% and 77.2% were obtained. As this, The proposed method will be an efficient method if we have a dataset with 100% recognition rate in the first stage.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.9
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• pp.1176-1182
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• 2021

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method.