• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.12
• /
• pp.77-83
• /
• 1998

Generally a moving picture suffers from motion blur, due to relative motion between moving objects and the image formation system. The purpose of this paper is to propose teh model for the motion blur and the restoration method using the regularized iterative technique. In the proposed model, the boundary effect between moving objects and background is analyzed mathematically to overcome the limit of the spatially invariant model. And we present the motion-based image segmentation technique for the object-based image restoration, which is the modified version of the conventional segmentation method. Based on the proposed model, the restoration technique removes the motion blur by using the estimated motion parameter from the result of the segmentation.

• Journal of Navigation and Port Research
• /
• v.28 no.1
• /
• pp.91-96
• /
• 2004

Although features, core element of marine GIS in many application, are same things users have difficulty in using them on account of varying according to method of classification. Accordingly feature cataloguing in accordance with the standard is the trend of the modem world. In this article we have became familiar with ISO 19110 - Methodology for Feature Cataloguing, we was able to discuss element and definition of features for the purpose of Marine GIS's activation. Through the result of study, we presented the methodology of Marine GIS feature cataloguing.

• Journal of the Korea Computer Graphics Society
• /
• v.17 no.3
• /
• pp.21-31
• /
• 2011

This paper presents a practical method to estimate the directions of light sources in real environment, using a mirror sphere placed on a set of known natural features in augmented reality. For the stable result of static lighting, we take the multiple images around the sphere and estimate the principal light directions of the vector clusters for each light source in realtime. We also estimate the moving illuminant for changes of the scene illumination, and augment the virtual objects onto the real image with the proper highlighting and shadows. The proposed method of this paper can be applied to augmented reality visualization without any previous information respecting the environmental illuminations.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.1438-1441
• /
• 2009

RFID 시스템에서 태그는 객체를 유일하게 식별하기 위한 정보를 가지고 있기 때문에 개인정보의 노출, 위치 추적 등의 프라이버시 침해를 유발할 수 있는 문제점이 있다. 태그가 다양한 목적을 위해 사용되어지는 경우 키 분배, 키 관리 등의 문제로 인해 공개키 암호화 기법이 적용될 수 있다. 공개키 암호화 기법을 이용한 기존 RFID 인증 프로토콜에서는 서버와 태그 사이에 공개키를 사전에 공유하고 있다고 가정을 하여 설계를 하였다. 하지만 하나의 태그가 다양한 목적으로 사용되는 다목적 구조에서 수동형 RFID 태그가 서로 다른 서버의 공개키를 모두 공유한다는 것은 현실적으로 불가능하다. 본 논문에서는 다목적 구조에서 XOR 연산과 리더와 태그가 사전에 공유한 마스터 키($K_m$)를 사용하여 태그에게 공개키를 안전하게 전달하며 이를 이용한 공개키 암호화 기반의 RFID 인증 프로토콜을 제안한다. 또한 제안한 인증 프로토콜은 프라이버시 침해를 유발할 수 있는 도청, 재전송 공격, 위치 추적과 같은 공격에도 안전성을 보장한다.

• The Korean Journal of Applied Statistics
• /
• v.18 no.1
• /
• pp.1-13
• /
• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.55-58
• /
• 2016

스카이라인 질의란 다수의 선택지 중 '선호될 만한(preferable)' 선택지를 요청하는 질의이다. 사용자가 검토해야하는 선택지의 수를 대폭 감소시키는 스카이라인 질의는 데이터가 폭증하는 빅데이터 환경에서 매우 유용하게 활용된다. 이러한 배경에서 대용량 데이터에 대한 스카이라인 질의를 분산 병렬 처리하는 기법이 각광을 받고 있으며, 특히 맵리듀스(MapReduce) 기반의 분산 병렬 처리 기법 연구가 활발히 진행 중이다. 맵리듀스 기반 알고리즘의 병렬성 제고를 위해서는 부하 불균등 문제 중복 계산 문제 과다한 네트워크 비용 발생 문제를 해소해야 한다. 최근 각 기반 공간분할 기법을 사용하여 부하 불균등 문제와 중복 계산 문제를 해소하는 맵리듀스 기반 스카이라인 질의 처리 기법이 제안되었으나 해당 기법은 네트워크 비용 관점에서 최적화되어있지 않다. 본 논문에서는 부하 불균등 문제와 중복 계산 문제를 해소하면서도 프루닝을 통해 네트워크 비용 절감 시킬 수 있는 새로운 맵리듀스 기반 병렬 스카이라인 질의 처리 기법인 MR-SEAP(MapReduce sample Skyline object Equality Angular Partitioning)을 제안한다. MR-SEAP에서는 데이터를 샘플링하여 샘플 스카이라인 객체를 추출한 뒤 해당 객체들을 균등 분배하는 각도를 기준으로 공간을 분할하여 스카이라인 질의를 병렬 계산하되, 샘플 스카이라인을 이용하여 다수의 객체를 사전에 프루닝함으로써 네트워크 비용을 절감한다. 본 논문에서는 다양한 데이터 수량(cardinality) 및 분포(distribution)에 따른 제안 기법의 성능을 실험 평가함으로써 제안 기법의 우수성을 검증한다.

• Journal of Convergence for Information Technology
• /
• v.11 no.9
• /
• pp.28-34
• /
• 2021

In the case of object detection using deep learning, both accuracy and real-time are required. However, it is difficult to use a deep learning model that processes a large amount of data in a limited resource environment. To solve this problem, this paper proposes an object detection model for small embedded devices. Unlike the general detection model, the model size was minimized by using a structure in which the pre-trained feature extractor was removed. The structure of the model was designed by repeatedly stacking lightweight convolution blocks. In addition, the number of region proposals is greatly reduced to reduce detection overhead. The proposed model was trained and evaluated using the public dataset PASCAL VOC. For quantitative evaluation of the model, detection performance was measured with average precision used in the detection field. And the detection speed was measured in a Raspberry Pi similar to an actual embedded device. Through the experiment, we achieved improved accuracy and faster reasoning speed compared to the existing detection method.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.12
• /
• pp.77-84
• /
• 2022

In this paper, we propose a Label Reassignment Strategy to improve the performance of an object detection algorithm. Our approach involves two stages: an inference stage and an assignment stage. In the inference stage, we perform multi-scale inference with predefined scale sizes on a trained model and re-infer masked images to obtain robust classification results. In the assignment stage, we calculate the IoU between bounding boxes to remove duplicates. We also check box and class occurrence between the detection result and annotation label to re-assign the dominant class type. We trained the YOLOX-L model with the re-annotated dataset to validate our strategy. The model achieved a 3.9% improvement in mAP and 3x better performance on AP_S compared to the model trained with the original dataset. Our results demonstrate that the proposed Label Reassignment Strategy can effectively improve the performance of an object detection model.

• Journal of Platform Technology
• /
• v.11 no.4
• /
• pp.3-12
• /
• 2023

In the post-COVID era, the importance of quarantine measures is greatly emphasized, and accordingly, research related to the detection of mask wearing conditions and prevention of other infectious diseases using deep learning is being conducted. However, research on the detection and tracking of visitors to cultural facilities to prevent the spread of diseases is equally important, so research on this should be conducted. In this paper, a convolutional neural network-based object detection model is trained through transfer learning using a pre-collected dataset. The weights of the trained detection model are then applied to a multi-object tracking model to monitor visitors. The visitor detection model demonstrates results with a precision of 96.3%, recall of 85.2%, and an F1-score of 90.4%. Quantitative results of the tracking model include a MOTA (Multiple Object Tracking Accuracy) of 65.6%, IDF1 (ID F1 Score) of 68.3%, and HOTA (Higher Order Tracking Accuracy) of 57.2%. Furthermore, a qualitative comparison with other multi-object tracking models showcased superior results for the model proposed in this paper. The research of this paper can be applied to the hygiene systems within cultural facilities in the post-COVID era.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.23 no.1
• /
• pp.123-133
• /
• 2024

Existing autonomous driving technology has been developed based on sensors attached to the vehicles to detect the environment and formulate driving plans. On the other hand, it has limitations, such as performance degradation in specific situations like adverse weather conditions, backlighting, and obstruction-induced occlusion. To address these issues, cooperative autonomous driving technology, which extends the perception range of autonomous vehicles through the support of road infrastructure, has attracted attention. Nevertheless, the real-time analysis of the 3D centroids of objects, as required by international standards, is challenging using single-lens cameras. This paper proposes an approach to detect objects and estimate the centroid of vehicles using the fixed field of view of road infrastructure and pre-measured geometric information in real-time. The proposed method has been confirmed to effectively estimate the center point of objects using GPS positioning equipment, and it is expected to contribute to the proliferation and adoption of cooperative autonomous driving infrastructure technology, applicable to both vehicles and road infrastructure.