• 한국컴퓨터정보학회논문지
• /
• 제22권3호
• /
• pp.45-51
• /
• 2017

In this paper, we propose a consecutive-frame super-resolution method to tackle a moving object problem. The super-resolution is a method restoring a high resolution image from a low resolution image. The super-resolution is classified into two types, briefly, single-frame super-resolution and consecutive-frame super-resolution. Typically, the consecutive-frame super-resolution recovers a better than the single-frame super-resolution, because it use more information from consecutive frames. However, the consecutive-frame super-resolution failed to recover the moving object. Therefore, we proposed an improved method via moving object detection. Experimental results showed that the proposed method restored both the moving object and the background properly.

• 반도체디스플레이기술학회지
• /
• 제17권2호
• /
• pp.67-70
• /
• 2018

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various application systems such as surveillance, military, and augmented reality. This paper proposes and implements a novel and more robust object recognition and tracking system to localize and track multiple objects from input images, which estimates target state using the likelihoods obtained from multiple CNNs. As the experimental result, the proposed algorithm is effective to handle multi-modal target appearances and other exceptions.

• International Journal of Advanced Culture Technology
• /
• 제4권4호
• /
• pp.81-86
• /
• 2016

In this paper, we propose a simple and effective vision-based tracking controller design for autonomous object tracking using multicopter. The multicopter based automatic tracking system usually unstable when the object moved so the tracking process can't define the object position location exactly that means when the object moves, the system can't track object suddenly along to the direction of objects movement. The system will always looking for the object from the first point or its home position. In this paper, PID control used to improve the stability of tracking system, so that the result object tracking became more stable than before, it can be seen from error of tracking. A computer vision and control strategy is applied to detect a diverse set of moving objects on Raspberry Pi based platform and Software defined PID controller design to control Yaw, Throttle, Pitch of the multicopter in real time. Finally based series of experiment results and concluded that the PID control make the tracking system become more stable in real time.

• 융합신호처리학회논문지
• /
• 제11권1호
• /
• pp.13-18
• /
• 2010

멀티터치 관련 연구는 전반사 장애 현상(FTIR: Frustrated Total Internal Reflection)의 원리를 기반으로 기존 방법을 이용하여 단지 구현하는 것이 대부분이다. 또한 멀티 터치점(Blob-Points) 검출이나 사용자 제스쳐 인식에 있어서 성능 향상을 위한 소프트웨어적 해법에 관한 연구는 드문 실정이다. 따라서 본 논문에서는 확산 투광(DI: Diffused Illumination) 방식을 기반으로 개선된 터치점 검출과 사용자 제스쳐 인식에 의한 멀티터치 테이블-탑 디스플레이를 구현한다. 제안된 방법은 실행 중인 어플리케이션 내의 객체들을 위한 동시 변형 멀티터치 명령을 지원하며, 제안한 사전 테스팅(Pre-Testing) 방법에 의해서 멀티 터치점 검출 과정에서 시스템 지연 시간의 감소가 가능하다. 구현된 멀티터치 테이블-탑 디스플레이 장치는 OSC(Open Sound Control) 프로토콜을 기반으로 하는 TUIO(Tangib1e User Interface Object) 환경에서 Flash AS3 어플리케이션을 제작하여 시뮬레이션 한 결과 최대 37% 시스템 지연 시간의 감소와 멀티터치 제스쳐 인식에서 성공적인 결과를 보였다.

• 대한방사선기술학회지:방사선기술과학
• /
• 제42권1호
• /
• pp.39-46
• /
• 2019

This study conducted a comparative analysis of differences between cartesian trajectory in a linear rectangular coordinate system and MultiVane trajectory in a nonlinear rectangular coordinate system axial T1 and axial T2 images using an American College of Radiology(ACR) phantom. The phantom was placed at the center of the head coil and the top-to-bottom and left-to-right levels were adjusted by using a level. The experiment was performed according to the Phantom Test Guidance provided by the ACR, and sagittal localizer images were obtained. As shown in Figure 2, slices # 1 and # 11 were scanned after placing them at the center of a $45^{\circ}$ wedge shape, and a total of 11 slices were obtained. According to the evaluation results, the image intensity uniformity(IIU) was 93.34% for the cartesian trajectory, and 93.19% for the MultiVane trajectory, both of which fall under the normal range in the axial T1 image. The IIU for the cartesian trajectory was 0.15% higher than that for the MultiVane trajectory. In axial T2, the IIU was 96.44% for the cartesian trajectory, and 95.97% for the MultiVane trajectory, which fall under the normal range. The IIU for the cartesian trajectory was by 0.47% higher than that for the MultiVane trajectory. As a result, the cartesian technique was superior to the MultiVane technique in terms of the high-contrast spatial resolution, image intensity uniformity, and low-contrast object detectability.

• 대한기계학회:학술대회논문집
• /
• 대한기계학회 2005년도 창립60주년 기념 추계 학술대회
• /
• pp.161.2-161.2
• /
• 2005

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2013년도 춘계학술대회 논문집
• /
• pp.423-424
• /
• 2013

• 한국컴퓨터정보학회논문지
• /
• 제27권10호
• /
• pp.19-28
• /
• 2022

본 논문에서는 실시간 고성능 다중 객체 추적을 수행하기 위해 최적의 TBD (Tracking-by-detection) 메커니즘을 결정할 수 있는 Detector Scheduler를 제안한다. Detector Scheduler는 서로 다른 프레임 간의 특징량 차이를 측정하는 것으로 검출기 실행 여부를 결정하여 전체 추적 속도를 향상한다. 하지만, Detector Scheduler의 학습에 필요한 GT (Ground Truth) 생성이 어렵기 때문에 Detector Scheduler를 추적 결과만을 통해 학습 가능한 자가 학습 방법을 제안한다. 제안된 자가 학습 방법은 프레임 간의 객체 카디널리티와 객체 외형 특징량의 비유사도가 커질 때 검출기를 실행할 수 있도록 의사 레이블을 생성하고 제안된 손실함수를 통해 Detector Scheduler를 학습한다.

• 융합보안논문지
• /
• 제22권5호
• /
• pp.11-20
• /
• 2022

인터넷과 웹 기술이 모바일 장치 중심으로 발전하면서 이미지 데이터는 사람, 텍스트, 공간 등 다양한 유형의 민감정보를 담고 있다. 이러한 특성과 더불어 SNS 사용이 증가하면서 온라인 상의 개인정보가 노출되고 악용되는 피해 규모가 커지고 있다. 그러나 개인정보보호를 위한 다중 유형 객체 탐지 기반의 비식별화 기술에 관한 연구는 미흡한 상황이다. 이에 본 논문은 기존의 단일 유형 객체 탐지 모델을 병렬적으로 이용하여 다중 유형의 객체를 탐지 및 비식별화하는 인공지능 모델을 제안한다. Cutmix 기법을 통해 사람과 텍스트 객체가 함께 존재하는 이미지를 생성하여 학습 데이터로 구성하고, 사람과 텍스트라는 다른 특징을 가진 객체에 대한 탐지 및 비식별화를 수행하였다. 제안하는 모델은 두 가지 객체가 동시에 존재할 때 0.724의 precision과 0.745의 mAP@.5 를 달성한다. 또한, 비식별화 수행 후 전체 객체에 대해 mAP@.5 가 0.224로, 0.4 이상의 감소폭을 보였다.

• 로봇학회논문지
• /
• 제19권2호
• /
• pp.176-187
• /
• 2024

Recently, automated solutions using collaborative robots have been emerging in various industries. Their primary functions include Pick & Place, Peg in the Hole, fastening and assembly, welding, and more, which are being utilized and researched in various fields. The application of these robots varies depending on the characteristics of the grippers attached to the end of the collaborative robots. To grasp a variety of objects, a gripper with a high degree of freedom is required. In this paper, we propose a service automation system using a multi-degree-of-freedom gripper, collaborative robots, and vision sensors. Assuming various products are placed at a checkout counter, we use three cameras to recognize the objects, estimate their pose, and create grasping points for grasping. The grasping points are grasped by the multi-degree-of-freedom gripper, and experiments are conducted to recognize barcodes, a key task in service automation. To recognize objects, we used a CNN (Convolutional Neural Network) based algorithm and point cloud to estimate the object's 6D pose. Using the recognized object's 6d pose information, we create grasping points for the multi-degree-of-freedom gripper and perform re-grasping in a direction that facilitates barcode scanning. The experiment was conducted with four selected objects, progressing through identification, 6D pose estimation, and grasping, recording the success and failure of barcode recognition to prove the effectiveness of the proposed system.