• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.238-241
• /
• 2009

정보 과학 기술의 발전과 생활 수준의 향상이 가속화되면서 주변 환경을 구성하는 가구나 공간조차도 정보 통신과 컴퓨터 기능이 내재된 인간과 상호작용할 수 있는 개념으로 확장되고 있다. 또한 비즈니스의 규모가 방대해지면서 협업이 필요성이 증가되는 가운데 단일 사용자에게 맞춰진 표준 데스크탑을 대체할 새로운 기기와 인터페이스에 대한 요구가 증가하고 있다. 본 연구에서는 이런 패러다임에 맞춰 선호되고 있는 테이블탑 컴퓨팅을 다수의 LCD와 PC를 포함하는 분산 하드웨어 방식의 고해상도 대형 디스플레이로 구성하고, 다중 사용자의 입력을 동시적으로 처리할 수 있는 적외선 카메라를 활용한 마커 인식 기반의 탠저블 인터페이스를 개발하였다. 그리고 이 시스템에서 동작하는 고해상도 이미지 뷰어와 퍼베이스브 블록 격파 게임 응용프로그램을 구현하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2021.06a
• /
• pp.271-274
• /
• 2021

Fisheye 카메라로 촬영된 영상은 일반 영상보다 넓은 시야각을 갖는 장점으로 여러 분야에서 활용되고 있다. 그러나 fisheye 카메라로 촬영된 영상은 어안렌즈의 곡률로 인하여 영상의 중앙 부분은 팽창되고 외곽 부분은 축소되는 방사 왜곡이 발생하기 때문에 영상을 활용함에 있어서 어려움이 있다. 이러한 방사 왜곡을 보정하기 위하여 기존 영상처리 분야에서는 렌즈의 곡률을 수학적으로 계산하여 보정하기도 하지만 이는 각각의 렌즈마다 왜곡 파라미터를 추정해야 하기 때문에, 개별적인 GT (Ground Truth) 영상이 필요하다는 제한 사항이 있다. 이에 본 논문에서는 렌즈의 종류마다 GT 영상을 필요로 하는 기존 기술의 제한 사항을 극복하기 위하여, fisheye 영상만을 입력으로 하여 왜곡계수를 계산하는 딥러닝 네트워크를 제안하고자 한다. 또한, 단일 왜곡계수를 왜곡모델로 활용함으로써 layer 수를 크게 줄일 수 있는 경량화 네트워크를 제안한다.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2022.01a
• /
• pp.93-96
• /
• 2022

본 논문에서는 카메라 센서만을 이용하여 손바닥 사진을 촬영하고 추출된 데이터들을 합성하여 멀티 채널 이미지를 생성 및 분류 모델에 입력하여 신원을 확인하는 딥러닝 모델을 제안한다. 이 모델은 손바닥 사진이 입력되면 손바닥 및 손금 세그멘테이션을 이용하여 마스크 이미지를 추출하고 단일 채널로 구성된 이미지들을 멀티 채널 이미지로 합성/재구성하여 신원을 분류하는 딥러닝 모델이다. 이는 카메라 센서 외 다른 센서가 필요 없다는 장점을 가지고 있으며, 비접촉 신원 인식 시스템에 적용할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2022.11a
• /
• pp.194-196
• /
• 2022

최근 대기 오염이 심각해지면서 이와 관련된 다양한 환경 문제가 발생하고 있다. 이러한 대기 오염에 대비하기 위해서 본 논문에서는 대기 오염 데이터를 수집하고 LiDAR 카메라를 활용하여 사각 지역에서 대기 오염을 측정하는 서비스를 제안한다. 이를 위해 대기 오염 데이터의 모니터링과 LiDAR 카메라를 활용할 수 있는 장치를 구축한다.

• The KIPS Transactions:PartB
• /
• v.16B no.1
• /
• pp.35-46
• /
• 2009

Lane extraction and lane departure warning algorithms using the image sensor attached in the vehicle are addressed. With the research about intelligent automobile, there have been many algorithms about lane recognition and lane departure warning system. However, since these algorithms require to detect 2 lanes, the high time complexity and the low recognition rate under various driving circumstances are critical problems. In this paper, we present a lane departure warning algorithm using single lane extraction and center point analysis that achieves the fast processing time and high detection rate. From the geometry between camera and objects, the region of interest (ROI) is determined and splitted into two parts. Hough transform detects the part of the lane. After the detected lane is restored to have a pre-determined size, lane departure is estimated by calculating the distance from the center point. On real driving environments, the presented algorithm is compared with previous algorithms. Experiment results support that the presented algorithm is fast and accurate.

• Journal of the Korea Computer Graphics Society
• /
• v.27 no.3
• /
• pp.1-11
• /
• 2021

Slow sync is a photography technique where a user takes an image with long exposure and a camera flash to enlighten the foreground and background. Unlike short exposure with flash and long exposure without flash, slow sync guarantees the bright foreground and background in the dim environment. However, taking a slow sync image with a smartphone is difficult because the smartphone camera has continuous and weak flash and can not turn on flash if the exposure time is long. This paper proposes a deep learning method that input is a short exposure flash image and output is a slow sync image. We present a deep learning network with a weight map for spatially varying enlightenment. We also propose a dataset that consists of smartphone short exposure flash images and slow sync images for supervised learning. We utilize the linearity of a RAW image to synthesize a slow sync image from short exposure flash and long exposure no-flash images. Experimental results show that our method trained with our dataset synthesizes slow sync images effectively.

• Journal of Korea Multimedia Society
• /
• v.4 no.1
• /
• pp.37-45
• /
• 2001

The studies to estimate the surface spectral reflectance of an object have received widespread attention using the multi-spectral camera system. However, the multi-spectral camera system requires the additional color filter according to increment of the channel and system complexity is increased by multiple capture. Thus, this paper proposes an algorithm to reduce the estimation error of surface spectral reflectance with the conventional 3-band RGB camera. In the proposed method, adaptive principal components for each pixel are calculated by renewing the population of surface reflectances and the adaptive principal components can reduce estimation error of surface spectral reflectance of current pixel. To evaluate performance of the proposed estimation method, 3-band principal component analysis, 5-band wiener estimation method, and the proposed method are compared in the estimation experiment with the Macbeth Color Checker. As a result, the proposed method showed a lower mean square error between the estimated and the measured spectra compared to the conventional 3-band principal component analysis method and represented a similar or advanced estimation performance compared to the 5-band wiener method.

• Explosives and Blasting
• /
• v.38 no.2
• /
• pp.1-12
• /
• 2020

Digital Image Correlation is a well-established method for displacements, strains and shape measurements of engineering objects. Stereo-camera 3D Digital Image Correlation (3D-DIC) systems have been developed to match the specific requirements for measurements posed by material and mechanical industries. Although DIC method provides the capabilities of scaling a field-of-view(FOV), dimensions of Geotechnical structure objects in many cases are too big to be measured with DIC based on a single camera pair. It can be the most important issue with applying 3D DIC to the measurement of Geotechnical structures. In this paper, We were present stereo vision conditions in a 3D DIC system that can be measured for large FOV(30×20m) and high precisions(z-displacement 0.5mm) of the ground objects with Stereo-camera DIC systems.

• Korean Journal of Optics and Photonics
• /
• v.17 no.6
• /
• pp.537-543
• /
• 2006

A method of estimating the distance using single zoom camera limits a distance range(only optical axis) in field of view. So, in this paper, we propose a method of estimating the distance information in Stereoscopic display using the stereo zoom lens module for estimating the distance in the wide range. The binocular stereo zoom lens system is composed using a horizontal moving camera module. The left and right images are acquired in polarized stereo monitor for getting the conversion and estimating a distance. The error distance is under 10mm which has difference between optically a traced distance and an estimated distance in left and right range $(0mm{\sim}500mm)$ at center. This presents the system using a function of the zoom and conversion has more precise distance information than that of conversion control. Also, a method of estimating a distance from horizontal moving camera is more precise value than that from toe-in camera by comparing the error distance of the two camera methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.1015-1017
• /
• 2013

Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location or the shape of the object in every frame. This paper describes an object tracking system based on active vision with two cameras, into algorithm of single camera tracking system an object active visual tracking and object locked system based on Extend Kalman Filter (EKF) is introduced, by analyzing data from which the next running state of the object can be figured out and after the tracking is performed at each of the cameras, the individual tracks are to be fused (combined) to obtain the final system object track.