• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.901-903
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• 2005

본 논문은 스테레오 카메라를 이용한 이동 카메라 환경에서 다수의 사람을 검출하여 검출된 사람을 추적하는 방법을 제안한다. 카메라가 이동하게 되면 카메라의 움직임과 검출 대상이 되는 사람의 움직임이 동시에 발생하기 때문에 카메라 움직임을 변환 모델을 사용하여 보정하고, 독립적인 움직임을 추출하여 사람을 검출 하였다. 추적은 검출된 사람 영역의 컬러 분포에 기반하여 평균 이동(Mean Shift) 알고리즘을 적용하였다. 평균 이동 알고리즘은 빠르고 안정적인 성능으로 실시간 추적에 적합하다. 그러나 객체의 컬러 정보만으로는 배경과 컬러 분포가 유사한 객체의 경우 추적에 실패할 수 있는 단점이 있다. 이점을 보완하기 위하여 본 논문에서는 변위 지도(Disparity map)를 결합하여 객체와 배경을 분리하는 깊이 마스크를 생성하였다. 변위 지도를 사용하여 다수의 사람이 등장 할 경우 발생하는 가려짐, 겹침 등 다양한 실내 환경에서 발생하는 문제도 해결 하였다. 본 논문에서 제안하는 알고리즘은 다양한 데이터에 대해서 실험한 결과 정확한 검출과 추적에 우수한 성능을 확인 할 수 있었다.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.313-314
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• 2006

Obtained disparity map from the stereo camera by using the several stereo matching algorithms carries lots of noise because of various causes. In our approach, mode filtering and noise elimination technique using the histogram and projection-based region merging methods are adopted for improving the quality of disparity map and image segmentation. The proposed algorithms are implemented in VHDL and the real-time experimentation shows the accurately divided objects.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.256-259
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• 2017

파노라마 영상은 카메라 시야각의 제한을 극복하여 넓은 시야를 가질 수 있으므로 컴퓨터 비전, 스테레오 카메라 등의 분야에서 효율적으로 연구되고 있다. 파노라마 영상을 생성하기 위해서는 영상 스티칭 기술이 필요하다. 영상 스티칭 기술은 여러 영상에서 추출한 특징점의 디스크립터를 생성하고, 특징점들 간의 유사도를 비교하여 영상들을 이어 붙여 큰 하나의 영상으로 만드는 것이다. 각각의 특징점은 수십 수백차원의 정보를 가지고 있고, 스티칭 할 영상이 많아질수록 데이터 처리 시간이 증가하게 된다. 본 논문에서는 이를 해결 하기 위해서 전처리 과정으로 겹치는 영역이 많을 것이라고 예상되는 영상들을 그룹화 하는 방법을 제안한다. 카메라 센서 정보를 기반으로 영상들을 미리 그룹화 하여 한 번에 스티칭 할 영상의 수를 줄임으로써 데이터 처리 시간을 줄일 수 있다. 후에 계층적으로 스티칭 하여 하나의 큰 파노라마를 만든다. 실험 결과를 통해 제안한 방법이 기존의 스티칭 처리 시간 보다 짧아진 것을 검증하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.438-446
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• 2013

Generally, a camera isn't located at the center of display in a tele-presence system and it causes an incorrect eye contact between speakers which reduce the realistic feeling during the conversation. To solve this incorrect eye contact problem, we newly propose an intermediate view reconstruction algorithm using both a color camera and a depth camera and applying for the depth image based rendering (DIBR) algorithm. In the proposed algorithm, an efficient hole filling method using the arithmetic mean value of neighbor pixels and an efficient boundary noise removal method by expanding the edge region of depth image are included. We show that the generated eye-contacted image has good quality through experiments.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.4
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• pp.463-469
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• 2010

Since MOLA(Mars Orbiter Laser Altimeter) data, which provides altimetry data for Mars, does not cover the whole Mars area, image matching with MOC imagery should be implemented for the generation of DEM. However, automatic image matching is difficult because of insufficient features and low contrast. In this paper, we present the area based semi-automatic image matching algorithm with MOC-NA(Mars Orbiter Camera ? Narrow Angle) imagery. To accomplish this, seed points describing conjugate points are manually added for the stereo imagery, and interesting points are automatically produced by using such seed points. Produced interesting points being used as initial conjugate points, area based image matching is implemented. For the points which fail to match, the locations of initial conjugate points are recalculated by using matched six points and image matching process is re-implemented. The quality assessment by reversing the role of target and search image shows 97.5 % of points were laid within one pixel absolute difference.

• The Journal of Information Technology
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• v.7 no.4
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• pp.1-8
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• 2004

The depth information of 3D image lost by projecting 3D-object to 2D-screen for earning image. If depth information is restored and is used to recognize 3D-object, we can make the more effective recognition system. We often use shape from stereo algorithm in order to restore this information. In this paper, we suggest 3-D object recognition system in which the 3-D Hough transform domain is employed to represent the 3-D objects. In this system, we use the moving vector of object to reduce matching time and In second matching step, the unknown input image is compared with the reference images, which is made with octree codes. Octree codes are used in volume-based representation of a three dimensional object. The result of simulation show that the proposed 3-D object recognition system provides satisfactory performance.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.715-726
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• 2019

With the increasing prevalence of high-resolution satellite images, the need for technology to generate accurate 3D information from the satellite images is emphasized. In order to create a digital terrain model (DTM) that is widely used in applications such as change detection and object extraction, it is necessary to extract trees, buildings, etc. that exist in the digital surface model (DSM) and estimate the height of the ground. This paper presents a method for automatically generating DTM from DSM extracted from KOMPSAT-3A stereo images. The technique was developed to detect the non-ground area and estimate the height value of the ground by using the previously constructed low-resolution topographic data. The average vertical accuracy of DTMs generated in the four experimental sites with various topographical characteristics, such as mountainous terrain, densely built area, flat topography, and complex terrain was about 5.8 meters. The proposed technique would be useful to produce high-quality DTMs that represent precise features of the bare-earth's surface.

• Journal of the Korea Society of Computer and Information
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• v.11 no.3
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• pp.31-39
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• 2006

This paper presents a novel approach to real-time recognition of 3D environment and objects for various applications such as intelligent robots, intelligent vehicles, intelligent buildings,..etc. First, we establish the three fundamental principles that humans use for recognizing and interacting with the environment. These principles have led to the development of an integrated approach to real-time 3D recognition and modeling, as follows: 1) It starts with a rapid but approximate characterization of the geometric configuration of workspace by identifying global plane features. 2) It quickly recognizes known objects in environment and replaces them by their models in database based on 3D registration. 3) It models the geometric details the geometric details on the fly adaptively to the need of the given task based on a multi-resolution octree representation. SIFT features with their 3D position data, referred to here as stereo-sis SIFT, are used extensively, together with point clouds, for fast extraction of global plane features, for fast recognition of objects, for fast registration of scenes, as well as for overcoming incomplete and noisy nature of point clouds.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.181-189
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• 2004

This paper describes the method for detecting vehicles in the rear and rear-side at night by using headlight features. A headlight is the outstanding feature that can be used to discriminate a vehicle from a dark background. In the segmentation process, a night image is transformed to a binary image that consists of black background and white regions by gray-level thresholding, and noise in the binary image is eliminated by a morphological operation. In the feature extraction process, the geometric features and moment invariant features of a headlight are defined, and they are measured in each segmented region. Regions that are not appropriate to a headlight are filtered by using geometric feature measurement. In region classification, a pair of headlights is detected by using relational features based on the symmetry of a pair of headlights. Experimental results show that this method is very applicable to an approaching vehicle detection system at nighttime.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.10C
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• pp.787-797
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• 2008

In this paper, we propose a fast disparity estimation method considering temporal and spatial redundancy based on a dynamic programming for stereo matching. For the first step, the dynamic programming is performed to estimate disparity vectors with correlation between neighboring pixels in an image. Next, we efficiently compensate regions, which disparity vectors are not allocated, with neighboring disparity vectors assuming that disparity vectors in same object are quite similar. Moreover, in case of video sequence, we can decrease a complexity with temporal redundancy between neighboring frames. For performance comparison, we generate an intermediate-view image using the estimated disparity vector. Test results show that the proposed algorithm gives $0.8{\sim}2.4dB$-increased PSNR(peak signal to noise ratio) compared to a conventional block matching algorithm, and the proposed algorithm also gives approximately 0.1dB-increased PSNR and $48{\sim}68%$-lower complexity compared to the disparity estimation method based on general dynamic programming.