• 한국컴퓨터정보학회논문지
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• 제11권2호
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• pp.83-92
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• 2006

본 논문에서는 영역기반의 스테레오 정합을 사용하여 영상의 변이를 추정하는 방법에 대해 기술한다. 영역기반의 변이 추정은 분할된 영역단위로 변이값을 계산하는데 스테레오 정합 단계의 정합오류뿐만 아니라 정합된 영역의 유형을 고려하지 않고 일률적인 방법으로 변이를 계산하기 때문에 부정확한 변이를 추정하게 되는 문제점을 가진다. 이런 문제점을 해결하기 위해 본 논문에서는 정합된 영역의 유형을 고려하여 변이를 추정하는 방법을 제안한다. 제안하는 방법은 스테레오 정합 수행 후 정합영역의 유형을 분석하여 유사정합, 비유사정합, 오정합, 비정합 영역으로 분류한 다음 분류된 정합영역별로 적절한 변이 추정 방법을 적용한다. 이 방법은 정합오류로 인한 잘못된 변이 추정을 최소화하며 정상적인 정합영역에 대해서도 변이의 정확도를 향상시킨다. 제안하는 방법의 성능을 평가하기 위해 다양한 장면에 대해 실험을 하였으며 실험결과 다양한 영상에서 정확도가 향상된 변이도를 얻을 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1403-1406
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• 1997

Generally, G.P.S(Global Positioning System) is using for the car navigation system but it has some restrictions such as the discontinuity of earth satellites and SA (Selective Availability). Recently, the hybrid navigation system combining with G.P.S and Dead-reckoning are much attractuve for improving the accuracy of a vehicle positioning. G.P.S called satellite navigation system, can measure its position by using satellites. Dead-Reckoning is the self-contained navigatioin system using a wheel sensor for the vehicle velocity and a gyro sensor for the vehicle angular velocity. Some algorithm could be generated for finding the vehicle position and orientation. In this paper, we developed a hybrid algotithm wiht G.P.S DR and Map-Matching.

• 전자공학회지
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• 제23권12호
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• IEIE Transactions on Smart Processing and Computing
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• 제2권5호
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• pp.317-321
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• 2013

This paper proposes a detection algorithm for view reversal in a stereoscopic video using a disparity map and motion vector field. We obtain the disparity map of a stereo image was obtained using a specific stereo matching algorithm and classify the image into the foreground and background. Next, the motion vector field of the image on a block basis was produced using a full search algorithm. Finally, the stereo image was considered to be reversed when the foreground moved toward the background and the covered region was in the foreground. The proposed algorithm achieved a good detection rate when the background was covered sufficiently by its moving foreground.

• IEIE Transactions on Smart Processing and Computing
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• 제6권3호
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• pp.175-182
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• 2017

In this paper, we present a multi-depth generation method using a time-of-flight (ToF) fusion camera system. Multi-view color cameras in the parallel type and ToF depth sensors are used for 3D scene capturing. Although each ToF depth sensor can measure the depth information of the scene in real-time, it has several problems to overcome. Therefore, after we capture low-resolution depth images by ToF depth sensors, we perform a post-processing to solve the problems. Then, the depth information of the depth sensor is warped to color image positions and used as initial disparity values. In addition, the warped depth data is used to generate a depth-discontinuity map for efficient stereo matching. By applying the stereo matching using belief propagation with the depth-discontinuity map and the initial disparity information, we have obtained more accurate and stable multi-view disparity maps in reduced time.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.27-29
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• 2004

This paper presents a sensor model design based on Monte Carlo Localization method. First, we define the measurement error of each sample using a map matching method by 2-D laser scanners and a pre-constructed grid-map of the environment. Second, samples are assigned probabilities due to matching errors from the gaussian probability density function considered of the sample's convergence. Simulation using real environment data shows good localization results by the designed sensor model.

• 전자공학회논문지S
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• 제34S권2호
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• pp.63-70
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• 1997

This paper proposes a new algorithm for recognizing polyhedral objects using a single 2-D image. It is base don a new representation scheme having two level hierarchey. In the lower level, geometrical features of each primitive surface are represented using their signatures and the variation of signature due to rotation is represented suing the rotation map. In the higher level, topological features are represented in the inter-surface description table(SDT). Based on the proposed representaton scheme, loer level database searched to find a matching primitive surface. The srotation map determines the degree of rotation as well as the matchness. If all surfaces in a test object find their matching primitive surfaces, its structural information is compared with the SDTs of object models. If primitive surfaces of a test object equal to tha tof certain model and satisfy inter-surfaces relationship in SDT, a test object is recognized as the model.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 춘계학술대회 논문집
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• pp.169-172
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• 2006

본 논문에서는 1m 해상도의 위성영상으로부터 건물의 경계선을 검출하기 위해 영상분할과 변이지도(disparity map)를 이용하는 새로운 방법을 제안한다. Watershed 방법으로 영상을 분할하고 분할된 영역 내부의 변이를 다중정합창틀(multiple matching window)과 결합된 다차원특징벡터정합(multi-dimensional feature vector matching)을 이용하여 계산한다 분할된 인접 영역들 가운데 panchromatic 및 multispectral 밝기값과 변이의 평균값이 유사하면 두 영역을 결합하여 하나의 영역을 생성하고 이 과정을 반복적으로 수행한다. 영역의 평균 변이값이 기준 값보다 크면 이를 건물 지붕 영역으로 결정한다. IKONOS 위성영상에 제안한 방법을 적용하여 작은 건물이 밀집되어 있는 도시 지역에서 건물 지붕의 영역과 경계선을 효과적으로 검출할 수 있었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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• pp.547-550
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• 2006

This study is to suggest a methodology to produce landslide hazard map by combining LRA (Logistic Regression Analysis) and AHP (Analytic Hierarchy Program) Approach. Topographic factors (slope, aspect, elevation), soil drain, soil depth and land use were adopted to classify landslide hazard areas. The method was applied to a 520 $km^2$ region located in the middle of South Korea which have occurred 39 landslides during 1999 and 2003. The suggested method showed 58.9 % matching rate for the real landslide sites comparing with the classified areas of high-risk landslide while LRA and AHP showed 46.1 % and 48.7 % matching rates respectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.920-923
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using perspective invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of the simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two parallel walls are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points(V.P). Point features for computing cross ratios are extracted robustly using a vanishing point and the intersection points between floor and the vertical lines of door frames. The robustness and feasibility of our algorithms have been demonstrated through experiments in indoor environments using an indoor mobile robot, KASIRI-II(KAist SImple Roving Intelligence).