• 전자공학회논문지SC
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• 제44권6호
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• pp.11-20
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• 2007

기존 Hough transform을 이용한 타원 검출의 수행 속도와 개수의 추정을 개선하기 위해 본 논문에서는 선분 세그먼트 기반 Randomized Hough Transform (RHT)을 제안한다. 제안하는 방법은 에지 영상을 선분 세그먼트 단위로 분할한 후 임의의 선분 세그먼트 쌍을 RHT를 이용해서 타원을 추정하여 병합여부를 판단한다. 이와 같이 선분 세그먼트 단위로 RHT를 적용하면 적은 반복수행으로 타원을 추정할 수 있으며 복잡한 에지 영상에서도 보다 정확한 타원의 개수를 추정할 수 있다. 제안된 방법의 효율성은 계산속도 및 타원검출의 정확도로 평가하였으며 다양한 입력영상에 대한 실험을 통해 입증하였다.

• 대한기계학회논문집B
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• 제27권10호
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• pp.1508-1515
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 춘계학술대회
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• pp.1783-1788
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• 2003

In this study, the image processing program for deducing parameters of the elliptic shape of the partially overlapped liquid droplets was developed using the randomized Hough transform and the parameter decomposition. The procedure for the shape detection consists of three steps. For the first step, the candidate centers of ellipses are determined by the geometric property of the ellipse. Next, the rest parameters are estimated by the randomized Hough transform. In the final step for the post-processing, optimally approximated parameters of ellipses are determined. The developed program was applied to the simulated overlapped ellipses, real overlapped droplets, and real spray droplets. The shape detection was very excellent unless there existed inherent problems in original images. Moreover, this method can be used as an effective separating method for the overlapped small particles.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2007년도 춘계학술대회 학술발표 논문집 제17권 제1호
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• pp.157-160
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• 2007

본 논문에서는 입력 영상의 에지를 단일 세그먼트로 구성하고 같은 타원에 속하는 에지 세그먼트를 병합하여 타원검출의 속도와 정확도를 향상시키는 방법을 제안한다. 먼저 분기점은 이용한 라벨링 기법과 코너 패턴 정합 기법으로 연속된 화소들의 집합인 에지 세그먼트를 만든다. 구성된 에지 세그먼트와 Randomized Hough Transform에 의해 타원을 추정하여 병합하고 타원을 결정한다. 위 과정으로부터 얻어진 병합된 에지 세그먼트 집합 하나가 타원 하나를 구성하므로 입력 영상 내의 전체 타원의 개수를 정확하게 추정할 수 있다. 또한 전체 에지 화소들로 타원을 검출하는 기존 방법과 달리 분리된 에지 세그먼트 단위로 타원 변수를 결정하기 때문에 전체 수행시간을 크게 줄일 수 있다.

• 제어로봇시스템학회논문지
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• 제17권1호
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• pp.22-30
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• 2011

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

• 전기학회논문지
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• 제57권11호
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.

• 제어로봇시스템학회논문지
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• 제17권5호
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• pp.419-428
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• 2011

This paper suggests a judgement method for an inclined plane before entrance of it and the detection of obstacle position. Main idea is started from the assumption that obstacle is always on the bottom plane, and corner appears at this position. The process to detect the obstacle consists of three steps. First the 3D data using stereo matching is acquired to detect an obstacle. Second a bottom plane is extracted by using limit condition. Last the obstacle position is found by using Harris corner detection. Obstacle position detection on an inclined plane was verified by outdoor and indoor experiment. In error analysis, it is confirmed that an average error of obstacle detection in outdoor was larger than the error in indoor but the error are within about 0.030 m. This method will be applied to unmanned vehicles to navigate under various environment.

• Journal of Biosystems Engineering
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• 제36권5호
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• pp.377-383
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• 2011

This study proposed the new method of discerning the assembled parts and presuming the position of central point in a Cowl Cross Bar (CCB) using a Charge-Couple Device (CCD) camera attached to a robot in the auto assembly line. Three control points of an ellipse were decided by three reference points, which were equally distanced. The radii of these reference points were determined by the size of the object, and the repeated presumption secured the precise determination. The comparison of the central point of ellipse presumed by Randomized Hough Transform (RHT) with the part information stored in a database was used for determining the faulty part in an assembly. The method proposed in this study was applied for the real-time inspection of elliptical parts, such as bolt, nut hole and so on, connected to a CCB using a CCD camera. The findings of this study showed that the precise decision on whether the parts are inferior or not can be made irrespective of the lighting condition of industrial site and the noises of the surface of the part. In addition, the defect decision on the individual elliptic parts assembled in a CCB showed more than 98% accuracy within a 500-millisecond period at most.

• 한국멀티미디어학회논문지
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• 제17권7호
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• pp.818-828
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• 2014

After emerging of Microsoft Kinect, the interest in three-dimensional (3D) depth image was significantly increased. Depth image data of an object can be converted to 3D coordinates by simple arithmetic calculation and then can be reconstructed as a 3D model on computer. However, because the surface coordinates can be acquired only from the front area facing Kinect, total solid which has a closed surface cannot be reconstructed. In this paper, 3D registration method for multiple Kinects was suggested, in which surface information from each Kinect was simultaneously collected and registered in real time to build 3D total solid. To unify relative coordinate system used by each Kinect, 3D perspective transform was adopted. Also, to detect control points which are necessary to generate transformation matrix, 3D randomized Hough transform was used. Once transform matrices were generated, real time 3D reconstruction of various objects was possible. To verify the usefulness of suggested method, human arms were 3D reconstructed and the volumes of them were measured by using four Kinects. This volume measuring system was developed to monitor the level of lymphedema of patients after cancer treatment and the measurement difference with medical CT was lower than 5%, expected CT reconstruction error.

• 대한원격탐사학회지
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• 제31권2호
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• pp.111-125
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• 2015

본 연구에서는 고해상도 위성영상인 TerraSAR-X와 WorldView-2 등을 융합하여 표적의 특성을 고려한 표적군(Group of targets) 검출을 수행하였다. 관심 대상으로 하는 표적은 고정되어 있으며, 군(Group)을 이루고 있는 특징이 있다. 표적 후보를 검출하기 위해 대상 물체의 레이더 후방산란 특성을 이용한 Constant False Alarm Rate (CFAR) 알고리즘을 적용하였다. 검출된 표적 후보군으로부터 비표적을 제거하기 위해 표적의 크기 정보를 이용한 화소 클러스터링, 표적군을 이루는 표적들간의 배치 특성을 이용한 네트워크 클러스터링. 그리고 SAR 간섭기법 적용이 가능한 간섭쌍이 있는 경우 긴밀도 정보를 이용하였다. 또한, 오경보(False Alarm)를 감소시키고 최종 표적을 결정하기 위해, 표적의 형태 정보를 추출할 수 있는 Electro-Optical (EO) 영상을 바탕으로 효과적인 타원 검출 기법을 개발하였다. 개발된 표적군 검출 알고리즘을 10개 지역에 적용한 결과, 표적군 검출율은 100%, 단일 표적에 대한 오경보율은 0.03~0.3개/$km^2$, 평균 오경보는 1.8군/$64km^2$로 낮은 오경보와 높은 검출율을 보이며 표적군이 검출되었다. 본 연구에서 개발된 표준화된 표적 검출 기법은 향후 무인화된 표적 검출 시스템 구축에 핵심적인 기술이 될 것으로 전망한다.