• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권10호
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR)은 도형이나 사물 등의 모양을 인식하는 기술로 문자인식, 모션인식, 얼굴인식, 상황인식 등의 기반이 되는 기술이다. 하지만 일반적인 SCR은 Shape의 모든 contour에 대해 히스토그램을 만들고 Shape A, B 비교를 위해 추출된 contour를 1:1 개수대로 매핑함으로써 처리속도가 느리다는 단점이 있다. 따라서 본 논문에서는 Shape 모양에 따라 윤곽선을 찾고 개량 DP 알고리즘 및 해리스코너 검출기를 이용하여 contour를 최적화시킴으로써 간략하면서도 더 효과적인 알고리즘을 만들었다. 이렇게 개선된 방법을 사용함으로써 기존방법보다 처리 수행속도가 빨라짐을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1562-1578
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• 2020

Vector data compression algorithm can meet requirements of different levels and scales by reducing the data amount of vector graphics, so as to reduce the transmission, processing time and storage overhead of data. In view of the fact that large threshold leading to comparatively large error in Douglas-Peucker vector data compression algorithm, which has difficulty in maintaining the uncertainty of shape features and threshold selection, a segmented Douglas-Peucker algorithm based on node importance is proposed. Firstly, the algorithm uses the vertical chord ratio as the main feature to detect and extract the critical points with large contribution to the shape of the curve, so as to ensure its basic shape. Then, combined with the radial distance constraint, it selects the maximum point as the critical point, and introduces the threshold related to the scale to merge and adjust the critical points, so as to realize local feature extraction between two critical points to meet the requirements in accuracy. Finally, through a large number of different vector data sets, the improved algorithm is analyzed and evaluated from qualitative and quantitative aspects. Experimental results indicate that the improved vector data compression algorithm is better than Douglas-Peucker algorithm in shape retention, compression error, results simplification and time efficiency.

• 한국측량학회지
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• 제17권2호
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• pp.117-128
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• 1999

Douglas-Peucker 알고리듬의 원리를 충실하게 반영한 단순 tree 구조의 단순화 기법은 단순화 지표가 실제 계층적 자료구조에 명확히 내재되는 장점을 갖는다. 그러나 단순 tree 방법은 단순화 지표의 계층성이 항상 보장되지 못할 가능성을 안고 있다. 그것은 Douglas-Peucker 알고리듬의 원리가 선형 사상의 국지적 특성을 충실하게 반영하지 못하는 전역적 기법이기 때문이다. 본 연구에서는 이러한 계층적 오류를 극복하기 위해 볼록다각형 탐색기법을 활용하여 형태적 대표점을 찾아 이를 기초로 계층적 자료구조를 갖는 단순화 기법 (CALS)을 구현하였다. CALS에 의한 방법은 단순 tree 방법에서 발생한 중상위 계층의 오류를 보정하는 효과가 있기 때문에 단순 tree 구조에 비해 단순화의 공간적 정확도를 향상시킨다.

• 플랜트 저널
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• 제16권4호
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• pp.33-39
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• 2020

For several decades, productivity in construction industry has been regressed and it is inevitable to improve productivity for major EPC players. One of challenges to achieve this goal is automatically extracting information from imaged drawings. Although computer vision technique has been advanced rapidly, it is still a problem to detect pipe lines in a drawing. Earlier works for line detection have problems that detected line elements be broken into small pieces and accuracy of detection is not enough for engineers. Thus, we adopted Contour and Hough Transform algorithm and reinforced these to improve detection results. First, Contour algorithm is used with Ramer Douglas Peucker algorithm(RDP). Weakness of contour algorithm is that some blank spaces are occasionally found in the middle of lines and RDP covers them around 17%. Second, HEC Hough Transform algorithm, we propose on this paper, is improved version of Hough Transform. It adopted iteration of Hough Transform and merged detected lines by conventional Hough Transform based on Euclidean Distance. As a result, performance of Our proposed method improved by 30% than previous.

• Transactions on Electrical and Electronic Materials
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• 제17권6호
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• pp.363-368
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• 2016

Rectangular shape detection plays an important role in many image recognition systems. However, it requires continued research for its improved performance. In this study, we propose a strong rectangular shape detection algorithm, which combines the canny edge and line detection algorithms based on the perpendicularity and parallelism of a rectangle. First, we use the canny edge detection algorithm in order to obtain an image edge map. We then find the edge of the contour by using the connected component and find each edge contour from the edge map by using a DP (douglas-peucker) algorithm, and convert the contour into a polyline segment by using a DP algorithm. Each of the segments is compared with each other to calculate parallelism, whether or not the segment intersects the perpendicularity intersecting corner necessary to detect the rectangular shape. Using the perpendicularity and the parallelism, the four best line segments are selected and whether a determined the rectangular shape about the combination. According to the result of the experiment, the proposed rectangular shape detection algorithm strongly showed the size, location, direction, and color of the various objects. In addition, the proposed algorithm is applied to the license plate detecting and it wants to show the strength of the results.