• Title/Summary/Keyword: Zhang Suen Thinning

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Obtaining 1-pixel Width Line Using an Enhanced Parallel Thinning Algorithm (병렬 세선화 알고리즘을 이용한 1-화소 굵기의 선 구하기)

  • Kwon, Jun-Sik
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.46 no.1
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    • pp.1-6
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    • 2009
  • A Thinning algorithm is a very Important factor in order to recognize the character, figure, and drawing. Until comparatively lately, the thinning algorithm was proposed by various methods. In this paper, we ascertain the point at issue of ZS(Zhang and Suen), LW(Lu and Wang) and WHF(Wang, Hui and Fleming) algorithms that are the parallel thinning algorithms. The parallel thinning algorithm means the first processing doesn't have to influence to the second processing. ZS algorithm has a problem which loses pixels in slanting lines and LW algorithm doesn't have one pixel width in slanting lines. So I propose an advanced parallel thinning algorithm that connects the pixels each other and preserve the end point.

Seam Carving based Occlusion Region Compensation Algorithm (심카빙 기반 가려짐 영역 보상 기법)

  • An, Jae-Woo;Yoo, Ji-Sang
    • Journal of Broadcast Engineering
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    • v.16 no.4
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    • pp.573-583
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    • 2011
  • In this paper, we propose an occlusion compensation algorithm which is used for virtual view generation. In general, since occlusion region is recovered from neighboring pixels by taking the mean value or median value of neighbor pixels, the visual characteristics of a given image are not considered and consequently the accuracy of the compensated occlusion regions is not guaranteed. To solve these problem, we propose an algorithm that considers primary visual characteristics of a given image to compensate the occluded regions by using seam carving algorithm. In the proposed algorithm, we first use Sobel mask to obtain the edge map of a given image and then make it binary digit 0 or 1 and finally thinning process follows. Then, the energy patterns of original and thinned edge map obtained by the modified seam carving method are used to compensate the occlusion regions. Through experiments with many test images, we verify that the proposed algorithm performed better than conventional algorithms.