史小松, 黄勇杰, 刘永革. 甲骨拓片图像的目标自动定位算法[J]. 微电子学与计算机, 2018, 35(11): 120-123.
引用本文: 史小松, 黄勇杰, 刘永革. 甲骨拓片图像的目标自动定位算法[J]. 微电子学与计算机, 2018, 35(11): 120-123.
SHI Xiao-song, HUANG Yong-jie, LIU Yong-ge. Target Automatic Positioning Algorithm of Otube Rubbing Image[J]. Microelectronics & Computer, 2018, 35(11): 120-123.
Citation: SHI Xiao-song, HUANG Yong-jie, LIU Yong-ge. Target Automatic Positioning Algorithm of Otube Rubbing Image[J]. Microelectronics & Computer, 2018, 35(11): 120-123.

甲骨拓片图像的目标自动定位算法

Target Automatic Positioning Algorithm of Otube Rubbing Image

  • 摘要: 为了解决当前图像目标定位算法难以自动定位发生形变的目标, 且定位时间耗时长等不足, 提出一种基于稀疏活动轮廓模型的甲骨拓片图像的目标自动定位算法.对甲骨拓片图像进行分块, 对每一块进行形状估计, 在目标形状估计约束下, 利用共同勾画算法学习图像目标轮廓模型, 在待定位图中扫描出与模型匹配数值最高的区域, 将其作为定位结果, 利用基于距离约束的霍夫变换, 精确定位结果.实验结果表明, 所提算法可以有效克图像目标尺度变化大的问题, 提高了目标自动定位精度, 减少了定位耗时.

     

    Abstract: In order to solve the problem that it is difficult for the current image target localization algorithm to automatically locate the target to be deformed and the positioning time is long and so on, an automatic target localization algorithm based on sparse active contour model is proposed. The image of tortoise rubra is divided into blocks, and the shape of each block is estimated. Under the constrained target shape estimation, the common contouring algorithm is used to learn the target contour model, and the area with the highest matching value is scanned in the to-be-positioned map. Positioning results, the use of distance-based constraints Huff transform, accurate positioning results. The experimental results show that the proposed algorithm can effectively overcome the problem of large changes in the target size of the image, improve the automatic positioning accuracy of the target and reduce the time-consuming positioning.

     

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