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LiveWire interactive boundary extraction algorithm based on Haar wavelet transform and control point set direction search

机译:基于Haar小波变换和控制点集方向搜索的LiveWire交互式边界提取算法。

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摘要

Based on deep analysis of the LiveWire interactive boundary extraction algorithm, a new algorithm focusing on improving the speed of LiveWire algorithm is proposed in this paper. Firstly, the Haar wavelet transform is carried on the input image, and the boundary is extracted on the low resolution image obtained by the wavelet transform of the input image. Secondly, calculating LiveWire shortest path is based on the control point set direction search by utilizing the spatial relationship between the two control points users provide in real time. Thirdly, the search order of the adjacent points of the starting node is set in advance. An ordinary queue instead of a priority queue is taken as the storage pool of the points when optimizing their shortest path value, thus reducing the complexity of the algorithm from O[n~2] to O[n]. Finally, A region iterative backward projection method based on neighborhood pixel polling has been used to convert dual-pixel boundary of the reconstructed image to single-pixel boundary after Haar wavelet inverse transform. The algorithm proposed in this paper combines the advantage of the Haar wavelet transform and the advantage of the optimal path searching method based on control point set direction search. The former has fast speed of image decomposition and reconstruction and is more consistent with the texture features of the image and the latter can reduce the time complexity of the original algorithm. So that the algorithm can improve the speed in interactive boundary extraction as well as reflect the boundary information of the image more comprehensively. All methods mentioned above have a big role in improving the execution efficiency and the robustness of the algorithm.
机译:在对LiveWire交互式边界提取算法进行深入分析的基础上,提出了一种新的算法,旨在提高LiveWire算法的速度。首先,对输入图像进行Haar小波变换,并在通过输入图像的小波变换获得的低分辨率图像上提取边界。其次,通过利用用户实时提供的两个控制点之间的空间关系,基于控制点设置方向搜索来计算LiveWire最短路径。第三,预先设置起始节点的相邻点的搜索顺序。优化点的最短路径值时,将普通队列而不是优先级队列作为点的存储池,从而将算法的复杂度从O [n〜2]降低到O [n]。最后,利用基于邻域像素轮询的区域迭代后向投影方法,将Haar小波逆变换后的重建图像的双像素边界转换为单像素边界。本文提出的算法结合了Haar小波变换的优点和基于控制点集方向搜索的最优路径搜索方法的优点。前者具有快速的图像分解和重建速度,与图像的纹理特征更加吻合,后者可以降低原始算法的时间复杂度。这样,该算法可以提高交互式边界提取的速度,并更全面地反映图像的边界信息。上面提到的所有方法在提高执行效率和算法的鲁棒性方面都起着重要作用。

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