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首页> 外文期刊>Journal of Computers >A Robust Thresholding Algorithm Framework based on Reconstruction and Dimensionality Reduction of the Three Dimensional Histogram
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A Robust Thresholding Algorithm Framework based on Reconstruction and Dimensionality Reduction of the Three Dimensional Histogram

机译：一种基于重构和三维直方图减少的鲁棒阈值算法框架

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摘要

—In this work, a robust thresholding algorithm framework based on reconstruction and dimensionality reduction of the three-dimensional (3-D) histogram is proposed with the consideration of the poor anti-noise performance in existing 3-D histogram-based segmentation methods due to the obviously wrong region division. Firstly, our method reconstructs the 3-D histogram based on the distribution of noisy points which reduce its segmentation performance. Secondly, we transfer the region division in 3- D histogram from eight partitions into two parts, thus reducing the searching space of threshold from 3-dimension to 1-dimension, which saves a lot of processing time and memory space. Thirdly, we apply the presented framework to global thresholding algorithms such as Otsu method, minimum error method, and maximum entropy method and so on, and propose corresponding robust global thresholding algorithms. Finally, segmentation result and running time are given at the end of this paper compared with those of 3-D Otsu’s method, Otsu method, minimum error method and maximum entropy method. The experimental results show that the presented method has better anti-noise performance and visual quality compared with the above four approaches, and has lower time complexity than 3-D Otsu’s method.
机译：在这项工作中，考虑到基于3-D基于直方图的分段方法的良差，基于重建和三维直方图的重建和维度降低的鲁棒阈值算法框架，提出了一种基于三维基于直方图的分割方法的差的抗噪声性能到明显的错误区域师。首先，我们的方法基于减少其分割性能的噪声点的分布来重建三维直方图。其次，我们将3-D直方图中的区域划分从8个分区转换为两个部分，从而将阈值的搜索空间从3维度降低到1维度，从而节省了大量处理时间和存储空间。第三，我们将呈现的框架应用于全局阈值化算法，例如OTSU方法，最小误差方法和最大熵方法等，并提出了相应的强大的鲁棒全局阈值算法。最后，与3-D OTSU的方法，OTSU方法，最小误差方法和最大熵方法相比，本文结束时给出了分割结果和运行时间。实验结果表明，与上述四种方法相比，该方法具有更好的抗噪声性能和视觉质量，并且具有比三顿OTSU的方法更低的时间复杂性。

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