Two Bayesian methods for junction classification
Two Bayesian methods for junction classification
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DOI：
10.1109/tip.2002.806242
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发表时间：
2003-03
期刊：
影响因子：
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通讯作者：
M. Cazorla;Francisco Escolano
中科院分区：
文献类型：
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作者：
M. Cazorla;Francisco Escolano
我们提出了两种贝叶斯方法的交界处分类的科纳方法演变：基于区域的方法和基于边缘的方法。我们的区域为基础的方法计算一个一维（1-D）的配置文件，其中楔被映射到均匀强度的间隔。这些区间是通过由贪婪规则驱动的增长和合并算法找到的。另一方面，我们的基于边缘的方法计算不同的配置文件，映射楔形限制的对比度峰值，这些峰值是通过阈值，然后通过非最大抑制。实验结果表明，这两种方法都比Kona方法具有更好的鲁棒性和效率，并且基于边缘的方法优于基于区域的方法。
We propose two Bayesian methods for junction classification which evolve from the Kona method: a region-based method and an edge-based method. Our region-based method computes a one-dimensional (1-D) profile where wedges are mapped to intervals with homogeneous intensity. These intervals are found through a growing-and-merging algorithm driven by a greedy rule. On the other hand, our edge-based method computes a different profile which maps wedge limits to peaks of contrast, and these peaks are found through thresholding followed by nonmaximum suppression. Experimental results show that both methods are more robust and efficient than the Kona method, and also that the edge-based method outperforms the region-based one.