Computer Vision Systems
Computer Vision Systems
复制标题
计算机视觉系统
DOI：
10.1007/978-3-540-79547-6_39
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Suganthan S
中科院分区：
文献类型：
--
作者：
Suganthan S
图像数据的多尺度特征提取已经研究多年。最近，处理包含不规则分布数据的图像的问题变得突出。我们提出了一种多尺度拉普拉斯方法，可以直接应用于不规则分布的数据，特别是我们专注于不规则分布的三维距离数据。我们的结果表明，该方法在不规则分布的范围内工作得很好，并且在距离数据上使用拉普拉斯算子比在强度数据上使用等效算子对噪声的敏感性要小得多。
Multiscale feature extraction in image data has been investigated for many years. More recently the problem of processing images containing irregularly distribution data has became prominent. We present a multiscale Laplacian approach that can be applied directly to irregularly distributed data and in particular we focus on irregularly distributed 3D range data. Our results illustrate that the approach works well over a range of irregular distributed and that the use of Laplacian operators on range data is much less susceptive to noise than the equivalent operators used on intensity data.