调整尺度的距离变换及其应用在多式模式图像的细分上
Nirmal Das1, Subhadip Basu2, Punam K Saha3
1Department of AIML, Netaji Subhas Engineering College, Kolkata, India.
Methods (San Diego, Calif.)
|November 9, 2025
概括
这项研究引入了尺度调整距离变换 (SADT) 来分析不论物体大小的物体. 萨特为结构分析提供了一个规范化的尺度,在图像分割和分离任务中表现优于传统方法.
科学领域:
- *图像分析和计算机视觉.
- * 计算几何和拓学.
背景情况:
- * 传统的距离变换 (DT) 与多维物体的尺度变化发生冲突.
- *规模依赖的DT值限制了不同尺寸物体之间的比较结构分析.
研究的目的:
- * 引入一种新的尺度调整距离变换 (SADT),不变于对象尺度.
- * 开发一种用于在不同尺度的物体中进行规范结构分析的方法.
- * 展示SADT在图像分割和分离应用中的有效性.
主要方法:
- * 开发了一个尺度调整距离 (SAD) 度量,这是一个连续的欧几里德空间度量函数.
- * 计算方法在传统的DT场上追踪梯度流路径以检测山脊.
- *生成了一个调整尺度密度 (SAD) 字段,并从SAD值计算SADT.
主要成果:
- *SADT提供了尺度不变的规范距离场,不变于转换,旋转和同otropic 缩放.
- *将SADT应用于二维和三维多式图像数据集,在联合核细分方面表现出卓越的性能.
- *在3DCT图像中实现了高度精确的多尺度动脉-静脉分离,优于标记器控制的分水算法.
结论:
- *SADT为多维物体的结构分析提供了强大的,规模独立的方法.
- * 该方法是透明的,数据模式独立的,在图像维度上执行一致.
- *SADT为特定图像分析任务提供了深度学习方法的可行替代方案.
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