Department of Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Germany.
IEEE Trans Pattern Anal Mach Intell. 2012 Aug;34(8):1563-75. doi: 10.1109/TPAMI.2011.263.
We present a method for densely computing local rotation invariant image descriptors in volumetric images. The descriptors are based on a transformation to the harmonic domain, which we compute very efficiently via differential operators. We show that this fast voxelwise computation is restricted to a family of basis functions that have certain differential relationships. Building upon this finding, we propose local descriptors based on the Gaussian Laguerre and spherical Gabor basis functions and show how the coefficients can be computed efficiently by recursive differentiation. We exemplarily demonstrate the effectiveness of such dense descriptors in a detection and classification task on biological 3D images. In a direct comparison to existing volumetric features, among them 3D SIFT, our descriptors reveal superior performance.
我们提出了一种在体绘制图像中密集计算局部旋转不变图像描述符的方法。这些描述符基于调和域的变换，我们通过微分算子非常有效地计算。我们表明，这种快速体素计算仅限于具有某些微分关系的基函数族。在此基础上，我们提出了基于高斯拉盖尔和球型 Gabor 基函数的局部描述符，并展示了如何通过递归微分有效地计算系数。我们通过在生物 3D 图像上的检测和分类任务，举例说明了这种密集描述符的有效性。与现有的体绘制特征（包括 3D SIFT）进行直接比较，我们的描述符显示出优越的性能。
