论文标题
使用离散的ra rantransation进行灰度图像矩
Using the discrete radon transformation for grayscale image moments
论文作者
论文摘要
图像矩是给定图像中像素值的加权总和，并用于对象检测和定位。原始图像矩直接源自图像，并且在推导时刻不变的数量中至关重要。原始图像矩的当前一般算法在计算上是昂贵的，并且乘以图像中的像素数所需的比例。对于大小（N，M）的图像，它具有O（nm）乘法。在本文中，我们使用离散的ra转换来概述算法来计算灰度图像的原始图像矩。它将二维计算的计算降低为一维力矩计算的线性组合。我们表明，乘法的数量需要比例为O（N + M），使其比最广泛使用的原始图像矩算法更快。
Image moments are weighted sums over pixel values in a given image and are used in object detection and localization. Raw image moments are derived directly from the image and are fundamental in deriving moment invariants quantities. The current general algorithm for raw image moments is computationally expensive and the number of multiplications needed scales with the number of pixels in the image. For an image of size (N,M), it has O(NM) multiplications. In this paper we outline an algorithm using the Discrete Radon Transformation for computing the raw image moments of a grayscale image. It reduces two dimensional moment calculations to linear combinations of one dimensional moment calculations. We show that the number of multiplications needed scales as O(N + M), making it faster then the most widely used algorithm of raw image moments.
