โ Scribed by Changming Sun
No coin nor oath required. For personal study only.
L ocal mean and variance measures are frequently required in multi-dimensional image analysis. These measures are needed when calculating correlation coefficients for local image matching purposes. Other measures such as skewness and autocorrelation are useful for texture analysis. This paper presents a fast algorithm for calculating these local statistics in a window of an N-dimensional image. The new algorithm, which is called the plunger method, recursively reduces the dimensions of the input N-dimensional image to achieve fast computation. The speed of the algorithm is independent of the window size. Another advantage of the algorithm is that it calculates the local statistics in one pass. Real image tests have been performed.
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A novel algorithm is proposed for segmenting an image into multiple levels using its mean and variance. Starting from the extreme pixel values at both ends of the histogram plot, the algorithm is applied recursively on sub-ranges computed from the previous step, so as to find a threshold level and a
## Abstract ## Purpose To develop and implement a new approach for correcting the intensity inhomogeneity in magnetic resonance imaging (MRI) data. ## Materials and Methods The algorithm is based on the assumption that intensity inhomogeneity in MR data is multiplicative and smoothly varying. Us
A new parallel algorithm for image component labeling with local operators on SIMD mesh connected computers is presented. This algorithm provides a positive answer to the open question of whether there exists an \(O(n)\)-time and \(O(\log n)\)-space local labeling algorithm on SIMD mesh connected co
In many engineering applications it is required to compute the dominant subspace of a matrix A of dimension m ร n, with m?n. Often the matrix A is produced incrementally, so all the columns are not available simultaneously. This problem arises, e.g., in image processing, where each column of the mat