| 摘要 |
A pixel of an image is classified between a first kind and a second kind by centering a sample mask on the pixel and applying each of a population of R given basis functions to the mask pixels to generate, for each basis function, a bucket of values. A probability density function is estimated for each of the bucket of values. Each of the R probability density functions is transformed to a single valued result, to generate an R-dimensional sample classification vector. The R-dimensional sample classification vector is classified against a R-dimensional first centroid vector and a R-dimensional second centroid vector, each of centroid vectors constructed in a previous training of applying the same population of R given basis functions to pixels known as being the first kind and to pixels known as being the second kind. Optionally, pixels may be conditionally classified and then finally classified based on subsequent classification of neighbor pixels.
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