module-skimage.filters.rank

Module: filters.rank

skimage.filters.rank.autolevel(image, selem) Auto-level image using local histogram.
skimage.filters.rank.autolevel_percentile(...) Return greyscale local autolevel of an image.
skimage.filters.rank.bottomhat(image, selem) Local bottom-hat of an image.
skimage.filters.rank.enhance_contrast(image, ...) Enhance contrast of an image.
skimage.filters.rank.enhance_contrast_percentile(...) Enhance contrast of an image.
skimage.filters.rank.entropy(image, selem[, ...]) Local entropy.
skimage.filters.rank.equalize(image, selem) Equalize image using local histogram.
skimage.filters.rank.geometric_mean(image, selem) Return local geometric mean of an image.
skimage.filters.rank.gradient(image, selem) Return local gradient of an image (i.e.
skimage.filters.rank.gradient_percentile(...) Return local gradient of an image (i.e.
skimage.filters.rank.maximum(image, selem[, ...]) Return local maximum of an image.
skimage.filters.rank.mean(image, selem[, ...]) Return local mean of an image.
skimage.filters.rank.mean_bilateral(image, selem) Apply a flat kernel bilateral filter.
skimage.filters.rank.mean_percentile(image, ...) Return local mean of an image.
skimage.filters.rank.median(image, selem[, ...]) Return local median of an image.
skimage.filters.rank.minimum(image, selem[, ...]) Return local minimum of an image.
skimage.filters.rank.modal(image, selem[, ...]) Return local mode of an image.
skimage.filters.rank.noise_filter(image, selem) Noise feature.
skimage.filters.rank.otsu(image, selem[, ...]) Local Otsu’s threshold value for each pixel.
skimage.filters.rank.percentile(image, selem) Return local percentile of an image.
skimage.filters.rank.pop(image, selem[, ...]) Return the local number (population) of pixels.
skimage.filters.rank.pop_bilateral(image, selem) Return the local number (population) of pixels.
skimage.filters.rank.pop_percentile(image, selem) Return the local number (population) of pixels.
skimage.filters.rank.subtract_mean(image, selem) Return image subtracted from its local mean.
skimage.filters.rank.subtract_mean_percentile(...) Return image subtracted from its local mean.
skimage.filters.rank.sum(image, selem[, ...]) Return the local sum of pixels.
skimage.filters.rank.sum_bilateral(image, selem) Apply a flat kernel bilateral filter.
skimage.filters.rank.sum_percentile(image, selem) Return the local sum of pixels.
skimage.filters.rank.threshold(image, selem) Local threshold of an image.
skimage.filters.rank.threshold_percentile(...) Local threshold of an image.
skimage.filters.rank.tophat(image, selem[, ...]) Local top-hat of an image.
skimage.filters.rank.windowed_histogram(...) Normalized sliding window histogram
doc_scikit_image
2017-01-12 17:22:15
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