Image segmentation: Difference between revisions

Revision as of 04:24, 5 April 2005

Revision as of 04:31, 5 April 2005

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The goal of segmentation is typically to locate certain ''objects of interest'' which may be depicted in the image. Segmentation could therefore be seen as a [[computer vision]] problem. Unfortunately, many important segmentation algorithms are too simple to solve this problem accurately: they compensate for this limitation with their predictability, generality, and efficiency.

A simple example of segmentation is [[Thresholding (image processing)|thresholding]] a [[grayscale]] image with a fixed threshold ''t'': each pixel ''p'' is assigned to one of two classes, ''P₀'' or ''P₁'', depending on whether ''I(p) < t'' or ''I(p) ≥ t''.

Some other segmentation algorithms are based on segmenting images into regions of similar texture according to [[Wavelet|wavelet]] or [[Fourier transform|Fourier]] transforms.