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The random walker algorithm is a segmentation algorithm solving the combinatorial Dirichlet problem, adapted to image segmentation by L. Grady in 2006. [16] In 2011, C. Couprie et al. proved that when the power of the weights of the graph converge toward infinity, the cut minimizing the random walker energy is a cut by maximum spanning forest. [17]
As applied in the field of computer vision, graph cut optimization can be employed to efficiently solve a wide variety of low-level computer vision problems (early vision [1]), such as image smoothing, the stereo correspondence problem, image segmentation, object co-segmentation, and many other computer vision problems that can be formulated in terms of energy minimization.
A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg [76] and been extensively tested in brain databases. These ideas for multi-scale image segmentation by linking image structures over scales have also been picked up by Florack and Kuijper. [77]
In short, once the first pixel of a connected component is found, all the connected pixels of that connected component are labelled before going onto the next pixel in the image. This algorithm is part of Vincent and Soille's watershed segmentation algorithm, [11] other implementations also exist. [12]
The use of stable image structures over scales has been furthered by Ahuja and his co-workers [10] [11] into a fully automated system. A fully automatic brain segmentation algorithm based on closely related ideas of multi-scale watersheds has been presented by Undeman and Lindeberg [12] and been extensively tested in brain databases.
Serge Beucher, one of the co-creators of the watershed algorithm, later published a paper about it called The Watershed Transformation Applied to Image Segmentation. This paper includes the following: Consider again an image f as a topographic surface and define the catchment basins of f and the watershed lines by means of a flooding process.
Clear Note extended Note Capture by using a watershed image segmentation algorithm to separate out the foreground writing from the background surface. This allowed it to convert the background to white, producing an image that was more suitable for printing. [2]
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