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This flooding process is performed on the gradient image, i.e. the basins should emerge along the edges. Normally this will lead to an over-segmentation of the image, especially for noisy image material, e.g. medical CT data. Either the image must be pre-processed or the regions must be merged on the basis of a similarity criterion afterwards.
Moreover, by proceeding with the watershed analogy beyond the delimiting saddle point, a grey-level blob tree was defined to capture the nested topological structure of level sets in the intensity landscape, in a way that is invariant to affine deformations in the image domain and monotone intensity transformations.
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.
Connected-component matrix is initialized to size of image matrix. A mark is initialized and incremented for every detected object in the image. A counter is initialized to count the number of objects. A row-major scan is started for the entire image. If an object pixel is detected, then following steps are repeated while (Index !=0)
Watershed delineation is the process of identifying the boundary of a watershed, also referred to as a catchment, drainage basin, or river basin.It is an important step in many areas of environmental science, engineering, and management, for example to study flooding, aquatic habitat, or water pollution.
Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution . The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by *.
An image segmentation neural network can process small areas of an image to extract simple features such as edges. [81] Another neural network, or any decision-making mechanism, can then combine these features to label the areas of an image accordingly. A type of network designed this way is the Kohonen map.
The process of finding a drainage boundary is referred to as watershed delineation. Finding the area and extent of a drainage basin is an important step in many areas of science and engineering. Most of the water that discharges from the basin outlet originated as precipitation falling on the basin. [11]