In mathematical morphology, the closing of a set () A by a structuring element B is the erosion of the dilation of that set,
where and denote the dilation and erosion, respectively.
In , closing is, together with opening, the basic workhorse of morphological noise removal. Opening removes small objects, while closing removes small holes.
It is idempotent, that is, .
It is increasing, that is, if , then .
It is extensive, i.e., .
It is translation invariant.
Image Analysis and Mathematical Morphology by Jean Serra, (1982)
Image Analysis and Mathematical Morphology, Volume 2: Theoretical Advances by Jean Serra, (1988)
An Introduction to Morphological Image Processing by Edward R.
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This course covers fundamental notions in image and video processing, as well as covers most popular tools used, such as edge detection, motion estimation, segmentation, and compression. It is compose
Mathematical morphology (MM) is a theory and technique for the analysis and processing of geometrical structures, based on set theory, lattice theory, topology, and random functions. MM is most commonly applied to s, but it can be employed as well on graphs, surface meshes, solids, and many other spatial structures. Topological and geometrical continuous-space concepts such as size, shape, convexity, connectivity, and geodesic distance, were introduced by MM on both continuous and discrete spaces.
Dilation (usually represented by ⊕) is one of the basic operations in mathematical morphology. Originally developed for , it has been expanded first to grayscale images, and then to complete lattices. The dilation operation usually uses a structuring element for probing and expanding the shapes contained in the input image. In binary morphology, dilation is a shift-invariant (translation invariant) operator, equivalent to Minkowski addition. A binary image is viewed in mathematical morphology as a subset of a Euclidean space Rd or the integer grid Zd, for some dimension d.
In mathematical morphology, opening is the dilation of the erosion of a set A by a structuring element B: where and denote erosion and dilation, respectively. Together with closing, the opening serves in computer vision and as a basic workhorse of morphological noise removal. Opening removes small objects from the foreground (usually taken as the bright pixels) of an image, placing them in the background, while closing removes small holes in the foreground, changing small islands of background into foreground.
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