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178:: A sequence B (b1, b2, ..., bk) of boundary pixels i.e. the contour. Define M(a) to be the Moore neighborhood of pixel a. Let p denote the current boundary pixel. Let c denote the current pixel under consideration i.e. c is in M(p). Let b denote the backtrack of c (i.e. neighbor pixel of p that was previously tested)
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The original termination condition was to stop after visiting the start pixel for the second time. This limits the set of contours the algorithm will walk completely. An improved stopping condition proposed by Jacob
Eliosoff is to stop after entering the start pixel for the second time in the same
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The idea behind the formulation of Moore neighborhood is to find the contour of a given graph. This idea was a great challenge for most analysts of the 18th century, and as a result an algorithm was derived from the
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The concept can be extended to higher dimensions, for example forming a 26-cell cubic neighborhood for a cellular automaton in three dimensions, as used by
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The Moore neighborhood is composed of nine cells: a central cell and the eight cells which surround it.
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It is one of the two most commonly used neighborhood types, the other one being the
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174:: A square tessellation, T, containing a connected component P of black cells.
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and is composed of a central cell and the eight cells that surround it.
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The Moore neighbourhood of a cell is the cell itself and the cells at a
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Cellular automaton neighborhood consisting of eight adjacent cells
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b = the pixel from which s was entered during the image scan.
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209:a black pixel, s, of P is found. Insert s in B.
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136:In two dimensions, the number of cells in an
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168:for the Moore-Neighbor tracing algorithm is
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126:{\displaystyle 0\leq d,d\in \mathbb {Z} }
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213:the current boundary point p
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205:right scan the cells of T
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326:"Moore Neighborhood"
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331:MathWorld
271:end While
152:Algorithm
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288:See also
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268:end If
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