Cris' Image Analysis Blog

Some time ago I wrote about how to compute the Feret diameters of a 2D object based on the chain code of its boundary. The diameters we computed were the longest and shortest projections of the object. The shortest projection, or smallest Feret diameter, is equivalent to the size measured when physically passing objects through sieves (i.e. sieve analysis, as is often done, e.g., with rocks). The longest projection, or largest Feret diameter, is useful as an estimate of the length of elongated objects.

The algorithm I described then simply rotated the object in two-degree intervals, and computed the projection length at each orientation. The problem with this algorithm is that the width estimated for very elongated objects is not very accurate: the orientation that produces the shortest projection could be up to 1 degree away from the optimal orientation, meaning that the estimated width is length⋅sin(π/180) too large. This doesn’t sound like much, but if the aspect ratio is 100, meaning the length is 100 times the width, we can overestimate the width by up to 175%!

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Last month I wrote a post showing how to calculate the perimeter of an object using its chain code. In this post I want to review several more measures that can be easily obtained from the chain codes: the minimum bounding box; the object’s orientation, maximum length and minimum width; and the object’s area. The bounding box and area are actually easier computed from the binary image, but if one needs to extract the chain code any way (for example to compute the perimeter) then it’s quite efficient to use the chain code to compute these measures, rather than using the full image. To obtain the chain codes, one can use the algorithm described in the previous post, or the DIPimage function dip_imagechaincode.

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