Data structures for pattern recognition algorithms: a case study
Experiences gained while programming several pattern recognition algorithms in the languages ALGOL, FORTRAN, PL/1, and PASCAL are described. The algorithms discussed are for boundary encodings of two-dimensional binary pictures, calculating and exploring the minimum spanning tree for a set of points, recognizing dotted curves from a set of planar points, and performing a template matching in the presence of severe noise distortions. The lesson seems to be that pattern recognition algorithms require a range of data structuring capabilities for their implementation, in particular, arrays, graphs, and lists. The languages PL/1 and PASCAL have facilities to accomodate graphs and lists, but there are important differences for the programmer. The ease with which the template matching program was written, debugged, and modified during a 3 week period, by using PASCAL, suggests that this small but powerful language should not be overlooked by those researchers who need a quick, reliable, and efficient implementation of a pattern recognition algorithm requiring graphs, lists, and arrays. 5 figures.
- Research Organization:
- Stanford Linear Accelerator Center, Calif. (USA)
- OSTI ID:
- 7081486
- Report Number(s):
- SLAC-PUB-1566; CONF-7505143-1
- Country of Publication:
- United States
- Language:
- English
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