A thorough discussion of an optimal implementation of the Hoshen-Kopelman (Hoshen and Kopelman, 1976) cluster identification algorithm is presented below. Particular emphasis is given to the input datasets, programming data structures, the cluster neighborhood rule, and the use of a finite-state-machine (FSM). The FSM discussion focuses on the three major implementation components: the temporary label assignment, the search path compression, and the formal finite state machine.

- 1.1 Pre-Processing the ERDAS/Lan Maps
- 1.2 Data Structures
- 1.3 Neighborhood Rule
- 1.4 Implementing the Hoshen-Kopelman Algorithm
- 1.5 Finite State Machine Implementation
- 1.6 The Finite State Machine

Michael W. Berry (berry@cs.utk.edu)

Sat Mar 30 23:40:13 EST 1996