Avtor/Urednik		Savšek, Tomaž; Pavešić, Nikola; Vezjak, Marjan
Naslov		Razpoznavanje znakov z mehkimi relacijskimi drevesi
Prevedeni naslov		Fuzzy relational trees in character recognition
Tip		članek
Vir		In: Zajc B, editor. Zbornik 6. elektrotehniške in računalniške konference ERK'97. Zvezek B. Računalništvo in informatika, umetna inteligenca, robotika, razpoznavanje vzorcev - 3. letna konferenca SDRV, biomedicinska tehnika, močnostna elektrotehnika, didaktika, študentski članki; 1997 sep 25-27; Portorož. Ljubljana: Slovenska sekcija IEEE,
Leto izdaje		1997
Obseg		str. 269-72
Jezik		slo
Abstrakt		In the past, reducing complex real-world systems into precise mathematical models was the main tendency in science and engineering. Unfortunately, real-world situations are often not so deterministic. When the knowledge about the system is uncompleted, or when the system is complex and data are apisodic rather than systematic, the principles of fuzzy set theory can be applied. On the other hand, graph theory plays an important role in the modeling of structures, especially in pattern recognition. The theory of fuzzy graphs has important links to the theory of fuzzy classfication and decision analysis. Fuzzy graphs are helpful tools for representing soft or ill-defined structures. With the increasing popularity of using hierarhical data structure and tree construction schemes, which are special class of graphs, we have to face the problem of comparing two tree-like data structures. The problem may arise in many ways: from comparing the tree structure of an input with that of the templates for classification and decision support or from sorting the data according to the similarity between tree structures for fast retrieval. These situations initiated the study of distance measures for trees. Using the idea of language transformation, a distance measure between two trees proposed by Lu. A tree can be derived from another by a series of transformation and the distance between two trees is the minimum cost sequence of transformations. In this paper we present the extension of Lu's algorithm to the fuzzy environment, where the idea of fuzzy transformations is applied. As an example of using this idea we represenst a recognition of hand-written characters and a hierarchical clustering method.
Deskriptorji		FUZZY LOGIC PATTERN RECOGNITION LINGUISTICS