King-Sun Fu

A survey on image segmentation

Abstract For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection.

A distance measure between attributed relational graphs for pattern recognition

A method to determine a distance measure between two nonhierarchical attributed relational graphs is presented. In order to apply this distance measure, the graphs are characterised by descriptive graph grammars (DGG). The proposed distance measure is based on the computation of the minimum number of modifications required to transform an input graph into the reference one. Specifically, the distance measure is defined as the cost of recognition of nodes plus the number of transformations which include node insertion, node deletion, branch insertion, branch deletion, node label substitution and branch label substitution. The major difference between the proposed distance measure and the other ones is the consideration of the cost of recognition of nodes in the distance computation. In order to do this, the principal features of the nodes are described by one or several cost functions which are used to compute the similarity between the input nodes and the reference ones. Finally, an application of this distance measure to the recognition of lower case handwritten English characters is presented.

Error-Correcting Isomorphisms of Attributed Relational Graphs for Pattern Analysis

The pattern deformational model proposed by Tsai and Fu [11] is extended so that numerical attributes and probability or density distributions can be introduced into primitives and relations in a nonhierarchical relational graph. Conventional graph isomorphisms are then generalized to include error-correcting capability for matching deformed patterns represented by such attributed relational graphs. An ordered-search algorithm is proposed for determining error-correcting isomorphisms. Finally, a pattern classification approach using graph isomorphisms is described, which can be considered as a combination of structural and statistical techniques.

Grammatical Inference: Introduction and Survey-Part II

The problem of grammatical inference is introduced, and its potential engineering applications are demonstrated. Inference algorithms for finite-state and context-free grammars are presented. The application of some of the algorithms to the inference of pattern grammars in syntactic pattern recognition is illustrated by examples.

Attributed Grammar-A Tool for Combining Syntactic and Statistical Approaches to Pattern Recognition

Attributed grammars are defned from the pattern recognidon point of view and shown to be useful for descriptions of syntactic stuctures as well as semantic attributes in primitives, subpatterns, and patterns. A pattern analysis system using attributed grammars Is proposed for pattern classification and description. This system extracts primitives and their attributes after preprocessing, performs syntax analysis of the resulting pattern representations, computes and extracts subpattern attributes for syntactically accepted patterns, and finally makes decisions according to the Bayes decision rule. Such a system uses a combination of syntactic and statistical pattern recognition techniques, as is demonstrated by illustrative examples and experimental results.

Learning control systems--Review and outlook

The basic concept of learning control is introduced. The following five learning schemes are briefly reviewed: 1) trainable controllers using pattern classifiers, 2) reinforcement learning control systems, 3) Bayesian estimation, 4) stochastic approximation, and 5) stochastic automata models. Potential applications and problems for further research in learning control are outlined.

A Sentence-to-Sentence Clustering Procedure for Pattern Analysis

Cluster analysis for patterns represented by sentences is investigated. The similarity between patterns is expressed in terms of the distance between their corresponding sentences. A weighted distance between two strings is defined and its probabilistic interpretation given. The class membership of an input pattern (sentence) is determined according to the nearest neighbor or k-nearest neighbor rule. A clustering procedure on a sentence-to-sentence basis is proposed. A set of English characters is used to illustrate the proposed metric and clustering procedure.

A Formulation of Fuzzy Automata and Its Application as a Model of Learning Systems

Based on the concept of fuzzy sets defined by Zadeh, a class of fuzzy automata is formulated similar to Mealys formulation of finite automata. A fuzzy automaton behaves in a deterministic fashion. However, it has many properties similar to that of stochastic automata. Its application as a model of learning systems is discussed. A nonsupervised learning scheme in automatic control and pattern recognition is proposed with computer simulation results presented. An advantage of employing fuzzy automaton as a learning model is its simplicity in design and computation.

A parallel thinning algorithm for 3-D pictures

Abstract A parallel algorithm for three-dimensional object thinning is presented. The concept of connectivity in 3-D digital pictures is studied, and two approaches, path connectivity and surface connectivity, are discussed. Criteria to avoid excessive deletion and preserve connectivity are described. The implementation of the algorithm and examples are provided.

Shape Discrimination Using Fourier Descriptors

Description or discrimination of boundary curves (shapes) is an important problem in picture processing and pattern recognition Fourier descriptors (FDs) have interesting properties in this respect. First, a critical review is given of two kinds of FDs. Some properties of the FDs are given and a distance measure is proposed, in terms of FDs, that measures the difference between two boundarv curves. It is shown how FDs can be used for obtaining skeletons fobjects. Finally, experimental results are given in character recognition and machine parts recognition.