Syntactic and Structural Pattern Recognition

I. Matching and Parsing I.- A string correction method based on the context-dependent similarity.- An error-correcting parser for a context-free language based on the context-dependent similarity.- Ordered structural matching.- II. Matching and Parsing II.- A parsing algorithm for weighted grammars and substring recognition.- Computing the minimum error distance of graphs in 0 (n3) time with precedence graph grammars.- A unified view on tree metrics.- III. Applications I.- Problems in recognition of drawings.- Application of structural pattern recognition in histopathology.- Applications of multidimensional search to structural feature identification.- IV. Grammatical Inference and Clustering.- Learning from examples in sequences and grammatical inference.- An efficient algorithm for the inference of circuit-free automata.- Voronoi trees and clustering problems.- V. Image Understanding.- Hough-space decomposition for polyhedral scene analysis.- Running efficiently arc consistency.- Smith: an efficient model-based two dimensional shape matching technique.- Training and model generation for a syntactic curve network parser.- VI. Applications II.- Knowledge-based computer recognition of speech.- Computers viewing artists at work.- Face recognition from range data by structural analysis.- Cryptosystems for picture languages.- VII. Hybrid Approaches I.- Hybrid approaches.- An AI-structural approach to edge detection.- Building hierarchies-an algorithmic approach.- VIII. Hybrid Approaches II.- Combining logic based and syntactic techniques: a powerful approach.- A syntactic approach to planning.- IX. Working Sessions.- Working Group A: 2D and 3D Image Understanding.- Working Group B: Waveform and Speech Recognition.- Working Group C: Hybrid Techniques.- Working Group D: Models and Inference.- X. Panel.- Artificial Intelligence Versus Syntactic Techniques: Theoretical and Practical Issues.- XL List of Participants.