Rafael C. Gonzalez

Bio: Rafael C. Gonzalez is an academic researcher from University of Tennessee. The author has contributed to research in topics: Digital image processing & Tree-adjoining grammar. The author has an hindex of 16, co-authored 37 publications receiving 12625 citations.

Digital Image Processing Using MATLAB

Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.

Pattern recognition principles

Abstract: The present work gives an account of basic principles and available techniques for the analysis and design of pattern processing and recognition systems. Areas covered include decision functions, pattern classification by distance functions, pattern classification by likelihood functions, the perceptron and the potential function approaches to trainable pattern classifiers, statistical approach to trainable classifiers, pattern preprocessing and feature selection, and syntactic pattern recognition.

Digital Image Processing 3rd Edition

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Data fusion in robotics and machine intelligence

Abstract: Data fusion and sensor integration - state-of-the-art 1990s, R.C. Luo and M.G. Gay multi-source spatial fusion using Bayesian reasoning, A. Elfes multi-sensor strategies using Dempster/Shafer belief accumulation, S.A. Hutchinson and A.C. Kak data fusion techniques using robust statistics, R. McKendall and M. Mintz recursive fusion operators - desirable properties and illustrations, Y. Chen and R.L. Kashyap distributed data fusion using Kalman filtering - a robotics application, C. Brown, et al kinematic and satistical models for data fusion using Kalman filtering, T.J. Broida and S.S. Blackman least-squares fusion of multi-sensory data, R.O. Eason and R.C. Gonzalez fusion of multi-dimensional data using regularization, M.A. Abidi geometric fusion - minimizing uncertainty ellipsoid volumes, Y. Nakamura combination of fuzzy information in the framework of possibility theory, D. Dubois and H. Prade data fusion - a neural networks implementation, T.L. Huntsberger.

Syntactic Pattern Recognition: An Introduction

Abstract: This book provides an introduction to basic concepts and techniques of syntactic pattern recognition. The presentation emphasizes fundamental and practical material rather than strictly theoretical topics, and numerous examples illustrate the principles. The subject is developed according to the following topics: introduction (background, patterns and pattern classes, approaches to pattern recognition, elements of a pattern recognition system, concluding remarks); elements of formal language theory (introduction; string grammars and languages; examples of pattern languages and grammars; equivalent context-free grammars; syntax-directed translations; deterministic, nondeterministic, and stochastic systems; concluding remarks); higher-dimensional grammars (introduction; tree grammars; web grammars; plex grammars; shape gammars; concluding remarks); recognition and translation of syntactic structures (introduction; string language recognizers; automata for simple syntax-directed translation; parsing in string languages; recognition of imperfect strings; tree automata; concluding remarks); stochastic grammars, languages, and recognizers (introduction; stochastic grammars and languages; consisting of stochastic context-free grammars; stochastic reocgnizers; stochastic syntax-directed translations; modified Cocke-Younger-Kasami parsing algorithm for stochastic errors of changed symbols; concluding remarks); and grammatical inference (introduction; inference of regular grammars; inference of context-free grammars; inference of tree grammars; inference of stochastic grammar; concluding remarks). 155 references, 93 figures, 4 tables. (RWR)

A Cluster Separation Measure

Abstract: A measure is presented which indicates the similarity of clusters which are assumed to have a data density which is a decreasing function of distance from a vector characteristic of the cluster. The measure can be used to infer the appropriateness of data partitions and can therefore be used to compare relative appropriateness of various divisions of the data. The measure does not depend on either the number of clusters analyzed nor the method of partitioning of the data and can be used to guide a cluster seeking algorithm.

Survey over image thresholding techniques and quantitative performance evaluation

Abstract: We conduct an exhaustive survey of image thresholding methods, categorize them, express their formulas under a uniform notation, and finally carry their performance comparison. The thresholding methods are categorized according to the information they are exploiting, such as histogram shape, measurement space clustering, entropy, object attributes, spatial correlation, and local gray-level surface. 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images. The comparison is based on the combined performance measures. We identify the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications. © 2004 SPIE and IS&T. (DOI: 10.1117/1.1631316)