Bibliography
Clowes, M. B. On seeing things. Artificial Intelligence. 1971; 2:79–116.
Connell, J. H., Brady, M. Generating and generalizing models of visual objects. Artificial Intelligence. 1987; 3:159–183.
Guzman, A. Decomposition of a visual scene into three-dimensional bodies. AFIPS Proceedings of Fall Joint Computer Conference. 1968; 33:291–304.
Huffman, D. A., Impossible objects as nonsense sentencesMeltzer, B., Michie, D., eds. Machine Intelligence; 6. Edinburgh University Press, 1971:295–324.
Kanade, T. A theory of origami world. Artificial Intelligence. 1980; 13:279–311.
Lenat, D. B. The ubiquity of discovery. Artificial Intelligence. 1977; 9:257–285.
Marr, D., Nishihara, H. K. Representation and recognition of the spatial organization of three-dimensional structure. Proceedings of Royal Society of London. 1978; B200:269–294.
Matsuyama, K., Nagao, M. Structural analysis of aerial pictures. Information Processing. 1980; 21:468–480. [(in Japanese)].
Mitchell, T. M., Utgoff, P. E., Benerji, R. Learning by experimentation: Acquiring and refining problem-solving heuristics. In: Michalski I. R. S., Carbonell J. G., Mitchell T. M., eds. Machine Learning: An Artificial Intelligence Approach. Tioga/Springer-Verlag; 1983:163–190.
Ohta, Y., Knowledge-based interpretation of outdoor natural color scenes. Research Notes in Artificial Intelligence; 4. Pitman, 1985.
Poggio, T., Torre, V., Koch, C. Computational vision and regularization theory. Nature. 1985; 317:314–319. [26 September].
Shirai, Y. Analyzing intensity arrays using knowledge about scenes. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
Waltz, D. L. Understanding line drawings of scenes with shadows. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
II. Reference books for advanced Japanese readers
we only list books written in Japanese or translated into Japanese.
Aho, A. V., Hopcroft, J. E., Ullman, J. D. The Design and Analysis of Computer Algorithms. Addison-Wesley, 1976.
This is a standard textbook for learning data structure and algorithms.
Bobrow, D. G., Collins, A. Representation and Understanding: Studies in Cognitive Science. Academic Press, 1975.
This is a collection of classic theses on knowledge representation.
Goto, S. : Symbolic Processing Programming (Iwanami Software Series 8), Iwanami-Shoten, 1988 (in Japanese).
This is for learning Lisp and Prolog.
Iri, M., Shirakawa, I., Kajiya, Y., Shinoda, S., et alPractice Graph Basics–Theory and Applications. Koronasha, 1983. (in Japanese)
This describes in detail the basics of the graphs and networks and their algorithms.
Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, 1982.
This is a must-read book for readers who learn computational theory in visual information processing by Marr.
Nagao, M. Knowledge and Inference. Academic Press, 1990.
This is a suitable book for learning basics of the method of using knowledge bases in the artificial intelligence system and the inference method.
Nagao, M. and Fuchi, K. : Theory and Meaning (Iwanami Information Science Series 7), Iwanami-Shoten, 1983 (in Japanese).
This is suitable for learning the basics of predicate logic and model semantics.
Nilsson, N. J. Problem Solving Methods in Artificial Intelligence. McGraw-Hill, 1971.
This is a classic book on the algorithm mainly of searches and problem solving.
Rich, E. Artificial Intelligence. McGraw-Hill, 1983.
One of the standard textbooks for learning basics of artificial intelligence.
Shirai, Y. and Tsujii, J. : Artificial Intelligence (Iwanani Information Science Series 22), Iwanami-Shoten, 1982 (in Japanese).
One of the standard textbooks for learning basic methods in artificial intelligence.
Tsujii, J. Knowledge Representation and Usage. Shokodo, 1987. [(in Japanese)].
This is a good book for learning knowledge representation and inference including the recent research.
Ueno, H. and Ishizuka, M. : Knowledge Representation and Usage, (Knowledge Engineering Series 2), Omusha, 1987 (in Japanese).
This covers recent research on knowledge representation and methods of inference.
Winston, P. H. The Psychology of Computer Vision. McGraw-Hill, 1975.
This is a collection of theses on pioneering research at MIT on pattern understanding.
Wylie, C. R. Advanced Engineering Mathematics. McGraw-Hill, 1960.
This is a good textbook for learning, from the engineering standpoint, analytical methods such as Fourier analysis, convolution integrals, and so on.
Ballard, D. H., Brown, C. M. Computer Vision. Prentice-Hall, 1982.
This explains various methods for pattern understanding over a wide range including its relationship to knowledge representation.
Furui, T. Digital Voice Processing. Tokai University Press, 1985. [(in Japanese)].
We did not talk about voice recognition in this book, but this is a good book for learning voice recognition/synthesis.
Hiratsuka, K., Kitabashi, T. and Ogawa, H. : Digital Screen Processing, Nikkan Kogyo News Paper, 1985 (in Japanese).
This surveys methods for feature extractions.
Marr, D. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, 1982.
This includes a detailed explanation of the zero-crossing method and its significance.
Mori, S. and Sakakura, T. : Basics of Screen Recognition [O] —Preprocessing Feature Extraction for Shape, Omusha, 1986 (in Japanese).
This explains in detail the theory of feature extraction and its algorithm.
Nagao, M. Screen Recognition Theory. Koronasha, 1983. [(in Japanese)].
This is suitable for learning basics of various methods of feature extraction.
Nagao, M. Pattern Information Processing. Koronasha, 1983. [(in Japanese)].
This is suitable for learning information processing methods for pattern understanding.
Rosenfeld, A., Kak, A. C. Digital Picture Processing I, II, second edition. Academic Press, 1982.
This is known as a standard textbook on the methods of screen pattern recognition.
Shirai, Y. Computer Vision. Shokodo, 1980. [(in Japanese)].
This is suitable for learning methods of screen pattern recognition on a computer.
Shirai, Y. Pattern Understanding. Omusha, 1987. [(in Japanese)].
This explains various methods of feature extraction and pattern understanding including recent research and applications.
Tamura, H. Introduction to Computer Screen Processing. Soken Publishers, 1985. [(supervisor), (in Japanese)].
This not only explains methods for screen pattern recognition but also includes reference books on screen pattern recognition.
Cohen, P. R., Feigenbaum, E. A. The Handbook of Artificial Intelligence; III. Pitman, 1982.
This book outlines learning algorithms up to the early 1980s.
Furukawa, K., Mizoguchi, F. Learning Mechanism of Knowledge. Kyoritsu Publishers, 1986. [(in Japanese)].
This summarizes research on learning on logic.
Michalski, R. S., Carbonell, J. G., Mitchell, T. M. Machine Learning: An Artificial Intelligence Approach. Tioga/Springer-Verlag, 1983.
This outlines research on learning theory and algorithms until the early 1980s.
Michalski, R. S., Carbonell, J. G., Mitchell, T. M. Machine Learning II: An Artificial Intelligence Approach. Morgan Kaufmann, 1986.
This is a sequel to Michalski et al., 1983.
Nilsson, N. J. Principles of Artificial Intelligence. McGraw-Hill, 1983.
This is suitable for learning problem solving methods based on logical inference.
Shimura, M. Machine Knowledge Theory. Shokodo, 1983.
This is suitable for learning basic methods of artificial intelligence.
Aihara, K. Neural Computer. Tokyo Denki University Press, 1988. [(in Japanese)].
This covers from learning on neural networks to Chaos, especially about neural information processing models.
Arnari, S. Doctrine of Neural Network. Sangyo Tosho, 1978. [(in Japanese)].
This is known as a book summarizing theories of neural network learning.
Aso, H. Neural Network Information Processing. Sangyo Tosho, 1988. [(in Japanese)].
This book discusses learning in neural networks, especially summaries of models of recognition information processing.
III. Books directly related to the content of this book
Some theses are already listed in I and II.
Anzai, Y. Production systems and artificial intelligence research—for knowledge acquisition problems. Computer Software. 1984; 1(no. 3):2–12. (in Japanese)
de Boor, C. A Practical Guide to Splines. Springer-Verlag, 1978.
Connell, J. H., Brady, M. Generating and generalizing models of visual objects. Artificial Intelligence. 1987; 31:159–183.
Feigenbaum, E. A. : The simulation of verbal learning behavior, Proceedings of the Western Joint Computer Conference, pp. 121–32, 1961.
Forgy, C. L. Rege: A fast algorithm for the many pattern/many objects pattern match problem. Artificial Intelligence. 1982; 19:17–37.
Hayes, P. J. The logic of frames. In: Metzing D., ed. Frame Conceptions and Text Understanding. Walter de Gruyter; 1979:46–61.
Horn, B. K. P. Robot Vision. The MIT Press, 1986.
Kowalski, R. A. Logic for Problem Solving. North-Holland, 1979.
Lloyd, J. W. Foundations of Logic Programming. Springer-Verlag, 1984.
Minsky, M. A framework for representing knowledge. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
Robinson, J. A. A machine-oriented logic based on the resolution principle. Journal of ACM. 1965; 12:23–41.
Winograd, T. Frame representations and the declarative/procedural controversy. In: Bobrow D. G., Collins A. M., eds. Representation and Understanding: Studies in Cognitive Science. Academic Press; 1975:185–210.
Ballard, D. H. Generalization of the Hough transform to detect arbitrary shapes. Pattern Recognition. 1981; 13:111–122.
Chow, C. K., Kaneko, T. Automatic boundary detection of the left ventricle from cineangiograms. Computers and Biomedical Research. 1972; 5:388–410.
Clocksin, W. F. : Computer prediction of visual thresholds for surface slant and edge detection from optical flow fields, Ph. D. dissertation, University of Edinburgh, 1980.
Duda, R. O., Hart, P. E. Pattern Recognition and Scene Analysis. Wiley, 1973.
Duda, R. O., Hart, P. E. Use of Hough transformation to detect lines and curves in pictures. Communications of ACM. 1972; 15:11–15.
Freeman, H. Computer processing of line drawing images. Computing Surveys. 1974; 6:57–98.
Fu, K. S. Syntactic Methods in Pattern Recognition. Academic Press, 1974.
Hildreth, E. C. The detection of intensity changes by computer and biological vision systems. Computer Vision, Graphics and Image Processing. 1983; 22:1–27.
Horn, B. K. P., Schunck, B. G. Determining optical flow. Artificial Intelligence. 1981; 17:185–203.
Marr, D., Nishihara, H. K. Representation and recognition of the spatial organization of three-dimensional structure. Proceedings of Royal Society of London. 1978; B200:269–294.
Martelli, A. An application of heuristic search methods to edge and contour detection. Communications of ACM. 1976; 19:73–83.
Poggio, T., Torre, V., Koch, C. Computational vision and regularization theory. Nature. 1985; 317:314–319. [26 September].
Samet, H. Region representation: Quadtrees from boundary codes. Communications of ACM. 1980; 23:163–170.
Voelcker, H. B., Requicha, A. A. G. Geometric modeling of mechanical parts and processes. Computer. 1977; 10:48–57.
Agin, G. J. and Binford, T. O. : Computer description of curved objects, Proceedings of the Third International Joint Conference on Artificial Intelligence, pp. 629–640, 1973.
Ambler, A. P., Barrow, H. G., Brown, C. M., Burstall, R. M., Poppleston, R. J. A versatile computer-controlled assembly system. Artificial Intelligence. 1975; 6:129–156.
Ballard, D. H. Hierarchic Recognition of Tumors in Chest Radiographs. Birkhauser-Verlag, 1976.
Barrow, H. G., Tenenbaum, J. M. MSYS: A system for reasoning about scenes, Technical Note Vol. 121. Artificial Intelligence Center, SRI International, March 1976.
Brooks, R. A. Symbolic reasoning among 3-D models and 2-D images. Artificial Intelligence. 1981; 17:285–348.
Brooks, R. A., Greiner, R. and Binford, T. O. : Progress report on a model-based vision system, in L. S. Baumann, Proceedings of the Image Understanding Workshop, pp. 145–151, 1978.
Clowes, M. B. On seeing things. Artificial Intelligence. 1971; 2:79–116.
Guzman, A. Decomposition of a visual scene into three-dimensional bodies. AFIPS Proceedings of Fall Joint Computer Conference. 1968; 33:291–304.
Huffman, D. A., Impossible objects as nonsense sentencesMeltzer, B., Michie, D., eds. Machine Intelligence; 6. Edinburgh University Press, 1971:295–324.
Kanade, T. A theory of origami world. Artificial Intelligence. 1980; 13:279–311.
Matsuyama, K., Hang, V. Screen Understanding System SIGMA— integrating the bottom-up and the top-down analysis. Journal of Information Processing Society. 1985; 26:877–889. [(in Japanese)].
Matsuyama, K., Nagao, M. Structural analysis of aerial pictures. Information Processing. 1980; 21:468–480. [(in Japanese)].
Nagao, M. Control strategies in pattern analysis. Pattern Recognition. 1984; 17:45–56.
Ohta, Y., Knowledge-based interpretation of outdoor natural color scenes. Research Notes in Artificial Intelligence; 4. Pitman, 1985.
Rosenfeld, A., Hummel, R. A., Zucker, S. W. Scene labeling by relaxation operations. IEEE Transactions on Systems, Man and Cybernetics. 1976; SMC-6:420–433.
Shirai, Y. Analyzing intensity arrays using knowledge about scenes. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
Waltz, D. L. Understanding line drawings of scenes with shadows. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
Michalski, R. S. A theory and methodology of inductive learning. Artificial Intelligence. 1983; 20:111–162.
Michalski, R. S., Stepp, R. E. Learning from observation: Conceptual clustering. In: Michalski R. S., Carbonell J. G., Mitchell T. M., eds. Machine Learning: An Artificial Intelligence Approach. Tioga/Springer-Verlag; 1983:331–363.
Mitchell, T. M. Generalization as search. Artificial Intelligence. 1982; 18:203–226.
Mitchell, T. M. : Version spaces: A candidate elimination approach to rule learning, Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 305–310, 1977.
Mitchell, T. M., Utgoff, P. E., Banerji, R. Learning by experimentation: Acquiring and refining problem-solving heuristics. In: Michalski R. S., Carbonell J. G., Mitchell T. M., eds. Machine Learning: An Artificial Intelligence Approach. Tioga/Springer-Verlag; 1983:163–190.
Winston, P. H. Learning structural descriptions from examples. In: Winston P. H., ed. The Psychology of Computer Vision. McGraw-Hill, 1975.
Anzai, Y. Learning/adaptation function model by production systems. Measuring and Control. 1979; 18(no. 4):306–311. (in Japanese)
Anzai, Y., Simon, H. A. The theory of learning by doing. Psychological Review. 1979; 86:124–140.
Anzai, Y. Cognitive control of real-time event-driven systems. Cognitive Science. 1984; 8:221–254.
Buchanan, B. G., Feigenbaum, E. A. DENDRAL and Meta-DENDRAL; their applications dimension. Artificial Intelligence. 1978; 11:5–24.
Fikes, R. E., Nilsson, N. J. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence. 1971; 2:189–208.
Fikes, R. E., Hart, P. E., Nilsson, N. J. Learning and executing generalized robot plans. Artificial Intelligence. 1972; 3:251–288.
Sussman, G. J. A Computer Model of Skill Acquisition. American Elsevier, 1975.
Vere, S. A. Inductive learning of relational productions. In: Waterman D. A., Hayes-Roth F., eds. Pattern-Directed Inference Systems. Academic Press, 1978.
Vere, S. A. : Induction of relational productions in the presence of background information, Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 340–355, 1977.
Waterman, D. A. : Adaptive production systems, Proceedings of the Fourth International Joint Conference on Artificial Intelligence, pp. 296–303, 1975.
Arikawa, S. Inductive inference and analogy—theory and application. In: Furukawa K., Mizoguchi F., eds. Learning Mechanism of Knowledge. Kyoritsu Publishers; 1986:23–51. [(in Japanese)].
DeJong, J., Mooney, R. Explanation-based learning: An alternative view. Machine Learning. 1986; 1:145–176.
Doyle, J. A truth maintenance system. Artificial Intelligence. 1979; 12:231–272.
Doyle, J. : The ins and outs of reason maintenance, Proceedings of the Eighth International Joint Conference on Artificial Intelligence, pp. 349–351, 1983.
Haguchi, M. Mechanization of analogy. In: Furukawa K., Mizoguchi F., eds. Learning Mechanism of Knowledge. Kyoritsu Publishers; 1986:125–154. [(in Japanese)].
Kedar-Cabelli, S. T. and McCarty, L. T. : Explanation-based generalizartion as resolution theorem proving, Proceedings of the Fourth International Workshop on Machine Learning, pp. 383–389, 1987.
de Kleer, J. An assumption-based TMS. Artificial Intelligence. 1986; 28:127–162.
McCarthy, J. Circumscription: A form of non-monotonic reasoning. Artificial Intelligence. 1980; 13:41–72.
McCarthy, J. Application of circumscription to formalize commonsense knowledge. Artificial Intelligence. 1986; 28:89–116.
Mitchell, T. M., Keller, R. M., Kedar-Cabelli, S. T. Explanation-based generalization: A unifying view. Machine Learning. 1986; 1:47–80.
Reiter, R. A logic for default reasoning. Artificial Intelligence. 1980; 13:81–132.
Falkenhainer, B. C., Michalski, R. S. Integrating quantitative and qualitative discovery: The ABACUS system. Machine Learning. 1986; 1:367–401.
Langley, P. W. Data-driven discovery of physical laws. Cognitive Science. 1981; 5:31–54.
Langley, P. W., Simon, H. A., Bradshow, G. L., Zytkow, J. M. Scientific Discovery: Computational Explorations of the Creative Processes. The MIT Press, 1987.
Lenat, D. B. : Automated theory formation in mathematics, Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 833–842, 1977.
Lenat, D. B. The ubiquity of discovery. Artificial Intelligence. 1977; 9:257–285.
Quinlan, J. R. Induction of decision trees. Machine Learning. 1986; 1:81–106.
Schlimmer, J. C., Granger, R. H., Jr. Incremental learning from noisy data. Machine Learning. 1986; 1:317–354.
Ackley, D. H., Hinton, G. D., Sejnowski, T. J. A learning algorithm for Boltzmann machines. Cognitive Science. 1985; 9:147–169.
Amari, S. A theory of adaptive pattern classifiers. IEEE Transactions on Electronic Computers. 1967; EC-16:299–307.
Fukushima, K. Neocognition: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics. 1980; 36:193–202.
Geman, S., Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 1984; PAMI-6:721–741.
Hinton, G. D., Sejnowski, T. J. Learning and relearning in Boltzmann machines. In: Parallel Distributed Processing, Vol. 1: Foundations. The MIT Press; 1986:282–318. [D. E. Rumelhart, J. L. McClelland and the PDP Research Group].
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of National Academy of Sciences U. S. A. 1982; 79:2554–2558.
Hopfield, J. J., Tank, D. W. Neural computation of decisions in optimization problems. Biological Cybernetics. 1985; 52:141–152.
Kohonen, T. Self-Organization and Associative Memory. Springer-Verlag, 1984.
Minsky, M., Papert, S. Perceptrons (expanded edition). The MIT Press, 1988.
Rosenblatt, R. Principles of Neurodynamics. New York, Spartan Books, 1959.
Rumelhart, D. E., Hinton, G. E., Williams, R. J. Learning internal representations by error propagation. In: Parallel Distributed Processing, Vol. 2: Psychological and Biological Models. The MIT Press; 1986:318–362. [J. L. McClelland, D. E. Rumelhart and the PDP Research Group].
Rumelhart, D. E., Zipser, D. Feature discovery by competitive learning. Cognitive Science. 1985; 9:75–112.
von der Malsburg, C. Self-organization of orientation sensitive cells in the striate cortex. Cybernetik. 1973; 14:85–100.
IV. Journals, collections of essays, and so on
Most of the books listed in I-III are classics. Here we list popular academic journals, research papers, and collections of essays that include not only classical but also recent research papers
Journal, collections of essays (D), proceedings of annual convention, spcial Interest Group papers, Institute of Electronics, Information, and Communication Engineers of Japan.
Journal, proceedings of annual conventio, special interest group papers, Japanese Society for Artificial Intelligence.
Journal (Information Processing), collections of essays, proceedings of annual convention, special interest group papers, Information Processing Society of Japan.
Journal, collections of essays, proceedings of annual convention, special interest group papers, Society of Instrument and Control Engineers.
Journal, Robotic Society of Japan.
Journal (Computer Software Journal), proceedings of annual convention, Japan Society for Software Science & Technology.
Communications of ACM (Association for Computing Machinery)
Computer Vision, Graphics and Image Processing
IEEE Transactions on Computers
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man and Cybernetics
Proceedings of IEEE Conference on Neural Networks
Proceedings of National Conference of Artificial Intelligence, American Association for Artificial Intelligence
Proceedings of the International Workshop on Machine Learning
Proceedings of the International Joint Conference on Artificial Intelligence
Proceedings of the International Conference on Pattern Recognition
Brachman, R. J., Levesque, H. J. Readings in Knowledge Representation. Morgan Kaufmann, 1985.
Fischer, M. A., Firschein, O. Readings in Computer Vision. Morgan Kaufmann, 1987.
Ginsberg, M. Readings in Non-Monotonic Reasoning. Morgan Kaufmann, 1987.
Webber, B. L., Nilsson, N. J. Readings in Artificial Intelligence. Morgan Kaufmann, 1981.