Abstract
We present the combination of a decision theoretic and a syntactic approach to image segmentation. It is shown how statistical properties of iconic information can be systematically used to program a special architecture for parallel decision theoretic image segmentation. It is also shown how the probabilistic output of this architecture automatically provides problem dependent primitives for a subsequent syntactic phase. This phase can resolve ambiguities and incomplete segmentation results in cases where objects and background are not clearly distinct by textural and gray level properties alone. Evidence for the performance of the suggested combined approach is provided by examples from different industrial and biomedical applications.
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References
Ballard D (1982) Computer vision. Prentice-Hall, Englewood Cliffs, New Jersey
Barr A, Feigenbaum E (1982) The handbook of artificial intelligence. Volume 2, William Kaufman, Los Altos, California
Blanz WE (1986) Feature selection and polynomial classifiers for industrial decision analysis. Research Report RJ 5242 (54242), IBM
Blanz WE (1988) Non-parametric feature selection for multiple class processes. In: Proc. 9th Int. Conf. Pattern Recognition. Rome, Italy
Blanz WE, Reinhardt ER (1982) General approach to image segmentation. In: Proc. 6th Int. Conf. Pattern Recognition. Munich, West Germany
Blanz WE, Sanz JLC, Hinkle EB (1988) Image analysis methods for solder-ball inspection in integrated circuit manufacturing. IEEE J. Robot. Autom., 4(2): 129–139
Blanz WE, Sanz JLC, Petkovic D (1987) Control-free low-level image segmentation: theory, architecture, and experimentation. In: Proc. 1st Int. Conf. Computer Vision. London, United Kingdom
Boerner H, Strecker H (1988) Automated x-ray inspection of aluminium castings. IEEE Trans. Pattern Anal. Machine Intell., PAMI-10(1):79–91
Bursky D (1987) CMOS four-chip set processes images at 20-MHz data rates. Electron. Design, 35(13):39–46
Don HS, Fu KS (1985) A syntactic method for image segmentation and object recognition. Pattern Recogn., 18(1):73–87
Duda RO, Hart PE (1973) Pattern classification and scene analysis. Wiley, New York
Flynn MJ (1966) Very high-speed computing systems. Proc. IEEE, 54(12):1901–1909
Fu KS (1974) Syntactic pattern recognition. Academic Press, New York
Fu KS, Mu JK (1981) A survey on image segmentation. Pattern Recogn., 13(1):3–16
Gonzalez RC, Thomson MG (1978) Syntactic pattern recognition, an introduction. Addison-Wesley, London
Levine MD, Nazif AM (1985) Rule-based image segmentation. Comput. Vision Graphics Image Process., 32(2): 104–126
Nevatia R (1982) Machine perception. Prentice-Hall, Englewood Cliffs, New Jersey
Roberts LG (1965) Machine perception of threedimensional solids: In: Tippet JT, Berkowitz DA, others (Ed.) Optical and electro-optical information processing. MIT Press, Cambridge, Massachusetts
Rosenblatt F (1962) A comparison of several perception models: In: Yovitis MC, others (Ed.) Self-organizing systems. Spartan, New York
Rosenfeld A, Kak A (1982) Digital picture processing. 3rd edition, Academic Press, Orlando, Florida
Schürmann J (1977) Polymomklassifikatoren für die Zeichenerkennung. Oldenbourg, Munich
Sklansky J, Wassel G (1981) Pattern classifiers and trainable machines. Springer-Verlag, New York
Zhang Z, Simaan M (1986) A rule-based interpretation system for segmentation of seismic images. Pattern Recognition, 20(1):45–53
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Supported by Bundesminister für Forschung und Technologie (BMfT) and AEG Ulm, Germany.
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Straub, B.J., Blanz, W.E. Combined decision theoretic and syntactic approach to image segmentation. Machine Vis. Apps. 2, 17–30 (1989). https://doi.org/10.1007/BF01214394
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DOI: https://doi.org/10.1007/BF01214394