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By Sagan H.

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On Pattern Recognition, pp. 72–80 (1980) 4. : Computer Recognition of three-dimensional objects in a visual scene. Tech. Rep. MAC-TR-59, AI Laboratory, MIT (1968) 5. : Non-Gibbsian Markov Random Fields for object recognition. The British Machine Vision Conference (submitted, 2007) 6. : Impossible Objects as Nonsense Sentences. Machine Intelligence 6, 295–323 (1971) 7. : One the foundations of relaxation labelling process. IEEE Transactions PAMI 5, 267–287 (1983) 8. : Markov Random Fields and their Applications.

The Psychology of Computer Vision, pp. 19–91. html 26. : Learning structural descriptions from examples. The psychology of computer vision, 157–209 (1975) 27. : A fuzzy algorithmic approach to the definition of complex or imprecise concepts. Int. J. Man-Machine Studies 8, 249–291 (1976) 28. htm Biometric Recognition: Overview and Recent Advances Anil K. edu Abstract. , international border crossings, access to buildings, laptops and mobile phones) have served as an impetus for a tremendous growth in biometric recognition technology.

K-Centers. The k-Centers prototype selector tries to adapt to the graph distrubution of set T and selects graphs that are in the center of densely populated areas. First a k-means clustering procedure is applied to set T . The number of clusters to be found is equal to the number of prototypes to be selected. Once the clusters have been established, the median of each cluster is selected as a prototype. Targetsphere. The Targetsphere prototype selector first selects a graph gc situated in the center of T .

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