By Eva Armengol, Enric Plaza (auth.), Jean-Paul Haton, Mark Keane, Michel Manago (eds.)
This e-book provides a variety of revised refereed papers taken from the contributions to the second one ecu Workshop on Case-Based Reasoning, EWCBR-94, held on the Abbaye de Royaumont close to Paris in November 1994. The 22 papers incorporated have been selected from a complete of 60 submissions. the real evolution by means of skilled man made intelligence over the past few years has been basically inspired via case-based reasoning, relatively through the realm of knowledge-based selection help. This e-book files the growth accomplished in CBR equipment and instruments throughout the very contemporary previous. It additionally outlines the big good fortune completed within the purposes area, in particular within the fields of structure and computer-aided layout, activity making plans, chemical synthesis, upkeep and analysis, and law.
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Additional info for Advances in Case-Based Reasoning: Second European Workshop, EWCBR-94 Chantilly, France, November 7–10, 1994 Selected Papers
5, for merging. ,, @ @R Level 2 Level 3 Level 4 Fig. 6. Decomposition of a Dog. The 3 − 4 − 5 metric is used for smoothing. One step with the simple merging algorithm is taken using 6-connectivity for the definition of border voxels. 5. connected component with the head of the Dog, since we use 26-connectivity for the shape. This component can not be considered as small. When this happens, the simple merging algorithm is of great help. It removes most of the small and skin-shaped parts at level 3.
1 Introduction A promising approach in discrete geometry for computer imagery is the one based on arithmetic geometry1 . Reveill`es observed in his pioneer work  that discrete straight lines in the plane can be analytically defined through a double linear Diophantine inequality of the form 0 ≤ ax + by + µ < ω, where the parameters are all integers. The parameter ω > 0 is interpreted as a thickness of the discrete line, while µ is the internal translation constant which measures the shift of the line with respect to the origin.
Through experiments we have seen that the AN D4 - and AN D8 -decompositions give more stable results than the AN D1 -decomposition. Even though these are more computationally heavy than the AN D1 -decomposition, their use is in most cases necessary. The difference between the AN D4 - and AN D8 decomposition is not equally large and the choice can be made from the computational power available and the stability needed. Even though the AN D8 -decomposition is selected, the decomposition is still not fully translation invariant.