This work introduces a residual operator called ultimate attribute leveling. We also present an efficient algorithm for ultimate attribute leveling computation by using a structure called tree of shapes. Our algorithm for computating ultimate attribute leveling is based on the fact (proved in this work) that levelings can be obtained by pruning nodes from the tree of shapes. This is a novel result, since so far it is known that levelings can be obtained from component trees. Finally, we propose the use of ultimate attribute leveling with shape information to extract contrast using a priori knowledge of an application. Experimental results applied to text location show the potentiality of using ultimate attribute leveling with shape information for solving problems in image processing area. Keywords-residual morphological operator; ultimate attribute opening; ultimate attribute closing; ultimate attribute leveling.

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The component-hypertree is a data structure that generalizes the concept of component-tree to multiple (increasing) neighborhoods. However, construction of a component-hypertree is costly because it needs to process a high number of neighbors. In this article, we present some properties used to obtain optimized neighborhoods for componenthypertree computation. Using these properties,we explore a new strategy to obtain neighboring elements based on hierarchy of partitions, leading to a more efficient algorithm with the drawback of a slight loss of precision on the distance of merged nodes.