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Connected operators are increasingly used in image processing They are at tractive in applications where the signal has to be simplified without loosing information about the contours A large number of simplification criteria such as size area con trast or complexity can be obtained with these operators In this paper we deal with a motion oriented connected operator This operator eliminates from the original sequence the components that do not undergo a specic motion ( defined as filtering parameter) while the remaining ob jects are almost perfectly preserved, Peer Reviewed, Postprint (published version)

This paper deals with a class of morphological operators called connected operators. These operators interact with the signal by merging flat zones. As a results, they do not create any new contours and are very attractive for filtering tasks where the contour information has to be preserved. This paper focuses on a class of operators dealing with motion information. They remove from the original sequence the components that do not undergo a specific motion. They have a large number of applications including image sequence analysis with motion multiresolution decomposition and motion estimation., Peer Reviewed, Postprint (published version)

This paper deals with the notion of connected operators. These operators are becoming popular in image processing because they have the fundamental property of simplifying the signal while preserving the contour information. In this paper, we discuss some practical approaches for the extension and the generalization of these operators. We focus on two important issues: the simpli cation criterion and the connectivity. We present in particular complexity- and motionoriented connected operators. Moreover, we discuss the creation connectivities that are either more or less strict than the usual ones., Peer Reviewed, Postprint (published version)