Motion connected operators for image sequences

This paper deals with motion-oriented connected operators. These operators eliminate from an original sequence the components that do not undergo a specific motion (defined as a filtering parameter). As any connected operator, they achieve a simplification of the original image while preserving the contour information of the components that have not be removed. Motion-oriented filtering may have a large number of applications including sequence analysis with motion multi-resolution decomposition or motion estimation.Peer ReviewedPostprint (published version

Antiextensive Connected Operators for Image and Sequence Processing

Abstract-This paper deals with a class of morphological operators called connected operators. These operators filter the signal by merging its flat zones. As a result, they do not create any new contours and are very attractive for filtering tasks where the contour information has to be preserved. This paper shows that connected operators work implicitly on a structured representation of the image made of flat zones. The max-tree is proposed as a suitable and efficient structure to deal with the processing steps involved in antiextensive connected operators. A formal definition of the various processing steps involved in the operator is proposed and, as a result, several lines of generalization are developed. First, the notion of connectivity and its definition are analyzed. Several modifications of the traditional approach are presented. They lead to connected operators that are able to deal with texture. They also allow the definition of connected operators with less leakage than the classical ones. Second, a set of simplification criteria are proposed and discussed. They lead to simplicity-, entropy-, and motion-oriented operators. The problem of using a nonincreasing criterion is analyzed. Its solution is formulated as an optimization problem that can be very efficiently solved by a Viterbi algorithm. Finally, several implementation issues are discussed showing that these operators can be very efficiently implemented

Motion connected operators for image sequences

ABSTRACT This paper deals with motion-oriented connected operators. These operators eliminate from an original sequence the components that do not undergo a specific motion (defined as a filtering parameter). As any connected operator, they achieve a simplification of the original image while preserving the contour information of the components that have not be removed. Motion-oriented filtering may have a large number of applications including sequence analysis with motion multi-resolution decomposition or motion estimation. 1 INTRODUCTION Morphological filters by reconstruction, and more generally connected operators, are increasingly used in image processing [8, 5, 9, 1, 7, 6]. They are attractive in applications where the signal has to be simplified without loosing information about the contours. A large number of simplification criteria, such as size [8], area [11], dynamics [2], contrast, or complexity [4] can be obtained with these operators. Motion information is a difficult issue in image sequence processing. Most of the time, motion is extracted from a local estimation that does not take into account the structure of the signal, that is the various objects in the scene. This is the case, in particular, for the popular block-matching or pel-recursive motion estimation algorithms [10]. The objective of this paper is to propose a filtering technique that leads to a different way of handling the motion information. The goal is to define a filtering tool allowing the simplification of the image following a motion criterion. In practice, the image components that do not undergo a specific motion should be removed