Stereo image analysis using connected operators

Connected operators are increasingly used in image processing due to their properties of simplifying the image with various criteria, without loosing contour's information. These properties are related to the connected operator approach that either preserves or completely eliminates a determined connected component, according to an established criterion of analysis. In this paper we will define a new connected operator for stereo images. The goal is to simplify one of the images (left) in the sense that the operator will eliminate the image components that are not present at a determined location in the other image (right). This filter let us select in a stereo image, objects as a function of their distance from the observer (for instance used in auto guided vehicles).Peer ReviewedPostprint (published version

Connected morphological operators for binary images

This paper presents a comprehensive discussion on connected morphological operators for binary images. Introducing a connectivity on the underlying space, every image induces a partition of the space in foreground and background components. A connected operator is an operator that coarsens this partition for every input image. A connected operator is called a grain operator if it has the following `local property': the value of the output image at a given point $x$ is exclusively determined by the zone of the partition of the input image that contains $x$. Every grain operator is uniquely specified by two grain criteria, one for the foreground and one for the background components. A well-known criterion is the area criterion demanding that the area of a zone is not below a given threshold. The second part of the paper is devoted to connected filters and grain filters. It is shown that alternating sequential filters resulting from grain openings and closings are strong filters and obey a strong absorption property, two properties that do not hold in the classical non-connected case

Practical extensions of connected operators

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 ReviewedPostprint (published version

Practical extensions of connected operators

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 Reviewe