Title: Advances in Connectivity and Connected Attribute Filters
Publisher: Elsevier
Publication Year: 2010
JRC N°: JRC59391
ISBN: 9780123813183
ISSN: 1076-5670
URI: http://www.sciencedirect.com/science/article/B7RNM-4YK6WTX-B/2/68da039cfccb4f488766957b35be2a07
DOI: 10.1016/S1076-5670(10)61005-1
Type: Articles in periodicals and books
Abstract: In this paper we review recent advances in connected filtering, with emphasis on attribute filters based on component trees. We first describe basic connected filters using standard graph-based connectivity, based on the familiar 4, and 8-neighborhood relations in two dimensions. We show that connected filtering can be seen as a nonlinear counterpart of matching pursuit and other linear image representation schemes using overcomplete dictionaries. Understood this way, it can be shown that connected filters deliver a sparser image representation than do standard structural filters. We then show how abstractions of the notion of connectivity allow further manipulation of the dictionaries used, providing improved control over the perceptual groups detected in images. A review of algorithms to compute these filters is also presented. Beyond connectivity, we discuss hyperconnectivity and attribute-space connectivity, which offer ways to deal with overlapping structures efficiently while retaining much of the descriptive power of connectivity.
JRC Directorate:Space, Security and Migration

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