Object-Based Image Analysis Using Multiscale Connectivity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphology on Label Images: Flat-Type Operators and Connections
Journal of Mathematical Imaging and Vision
Constructing multiscale connectivities
Computer Vision and Image Understanding
Mask-Based Second-Generation Connectivity and Attribute Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Fuzzy Connectivity Measure for Fuzzy Sets
Journal of Mathematical Imaging and Vision
Constructing multiscale connectivities
Computer Vision and Image Understanding
Gradient estimation using wide support operators
IEEE Transactions on Image Processing
A new fuzzy connectivity class application to structural recognition in images
DGCI'08 Proceedings of the 14th IAPR international conference on Discrete geometry for computer imagery
Partition-induced connections and operators for pattern analysis
Pattern Recognition
Efficient computation of new extinction values from extended component tree
Pattern Recognition Letters
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A novel notion of connectivity for grayscale images is introduced, defined by means of a binary connectivity assigned at image-level sets. In this framework, a grayscale image is connected if all level sets below a prespecified threshold are connected. The proposed notion is referred to as grayscale level connectivity and includes, as special cases, other well-known notions of grayscale connectivity, such as fuzzy grayscale connectivity and grayscale blobs. In contrast to those approaches, the present framework does not require all image-level sets to be connected. Moreover, a connected grayscale object may contain more than one regional maximum. Grayscale level connectivity is studied in the rigorous framework of connectivity classes. The use of grayscale level connectivity in image analysis applications, such as object extraction, image segmentation, object-based filtering, and hierarchical image representation, is discussed and illustrated.