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Book ChapterDOI

Region Adjacency Graphs and Connected Morphological Operators

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TLDR
A morphological operator is called connected if it does not split components of the levelsets, but acts on the level of flat zones, and a simple description of such operators can be obtained by representing an image as a region adjacency graph, a graph whose vertices represent the component of the level sets and whose edges describe adjacencies.
Abstract
A morphological operator is called connected if it does not split components of the levelsets, but acts on the level of flat zones. A simple description of such operators can be obtained byrepresenting an image as a region adjacency graph, a graph whose vertices represent the componentsof the level sets and whose edges describe adjacency. In this graph connected operators can onlychange grey-values of the vertices. To obtain the adjacency graph of the transformed image, onehas to merge adjacent vertices which carry the same grey-value.

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Citations
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Journal ArticleDOI

Antiextensive connected operators for image and sequence processing

TL;DR: This paper shows that connected operators work implicitly on a structured representation of the image made of flat zones, and proposes the max-tree as a suitable and efficient structure to deal with the processing steps involved in antiextensive connected operators.
Journal ArticleDOI

Connected Filtering and Segmentation Using Component Trees

TL;DR: The component tree is proposed as an efficient and accessible data structure used to implement nonflat gray-level connected filters and an application of nonflat component filters to the segmentation of wood micrographs is presented.

Connected morphological operators for binary images

TL;DR: 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.
Posted Content

Hierarchical Graph Representations in Digital Pathology

TL;DR: A novel multi-level hierarchical entity-graph representation of tissue specimens is proposed to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions and is demonstrated to yield superior classification results compared to alternative methods aswell as individual pathologists.
Journal ArticleDOI

Hierarchical graph representations in digital pathology

TL;DR: In this article, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality, treating the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level.
References
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Book

Image Analysis and Mathematical Morphology

Jean Serra
TL;DR: This invaluable reference helps readers assess and simplify problems and their essential requirements and complexities, giving them all the necessary data and methodology to master current theoretical developments and applications, as well as create new ones.
Book

Image Processing: Analysis and Machine Vision

TL;DR: The digitized image and its properties are studied, including shape representation and description, and linear discrete image transforms, and texture analysis.
Journal ArticleDOI

Morphological grayscale reconstruction in image analysis: applications and efficient algorithms

TL;DR: An algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm.
Book

Morphological image operators

TL;DR: First Principles Complete Lattices Operators on complete lattice operators which are Translation Invariant Adjunctions, Dilations, and Erosions Openings and Closings Hit-or-miss Topology and Semi Continuity Discretization Convexity, Distance, and Connectivity Lattice Representations of Functions Morphology for Grey-Scale Images Morphological Filters Filtering and Iteration.
Journal ArticleDOI

Flat zones filtering, connected operators, and filters by reconstruction

TL;DR: It is shown that from any connected operator acting on sets, one can construct a connected operator for functions (however, it is not the unique way of generating connected operators for functions).
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