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Book ChapterDOI

Adaptive structuring elements based on salience information

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TLDR
This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures, and shows how the new adaptive morphological operations can isolate the text in historical documents.
Abstract
Adaptive structuring elements modify their shape and size according to the image content and may outperform fixed structuring elements. Without any restrictions, they suffer from a high computational complexity, which is often higher than linear with respect to the number of pixels in the image. This paper introduces adaptive structuring elements that have predefined shape, but where the size is adjusted to the local image structures. The size of adaptive structuring elements is determined by the salience map that corresponds to the salience of the edges in the image, which can be computed in linear time. We illustrate the difference between the new adaptive structuring elements and morphological amoebas. As an example of its usefulness, we show how the new adaptive morphological operations can isolate the text in historical documents.

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Citations
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Journal ArticleDOI

Adaptive mathematical morphology - A survey of the field

TL;DR: An up-to-date survey on the topic of adaptive mathematical morphology, providing a brief analysis of perspectives and trends within the field, and discussing possible directions for future studies.
Book ChapterDOI

Salience-Based Parabolic Structuring Functions

TL;DR: This paper proposes salience-based parabolic structuring functions that are defined for a fixed, predefined spatial support, and have low computational complexity.

Distance Functions and Their Use in Adaptive Mathematical Morphology

TL;DR: A comparison of different shapes in images to determine the extent to which one shape differs from another is usually a desirable step in image analysis.
Book ChapterDOI

Adaptive Hit or Miss Transform

TL;DR: A framework for adaptive Hit or Miss Transform, where structuring elements are adaptive with respect to the input image itself, and can detect particles in single molecule imaging is presented.
References
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Journal ArticleDOI

A Computational Approach to Edge Detection

TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
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.
Journal ArticleDOI

Distance transformations in digital images

TL;DR: Six different distance transformations, both old and new, are used for a few different applications, which show both that the choice of distance transformation is important, and that any of the six transformations may be the right choice.
Journal ArticleDOI

A simple method for detecting salient regions

TL;DR: A simple method for detecting salient regions in images that requires only edge detection, threshold decomposition, the distance transform, and thresholding and is surprisingly effective, and has the benefit of easy implementation.
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