Mathematical Morphology on Irregularly Sampled Data in One Dimension

• Mathematical morphology (MM) on grayscale images is commonly performed in the discretedomain on regularly sampled data. However, if the intention is to characterize or quantify continuousdomainobjects, then the discrete-domain morphology is affected by discretization errors that may bealleviated by considering the underlying continuous signal, given a correctly sampled bandlimited image.Additionally, there are a number of applications where MM would be useful and the data is irregularlysampled. A common way to deal with this is to resample the data onto a regular grid. Often this createsproblems where data is interpolated in areas with too few samples. In this paper, an alternative way ofthinking about the morphological operators is presented. This leads to a new type of discrete operatorsthat work on irregularly sampled data. These operators are shown to be morphological operators thatare consistent with the regular, morphological operators under the same conditions, and yield accurateresults under certain conditions where traditional morphology performs poorly

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

mathematical morphology

Signalbehandling

Signal Processing

Computerized Image Processing

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