Authors: Klette, Reinhard
Žunić, Joviša 
Title: On discrete moments of unbounded order
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 4245 LNCS
First page: 367
Last page: 378
Conference: 13th International Conference on Discrete Geometry for Computer Imagery, DGCI 2006; Szeged; Hungary; 25 October 2006 through 27 October 2006
Issue Date: 8-Dec-2006
Rank: M23
ISBN: 978-3-540-47651-2
ISSN: 0302-9743
DOI: 10.1007/11907350_31
Moment-based procedures are commonly used in computer vision, image analysis, or pattern recognition. Basic shape features such as size, position, orientation, or elongation are estimated by moments of order ≤ 2. Shape invariants are defined by higher order moments. In contrast to a theory of moments in continuous mathematics, shape moments in imaging have to be estimated from digitized data. Infinitely many different shapes in Euclidean space are represented by an identical digital shape. There is an inherent loss of information, impacting moment estimation. This paper discusses accuracy limitations in moment reconstruction in dependency of order of reconstructed moments and applied resolution of digital pictures. We consider moments of arbitrary order, which is not assumed to be bounded by a constant.
Keywords: Accuracy of estimation | Digital shapes | Discrete moments | Moments | Multigrid convergence
Publisher: Springer Link

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