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Distance measures between digital fuzzy objects and their applicability in image processing
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.ORCID-id: 0000-0001-7312-8222
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Centrum för bildanalys. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion. Faculty of Technical Sciences, University of Novi Sad, Serbia.ORCID-id: 0000-0002-6041-6310
2011 (engelsk)Inngår i: Combinatorial Image Analysis / [ed] Jake Aggarwal, Reneta Barneva, Valentin Brimkov, Kostadin Koroutchev, Elka Koroutcheva, Springer Berlin/Heidelberg, 2011, s. 385-397Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We present two different extensions of the Sum of minimal distances and the Complement weighted sum of minimal distances to distances between fuzzy sets. We evaluate to what extent the proposed distances show monotonic behavior with respect to increasing translation and rotation of digital objects, in noise free, as well as in noisy conditions. Tests show that one of the extension approaches leads to distances exhibiting very good performance. Furthermore, we evaluate distance based classification of crisp and fuzzy representations of objects at a range of resolutions. We conclude that the proposed distances are able to utilize the additional information available in a fuzzy representation, thereby leading to improved performance of related image processing tasks.

sted, utgiver, år, opplag, sider
Springer Berlin/Heidelberg, 2011. s. 385-397
Serie
Lecture Notes in Computer Science ; 6636
Emneord [en]
Fuzzy sets, set distance, registration, classification
HSV kategori
Forskningsprogram
Datoriserad bildanalys; Datoriserad bildbehandling
Identifikatorer
URN: urn:nbn:se:uu:diva-157186DOI: 10.1007/978-3-642-21073-0_34ISBN: 978-3-642-21072-3 (tryckt)OAI: oai:DiVA.org:uu-157186DiVA, id: diva2:435525
Konferanse
Internatiional Workshop on Combinatorial Image Analysis, IWCIA 2011
Tilgjengelig fra: 2011-08-18 Laget: 2011-08-18 Sist oppdatert: 2018-12-18
Inngår i avhandling
1. Distance Functions and Their Use in Adaptive Mathematical Morphology
Åpne denne publikasjonen i ny fane eller vindu >>Distance Functions and Their Use in Adaptive Mathematical Morphology
2014 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

One of the main problems in image analysis is a comparison of different shapes in images. It is often desirable to determine the extent to which one shape differs from another. This is usually a difficult task because shapes vary in size, length, contrast, texture, orientation, etc. Shapes can be described using sets of points, crisp of fuzzy. Hence, distance functions between sets have been used for comparing different shapes.

Mathematical morphology is a non-linear theory related to the shape or morphology of features in the image, and morphological operators are defined by the interaction between an image and a small set called a structuring element. Although morphological operators have been extensively used to differentiate shapes by their size, it is not an easy task to differentiate shapes with respect to other features such as contrast or orientation. One approach for differentiation on these type of features is to use data-dependent structuring elements.

In this thesis, we investigate the usefulness of various distance functions for: (i) shape registration and recognition; and (ii) construction of adaptive structuring elements and functions.

We examine existing distance functions between sets, and propose a new one, called the Complement weighted sum of minimal distances, where the contribution of each point to the distance function is determined by the position of the point within the set. The usefulness of the new distance function is shown for different image registration and shape recognition problems. Furthermore, we extend the new distance function to fuzzy sets and show its applicability to classification of fuzzy objects.

We propose two different types of adaptive structuring elements from the salience map of the edge strength: (i) the shape of a structuring element is predefined, and its size is determined from the salience map; (ii) the shape and size of a structuring element are dependent on the salience map. Using this salience map, we also define adaptive structuring functions. We also present the applicability of adaptive mathematical morphology to image regularization. The connection between adaptive mathematical morphology and Lasry-Lions regularization of non-smooth functions provides an elegant tool for image regularization.

sted, utgiver, år, opplag, sider
Uppsala: Acta Universitatis Upsaliensis, 2014. s. 88
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1137
Emneord
Image analysis, Distance functions, Mathematical morphology, Adaptive mathematical morphology, Image regularization
HSV kategori
Forskningsprogram
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-221568 (URN)978-91-554-8923-6 (ISBN)
Disputas
2014-05-23, Room 2347, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2014-04-28 Laget: 2014-04-01 Sist oppdatert: 2018-01-11

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