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Mathematical Morphology on Irregularly Sampled Signals
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.ORCID-id: 0000-0002-0612-558x
Flagship Biosciences Inc., Westminster, USA.
Luleå University of Technology, Luleå, Sweden.
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Avdelningen för visuell information och interaktion. Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Institutionen för informationsteknologi, Bildanalys och människa-datorinteraktion.
2017 (Engelska)Ingår i: Computer Vision – ACCV 2016 Workshops. ACCV 2016, Springer, 2017, Vol. 10117, s. 506-520Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

This paper introduces a new operator that can be used to approximate continuous-domain mathematical morphology on irregularly sampled surfaces. We define a new way of approximating the continuous domain dilation by duplicating and shifting samples according to a flat continuous structuring element. We show that the proposed algorithm can better approximate continuous dilation, and that dilations may be sampled irregularly to achieve a smaller sampling without greatly compromising the accuracy of the result.

Ort, förlag, år, upplaga, sidor
Springer, 2017. Vol. 10117, s. 506-520
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10117
Nationell ämneskategori
Datavetenskap (datalogi) Beräkningsmatematik
Identifikatorer
URN: urn:nbn:se:uu:diva-309921DOI: 10.1007/978-3-319-54427-4_37ISI: 000426193700037ISBN: 978-3-319-54427-4 (digital)ISBN: 978-3-319-54426-7 (tryckt)OAI: oai:DiVA.org:uu-309921DiVA, id: diva2:1053063
Konferens
13th Asian Conference on Computer Vision (ACCV), Taipei, Taiwan, November 20-24, 2016
Forskningsfinansiär
Vetenskapsrådet, 2014-5983Tillgänglig från: 2016-12-08 Skapad: 2016-12-08 Senast uppdaterad: 2019-10-17Bibliografiskt granskad
Ingår i avhandling
1. Precise Image-Based Measurements through Irregular Sampling
Öppna denna publikation i ny flik eller fönster >>Precise Image-Based Measurements through Irregular Sampling
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Alternativ titel[sv]
Noggranna bildbaserade mätningar via irreguljär sampling
Abstract [en]

Mathematical morphology is a theory that is applicable broadly in signal processing, but in this thesis we focus mainly on image data. Fundamental concepts of morphology include the structuring element and the four operators: dilation, erosion, closing, and opening. One way of thinking about the role of the structuring element is as a probe, which traverses the signal (e.g. the image) systematically and inspects how well it "fits" in a certain sense that depends on the operator.

Although morphology is defined in the discrete as well as in the continuous domain, often only the discrete case is considered in practice. However, commonly digital images are a representation of continuous reality and thus it is of interest to maintain a correspondence between mathematical morphology operating in the discrete and in the continuous domain. Therefore, much of this thesis investigates how to better approximate continuous morphology in the discrete domain. We present a number of issues relating to this goal when applying morphology in the regular, discrete case, and show that allowing for irregularly sampled signals can improve this approximation, since moving to irregularly sampled signals frees us from constraints (namely those imposed by the sampling lattice) that harm the correspondence in the regular case. The thesis develops a framework for applying morphology in the irregular case, using a wide range of structuring elements, including non-flat structuring elements (or structuring functions) and adaptive morphology. This proposed framework is then shown to better approximate continuous morphology than its regular, discrete counterpart.

Additionally, the thesis contains work dealing with regularly sampled images using regular, discrete morphology and weighting to improve results. However, these cases can be interpreted as specific instances of irregularly sampled signals, thus naturally connecting them to the overarching theme of irregular sampling, precise measurements, and mathematical morphology.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2019. s. 63
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1869
Nyckelord
image analysis, image processing, mathematical morphology, irregular sampling, adaptive morphology, missing samples, continuous morphology, path opening.
Nationell ämneskategori
Signalbehandling Annan data- och informationsvetenskap
Forskningsämne
Datoriserad bildbehandling
Identifikatorer
urn:nbn:se:uu:diva-395205 (URN)978-91-513-0783-1 (ISBN)
Disputation
2019-12-06, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 13:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
Vetenskapsrådet, 2014-5983
Tillgänglig från: 2019-11-13 Skapad: 2019-10-17 Senast uppdaterad: 2019-11-13

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