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Extending Distance Computation - Propagating Derivatives
Uppsala universitet, Teknisk-naturvetenskapliga vetenskapsområdet, Matematisk-datavetenskapliga sektionen, Centrum för bildanalys.ORCID-id: 0000-0002-4405-6888
2010 (engelsk)Inngår i: Proceedings SSBA 2010 / [ed] Cris Luengo and Milan Gavrilovic, Uppsala: Centre for Image Analysis , 2010, s. 39-42Konferansepaper, Publicerat paper (Annet vitenskapelig)
Abstract [en]

In this paper we present a technique to extend distance computation  algorithms that compute global distances from a series of local  updates. This includes algorithms such as the fast marching method  (FMM) and the chamfering algorithm for weighted distances. In  addition to the value of a distance function or distance map, we  derive formulas to compute the gradient and higher order partial  derivatives of the distance function within the same framework. The  approach is based on symbolic differentiation of the update scheme,  which makes it general and straight forward to apply to almost any  distance computation scheme. The main result is a novel set of  ``derivative maps'' that are computed along with the ordinary  distance maps. Apart from the theory itself, these maps and this  technique may be used to compute skeletons and parameterizations  such as Riemannian Normal Coordinates and Gauss Normal Coordinates.

sted, utgiver, år, opplag, sider
Uppsala: Centre for Image Analysis , 2010. s. 39-42
Serie
Centre for Image Analysis Report Series, ISSN 1100-6641 ; 34
HSV kategori
Forskningsprogram
Datoriserad bildanalys
Identifikatorer
URN: urn:nbn:se:uu:diva-142812OAI: oai:DiVA.org:uu-142812DiVA, id: diva2:388277
Konferanse
Symposium on Image Analysis
Tilgjengelig fra: 2011-01-17 Laget: 2011-01-17 Sist oppdatert: 2018-01-12bibliografisk kontrollert

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