A k-max Geodesic Distance and Its Application in Image Segmentation

Holuša, Michael; Sojka, Eduard

doi:10.1007/978-3-319-23192-1_52

Cited by 3 publications

(2 citation statements)

References 10 publications

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“…A major advantage of Hash-Combs when compared to classic techniques [29] [20] is that our approach is not limited to Euclidean data spaces, where distances represent the shortest path between two points along a straight line. In fact, Hash-Combs are also suitable for approximating non-Euclidean geodesic distances [37], which have applications to network security [38], to tracing paths on 3D mesh objects [39], to clustering [40] and training Machine Learning models [41]. Revisiting the example in Figure 4, we can define a simple distance measure based on an element-wise equality check.…”

Section: Hash-combmentioning

confidence: 99%

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

2022

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Data privacy regulations like the EU GDPR allow the use of hashing techniques to anonymize data that may contain personal information. However, hashing is well-known to destroy any possibility of performing analytics. Homomorphic crypto-systems allow computing analytics over encrypted data, but cannot guarantee privacy compliance without being coupled with specific privacy-preservation provisions. In this work, we present a novel distance-preserving hashing scheme supporting both regulatory compliance and collaborative analytics. Our scheme achieves regulatory compliance by relying on standard cryptographic hashes while preserving a controllable notion of distance between data points.

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Section: Hash-combmentioning

confidence: 99%

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

2022

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“…The MBD based on ρ is, similar to [8], not smooth in the sense of [2,6]. As a consequence, exact DTs for ρ cannot be computed as efficiently as for ϕ, but both approximate algorithms and efficient algorithms for exact computation have been developed.…”

Section: Introductionmentioning

confidence: 99%

The Minimum Barrier Distance: A Summary of Recent Advances

Strand

Ciesielski

Malmberg

et al. 2017

Discrete Geometry for Computer Imagery

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Abstract. In this paper we present an overview and summary of recent results of the minimum barrier distance (MBD), a distance operator that is a promising tool in several image processing applications. The theory constitutes of the continuous MBD in R n , its discrete formulation in Z n (in two different natural formulations), and of the discussion of convergence of discrete MBDs to their continuous counterpart. We describe two algorithms that compute MBD, one very fast but returning only approximate MBD, the other a bit slower, but returning the exact MBD. Finally, some image processing applications of MBD are presented and the directions of potential future research in this area are indicated.

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The k -max distance in graphs and images

Holuša

Sojka

2017

Pattern Recognition Letters

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A k-max Geodesic Distance and Its Application in Image Segmentation

Cited by 3 publications

References 10 publications

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

Hash-Comb: A Hierarchical Distance-Preserving Multi-Hash Data Representation for Collaborative Analytics

The Minimum Barrier Distance: A Summary of Recent Advances

The k -max distance in graphs and images

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