ScholarLens Back to search
2009•Society for Industrial and Applied Mathematics eBooksRequires access

7. Distance Measures

Jan Modersitzki

Open publisher page 0 citations

Abstract

Chapter 5 discussed feature-based distance measure and in Chapter 6 the sum of squared differences (SSD) as a prototype for an intensity-based measure was introduced. A disadvantage of the latter measure is that it assumes a correspondence of gray values of corresponding points. In this chapter, more powerful intensity-based distance measures are explored. All distance measures are considered as functionals in T and R and are phrased asD[T,R]=∫Ωϕ(T(x),R(x))dx.7.1Considering D [y]≔D [T [y],R ] enables a unified treatment in numerical optimization.

About this research paper

What this paper is about

Chapter 5 discussed feature-based distance measure and in Chapter 6 the sum of squared differences (SSD) as a prototype for an intensity-based measure was introduced. A disadvantage of the latter measure is that it assumes a correspondence of gray values of corresponding points. In this chapter, more powerful intensity-based distance measures are explored. All distance measures are considered as functionals in T and R and are phrased asD[T,R]=∫Ωϕ(T(x),R(x))dx.7.1Considering D [y]≔D [T [y],R ] enables a unified treatment in numerical optimization.

Why it matters

A significance statement is not available in the OpenAlex record.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Chapter 5 discussed feature-based distance measure and in Chapter 6 the sum of squared differences (SSD) as a prototype for an intensity-based measure was introduced. A disadvantage of the latter measure is that it assumes a correspondence of gray values of corresponding points. In this chapter, more powerful intensity-based distance measures are explored. All distance measures are considered as functionals in T and R and are phrased asD[T,R]=∫Ωϕ(T(x),R(x))dx.7.1Considering D [y]≔D [T [y],R ] enables a unified treatment in numerical optimization.

Key concepts: Measure (data warehouse), Mutual information, Mathematics, Ideal (ethics), Section (typography), Perspective (graphical), Point (geometry), Distance measures

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
7. Distance Measures — Research Paper | ScholarLens