hypercomplex mathematical morphologyangulo/publicat/... · 2009-12-13 · 1/56. hypercomplex...
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Hypercomplex mathematical morphology
Hypercomplex mathematical morphology
Jesús [email protected] ; http://cmm.ensmp.fr/∼angulo
CMM-Centre de Morphologie Mathématique,Mathématiques et Systèmes, MINES Paristech
Mathematics and Image Analysis 2009 - MIA'09December 14-16, 2009 - Paris, France
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Hypercomplex mathematical morphology
Mathematical Morphology
Nonlinear image processing methodology based on the application oflattice theory to dene spatial operators
Main ingredient: an appropriate partial order ≤ for the set of imagevalues; e.g., natural ordering for grey-level images
Two basic operators
Denition (Dilation and Erosion)
δB(f )(x) = f (y) : f (y) =∨
[f (z)], z ∈ Bx,
εB(f )(x) = f (y) : f (y) =∧
[f (z)], z ∈ Bx.
The structuring element B ⊂ E introduces the shape/size of thespatial eect
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Hypercomplex mathematical morphology
Mathematical Morphology
Nonlinear image processing methodology based on the application oflattice theory to dene spatial operators
Main ingredient: an appropriate partial order ≤ for the set of imagevalues; e.g., natural ordering for grey-level images
Two basic operators
Denition (Dilation and Erosion)
δB(f )(x) = f (y) : f (y) =∨
[f (z)], z ∈ Bx,
εB(f )(x) = f (y) : f (y) =∧
[f (z)], z ∈ Bx.
The structuring element B ⊂ E introduces the shape/size of thespatial eect
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Hypercomplex mathematical morphology
Mathematical Morphology
Nonlinear image processing methodology based on the application oflattice theory to dene spatial operators
Main ingredient: an appropriate partial order ≤ for the set of imagevalues; e.g., natural ordering for grey-level images
Two basic operators
Denition (Dilation and Erosion)
δB(f )(x) = f (y) : f (y) =∨
[f (z)], z ∈ Bx,
εB(f )(x) = f (y) : f (y) =∧
[f (z)], z ∈ Bx.
The structuring element B ⊂ E introduces the shape/size of thespatial eect
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Hypercomplex mathematical morphology
Mathematical Morphology
Nonlinear image processing methodology based on the application oflattice theory to dene spatial operators
Main ingredient: an appropriate partial order ≤ for the set of imagevalues; e.g., natural ordering for grey-level images
Two basic operators
Denition (Dilation and Erosion)
δB(f )(x) = f (y) : f (y) =∨
[f (z)], z ∈ Bx,
εB(f )(x) = f (y) : f (y) =∧
[f (z)], z ∈ Bx.
The structuring element B ⊂ E introduces the shape/size of thespatial eect
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Hypercomplex mathematical morphology
Morphological operators
Image ltering using centre of size 3
f (x) ζ(f ) = [f ∨ (γϕγ(f ) ∧ ϕγϕ(f ))]∧ f − ζ(f )
(γϕγ(f ) ∨ ϕγϕ(f ))
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Hypercomplex mathematical morphology
Morphological operators
Image ltering using centre of size 3
f (x) ζ(f ) = [f ∨ (γϕγ(f ) ∧ ϕγϕ(f ))]∧ f − ζ(f )
(γϕγ(f ) ∨ ϕγϕ(f ))
bf (x), σ = 354 / 56
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Hypercomplex mathematical morphology
Morphological operators
Image simplication using opening by reconstruction
f (x) m(x)
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Hypercomplex mathematical morphology
Morphological operators
Image simplication using opening by reconstruction
f (x) m(x) γrec (f ,m) = δigeo(f ,m) ∧m (*)
(*) δigeo(f ,m) = δ1geo(f , δi−1geo (f ,m)); δ1geo(f ,m) = δB (m) ∧m
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Hypercomplex mathematical morphology
Morphological operators
Image simplication using opening by reconstruction
f (x) m(x) γrec (f ,m) = δigeo(f ,m) ∧m (*)
(*) δigeo(f ,m) = δ1geo(f , δi−1geo (f ,m)); δ1geo(f ,m) = δB (m) ∧m
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Hypercomplex mathematical morphology
Regularized mathematical morphology
Our fundamental objective: To introduce regularized dilations anderosion
State-of-the art,
n−rank pseudo-morphologySoft morphology and fuzzy logic morphologyStatistical physics-based morphologySelf-dual morphologyViscous and micro-viscous morphology
Our framework: Formulation using geometric algebra representations
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Hypercomplex mathematical morphology
Regularized mathematical morphology
Our fundamental objective: To introduce regularized dilations anderosion
State-of-the art,
n−rank pseudo-morphologySoft morphology and fuzzy logic morphologyStatistical physics-based morphologySelf-dual morphologyViscous and micro-viscous morphology
Our framework: Formulation using geometric algebra representations
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Hypercomplex mathematical morphology
Regularized mathematical morphology
Our fundamental objective: To introduce regularized dilations anderosion
State-of-the art,
n−rank pseudo-morphologySoft morphology and fuzzy logic morphologyStatistical physics-based morphologySelf-dual morphologyViscous and micro-viscous morphology
Our framework: Formulation using geometric algebra representations
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Hypercomplex mathematical morphology
Plan
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Introduction
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Introduction
Introduction
Context Classical complex-valued signal processing model
The analytic signal for one-dimensional signals:
f (t) 7→ fA(t) = f (t) + ifHi (t)
where fHi (t) is the Hilbert transformation, i.e., fHi (t) = f (t) ∗ 1πt .
In the frequency domain: FA(u) = F (u) + iFHi (u) =F (u) (1 + sign(u)) (the Hilbert transform is a phase shifting thesignal by −π/2).The polar representation yields the local amplitude and the localphase.
Extensions to 2D signals:Quaternionic Fourier transform by (Bulow and Sommer, 2001);Monogenic signal (Riesz transform) by (Felsberg and Sommer, 2001);2D scalar-valued images are embedded into the geometric algebra ofthe Euclidean 4D space and then the image structures aredecomposed using monogenic curvature tensor (Zang and Sommer,2007).
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Hypercomplex mathematical morphology
Introduction
Introduction
Context Classical complex-valued signal processing model
The analytic signal for one-dimensional signals:
f (t) 7→ fA(t) = f (t) + ifHi (t)
where fHi (t) is the Hilbert transformation, i.e., fHi (t) = f (t) ∗ 1πt .
In the frequency domain: FA(u) = F (u) + iFHi (u) =F (u) (1 + sign(u)) (the Hilbert transform is a phase shifting thesignal by −π/2).The polar representation yields the local amplitude and the localphase.
Extensions to 2D signals:Quaternionic Fourier transform by (Bulow and Sommer, 2001);Monogenic signal (Riesz transform) by (Felsberg and Sommer, 2001);2D scalar-valued images are embedded into the geometric algebra ofthe Euclidean 4D space and then the image structures aredecomposed using monogenic curvature tensor (Zang and Sommer,2007).
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Hypercomplex mathematical morphology
Introduction
Introduction
Context Classical complex-valued signal processing model
The analytic signal for one-dimensional signals:
f (t) 7→ fA(t) = f (t) + ifHi (t)
where fHi (t) is the Hilbert transformation, i.e., fHi (t) = f (t) ∗ 1πt .
In the frequency domain: FA(u) = F (u) + iFHi (u) =F (u) (1 + sign(u)) (the Hilbert transform is a phase shifting thesignal by −π/2).
The polar representation yields the local amplitude and the localphase.
Extensions to 2D signals:Quaternionic Fourier transform by (Bulow and Sommer, 2001);Monogenic signal (Riesz transform) by (Felsberg and Sommer, 2001);2D scalar-valued images are embedded into the geometric algebra ofthe Euclidean 4D space and then the image structures aredecomposed using monogenic curvature tensor (Zang and Sommer,2007).
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Hypercomplex mathematical morphology
Introduction
Introduction
Context Classical complex-valued signal processing model
The analytic signal for one-dimensional signals:
f (t) 7→ fA(t) = f (t) + ifHi (t)
where fHi (t) is the Hilbert transformation, i.e., fHi (t) = f (t) ∗ 1πt .
In the frequency domain: FA(u) = F (u) + iFHi (u) =F (u) (1 + sign(u)) (the Hilbert transform is a phase shifting thesignal by −π/2).The polar representation yields the local amplitude and the localphase.
Extensions to 2D signals:Quaternionic Fourier transform by (Bulow and Sommer, 2001);Monogenic signal (Riesz transform) by (Felsberg and Sommer, 2001);2D scalar-valued images are embedded into the geometric algebra ofthe Euclidean 4D space and then the image structures aredecomposed using monogenic curvature tensor (Zang and Sommer,2007).
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Hypercomplex mathematical morphology
Introduction
Introduction
Context Classical complex-valued signal processing model
The analytic signal for one-dimensional signals:
f (t) 7→ fA(t) = f (t) + ifHi (t)
where fHi (t) is the Hilbert transformation, i.e., fHi (t) = f (t) ∗ 1πt .
In the frequency domain: FA(u) = F (u) + iFHi (u) =F (u) (1 + sign(u)) (the Hilbert transform is a phase shifting thesignal by −π/2).The polar representation yields the local amplitude and the localphase.
Extensions to 2D signals:Quaternionic Fourier transform by (Bulow and Sommer, 2001);Monogenic signal (Riesz transform) by (Felsberg and Sommer, 2001);2D scalar-valued images are embedded into the geometric algebra ofthe Euclidean 4D space and then the image structures aredecomposed using monogenic curvature tensor (Zang and Sommer,2007).
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Hypercomplex mathematical morphology
Introduction
Introduction
ApproachWhat about a morphological complex representation?
f (x) 7→ fC (x) = f (x) + iψB(f )(x)
Aim To construct (hyper)-complex image representations which willbe endowed with total orderings and consequently, which will lead tocomplete lattices.
Rationale To obtain regularized morphological operators whoseresult depends not only on the sup/inf of the grey values, locallycomputed in the structuring element, but also on dierentialinformation or more regional information.
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Hypercomplex mathematical morphology
Introduction
Introduction
ApproachWhat about a morphological complex representation?
f (x) 7→ fC (x) = f (x) + iψB(f )(x)
Aim To construct (hyper)-complex image representations which willbe endowed with total orderings and consequently, which will lead tocomplete lattices.
Rationale To obtain regularized morphological operators whoseresult depends not only on the sup/inf of the grey values, locallycomputed in the structuring element, but also on dierentialinformation or more regional information.
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Hypercomplex mathematical morphology
Introduction
Introduction
ApproachWhat about a morphological complex representation?
f (x) 7→ fC (x) = f (x) + iψB(f )(x)
Aim To construct (hyper)-complex image representations which willbe endowed with total orderings and consequently, which will lead tocomplete lattices.
Rationale To obtain regularized morphological operators whoseresult depends not only on the sup/inf of the grey values, locallycomputed in the structuring element, but also on dierentialinformation or more regional information.
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Morphological complex image
ψ-complex image
Mapping from the scalar image to the complex image
f (x) 7→ fC (x) = f (x) + iψB(f )(x),
where ψB(f )(x) is the transformation ψ applied to f (x) ∈ F(E , T )according to the shape and size associated to the structuringelement B,
with fC ∈ F(E , T × iT ).
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Morphological complex image
The data of the bivalued image are discrete complex numbers:fC (x) = cn = an + ibn, where an and bn are respectively the real andthe imaginary part of the complex of index n in the nite spaceT × iT ⊂ C.
Let us consider the polar representation, i.e., cn = ρn exp (iθn),where the modulus is given by
ρn = |cn| =√
a2n + b2n
and the phase is computed as
θn = arg (cn) = atan2 (bn, an) = sign(bn) atan (|bn|/an) ,
with atan2 (·) ∈ (−π, π]. The phase can be mapped to [0, 2π) byadding 2π to negative values.
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Morphological complex image
The data of the bivalued image are discrete complex numbers:fC (x) = cn = an + ibn, where an and bn are respectively the real andthe imaginary part of the complex of index n in the nite spaceT × iT ⊂ C.
Let us consider the polar representation, i.e., cn = ρn exp (iθn),where the modulus is given by
ρn = |cn| =√
a2n + b2n
and the phase is computed as
θn = arg (cn) = atan2 (bn, an) = sign(bn) atan (|bn|/an) ,
with atan2 (·) ∈ (−π, π]. The phase can be mapped to [0, 2π) byadding 2π to negative values.
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Total orderings for complex images
Working in the polar representation, two alternative total orderings basedon lexicographic cascades can be dened for complex numbers:
Ordering ≤Ω
θ01
cn ≤Ωθ01
cm ⇔ρn < ρm or
ρn = ρm and θn θ0 θm
and
Ordering ≤Ω
θ02
cn ≤Ωθ02
cm ⇔θn ≺θ0 θm or
θn =θ0 θm and ρn ≤ ρm
where θ0 depends on the angular dierence to a reference angle θ0 onthe unit circle.
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Total orderings for complex images
θ0-reference based phase ordering θ0
θn θ0 θm ⇔
(θn ÷ θ0) > (θm ÷ θ0) or
(θn ÷ θ0) = (θm ÷ θ0) and θn ≤ θm
such that
θp ÷ θq =
| θp − θq | if | θp − θq |≤ π
2π− | θp − θq | if | θp − θq |> π
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Total orderings for complex images
Given now a set Z ⊂ E of pixels of the initial image [f (z)]z∈Z , thebasic idea behind our approach is to use the morphological compleximage to calculate the indirectly the supremum and the inmum
Formally, we have
Indirect total ordering 4Ω
θ01
fC (y) ≤Ω
θ01
fC (z) =⇒ f (y) 4Ω
θ01
f (z),
where 4Ω
θ01
allows to compute the supremum∨
Ωθ01
and the inmum∧Ω
θ01
in the original scalar-valued image, i.e.,
fC (y) =∨
Ωθ01 ,z∈Z
[fC (z)] =⇒ f (y) =∨
Ωθ01 ,z∈Z
[f (z)] .
Alternatively, f (y) = Re fC (y)
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Total orderings for complex images
Given now a set Z ⊂ E of pixels of the initial image [f (z)]z∈Z , thebasic idea behind our approach is to use the morphological compleximage to calculate the indirectly the supremum and the inmum
Formally, we have
Indirect total ordering 4Ω
θ01
fC (y) ≤Ω
θ01
fC (z) =⇒ f (y) 4Ω
θ01
f (z),
where 4Ω
θ01
allows to compute the supremum∨
Ωθ01
and the inmum∧Ω
θ01
in the original scalar-valued image, i.e.,
fC (y) =∨
Ωθ01 ,z∈Z
[fC (z)] =⇒ f (y) =∨
Ωθ01 ,z∈Z
[f (z)] .
Alternatively, f (y) = Re fC (y)
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Hypercomplex mathematical morphology
Complex representation and associated total orderings
Total orderings for complex images
Given now a set Z ⊂ E of pixels of the initial image [f (z)]z∈Z , thebasic idea behind our approach is to use the morphological compleximage to calculate the indirectly the supremum and the inmum
Formally, we have
Indirect total ordering 4Ω
θ01
fC (y) ≤Ω
θ01
fC (z) =⇒ f (y) 4Ω
θ01
f (z),
where 4Ω
θ01
allows to compute the supremum∨
Ωθ01
and the inmum∧Ω
θ01
in the original scalar-valued image, i.e.,
fC (y) =∨
Ωθ01 ,z∈Z
[fC (z)] =⇒ f (y) =∨
Ωθ01 ,z∈Z
[f (z)] .
Alternatively, f (y) = Re fC (y)18 / 56
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Hypercomplex mathematical morphology
Complex dilations and erosions
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Complex dilations and erosions
γ-complex image
Opening: γB(f ) = δB (εB(f ))
γ-complex image: fC (x) = f (x) + iγBC(f )(x)
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Hypercomplex mathematical morphology
Complex dilations and erosions
γ-complex image
Opening: γB(f ) = δB (εB(f ))
γ-complex image: fC (x) = f (x) + iγBC(f )(x)
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Hypercomplex mathematical morphology
Complex dilations and erosions
ϕ-complex image
Closing: ϕB(f ) = εB (δB(f ))
ϕ-complex image: fC (x) = f (x) + iϕBC(f )(x)
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Hypercomplex mathematical morphology
Complex dilations and erosions
γ-complex dilations and ϕ-complex erosions
(Ω
π/21 , γ
)-complex dilation8<:fC (x) = f (x) + iγBC (f )(x),
δ〈1,γBC,B〉(f )(x) = f (y) : fC (y) =
WΩ
π/21
[fC (z)], z ∈ B(x)).
and(Ω
π/21 , ϕ
)-complex erosion8<:fC (x) = f (x) + iϕBC (f )(x),
ε〈1,ϕBC,B〉(f )(x) = f (y) : fC (y) =
VΩ
π/21
[fC (z)], z ∈ B(x)).
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Hypercomplex mathematical morphology
Complex dilations and erosions
γ-complex dilations and ϕ-complex erosions
The equivalent(Ω
π/22 , γ
)-complex dilation and
(Ω
π/22 , ϕ
)-complex
erosion are respectively
δ〈2,γBC ,B〉(f )(x) = f (y) : fC (y) =∨
Ωπ/22
[fC (z)], z ∈ B(x)),
and
ε〈2,ϕBC,B〉(f )(x) = f (y) : fC (y) =
∧Ω
π/22
[fC (z)], z ∈ B(x)),
where the complex part of the image fC (x) is again an opening for thedilation and a closing for the erosion.
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Hypercomplex mathematical morphology
Complex dilations and erosions
Properties
Proposition
The pair(δ〈1,γBC ,B〉, ε〈1,ϕBC
,B〉
)is an adjunction, i.e.,
δ〈1,γBC ,B〉(f )(x) 4Ω
π/21
g(x) ⇔ f (x) 4Ω
π/21
ε〈1,ϕBC,B〉(g)(x),
∀f , g ∈ F(E , T ). Similarly, the pair(δ〈2,γBC ,B〉, ε〈2,ϕBC
,B〉
)is also an
adjunction.
Proposition
The two pairs of γ−complex dilation and ϕ−complex erosion are dualoperators, i.e.,
δ〈1,γBC ,B〉(f ) =[ε〈1,ϕBC
,B〉(fc)
]c; δ〈2,γBC ,B〉(f ) =
[ε〈2,ϕBC
,B〉(fc)
]c25 / 56
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Hypercomplex mathematical morphology
Complex dilations and erosions
Interpretation
Complex plane for points ∈ f (x) + iψB(f )(x).
By the anti-extensivity of opening, we have f (x) ≥ γB(f )(x) andhence 0 ≤ θ ≤ π/4.
By the extensivity of closing: ϕB(f )(x)/f (x) ≥ 1 ⇒ π/4 ≤ θ ≤ π/2.
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Hypercomplex mathematical morphology
Complex dilations and erosions
Example: γ, ϕ-complex operators
f δB (f ) εB (f )
δ〈1,γBC,B〉(f ) δ〈2,γBC
,B〉(f ) ε〈1,ϕBC,B〉(f ) ε〈2,ϕBC
,B〉(f )
B = 5, BC = 3
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Hypercomplex mathematical morphology
Complex dilations and erosions
Example: γ, ϕ-complex operators
f ϕB (f ) γB (f )
ϕ〈1,(ϕBC,γBC
),B〉(f ) ϕ〈2,(ϕBC,γBC
),B〉(f ) γ〈1,(γBC,ϕBC
),B〉(f ) γ〈2,(γBC,ϕBC
),B〉(f )
B = 5, BC = 3
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Hypercomplex mathematical morphology
Complex dilations and erosions
τ+-complex and τ−-complex images
White top-hat: τ+B (f ) = f − γB(f )
Black top-hat: τ−B (f ) = ϕB(f )− f
τ+-complex image: fC (x) = f (x) + iτ+BC
(f )(x)
τ−-complex image: fC (x) = f (x)− i [τ−BC(f )(x)]c
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Hypercomplex mathematical morphology
Complex dilations and erosions
τ+-complex dilations and τ−-complex erosions
(Ω
π/41 , τ+
)-complex dilation8><>:
fC (x) = f (x) + iτ+BC
(f )(x),
δ〈1,τ+BC
,B〉(f )(x) = f (y) : fC (y) =W
Ωπ/41
[fC (z)], z ∈ B(x)).
and(Ω−π/41 ,−[τ−]c
)-complex erosion8><>:
fC (x) = f (x)− i [τ−BC
(f )(x)]c ,
ε〈1,−[τ−BC
]c ,B〉(f )(x) = f (y) : fC (y) =V
Ω−π/41
[fC (z)], z ∈ B(x)).
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Hypercomplex mathematical morphology
Complex dilations and erosions
τ+-complex dilations and τ−-complex erosions
(Ω
π/42 , τ+
)-complex dilation8><>:
fC (x) = f (x) + iτ+BC
(f )(x),
δ〈2,τ+BC
,B〉(f )(x) = f (y) : fC (y) =W
Ωπ/42
[fC (z)], z ∈ B(x)).
and(Ω−π/42 ,−[τ−]c
)-complex erosion8><>:
fC (x) = f (x)− i [τ−BC
(f )(x)]c ,
ε〈2,−[τ−BC
]c ,B〉(f )(x) = f (y) : fC (y) =V
Ω−π/42
[fC (z)], z ∈ B(x)).
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Hypercomplex mathematical morphology
Complex dilations and erosions
Example: τ+, τ−-complex representation
f (x) τ+B (f )(x) τ−
B(f )(x)
ρ(x), f (x) + iτ+B (f )(x) θ(x), f (x) + iτ+
B (f )(x) θ(x)÷ π/4
ρ(x), f (x)− i [τ−B
(f )(x)]c θ(x), f (x)− i [τ−B
(f )(x)]c θ(x)÷−π/4
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Natural hypercomplex generalisation: Quaternions
We generalise the previous ideas by the extension to imagerepresentations based on hypercomplex numbers or real quaternions.
Before that, we remind the foundations of quaternions...
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Natural hypercomplex generalisation: Quaternions
We generalise the previous ideas by the extension to imagerepresentations based on hypercomplex numbers or real quaternions.
Before that, we remind the foundations of quaternions...
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Reminder on quaternions
Quaternion
A quaternion q ∈ H may be represented in hypercomplex form as
q = a + bi + cj + dk ,
where a, b, c and d are real.
A quaternion has a real part or scalar part, S(q) = a, and animaginary part or vector part, V (q) = bi + cj + dk :
q = S(q) + V (q)
A quaternion with a zero scalar part is called a pure quaternion.
Addition of quaternions
The addition of two quaternions, q,q′ ∈ H, is dened as follows q+ q′ =(a + a′) + (b + b′)i + (c + c ′)j + (d + d ′)j . The addition is commutativeand associative.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Reminder on quaternions
Product of two quaternions
The product of two quaternions, q,q′ ∈ H, is dened as follows
q′′ = qq′ = (aa′ − bb′ − cc ′ − dd ′) + (ab′ + ba′ + cd ′ − dc ′)i+(ac ′ + ca′ + db′ − bd ′)j + (ad ′ + a′d + bc ′ − cb′)k
In terms of dot and cross product of vectors:q′′ = qq′ = S(q′′) + V (q′′), with
S(q′′) = S(q)S(q′)− V (q) · V (q′),
andV (q′′) = S(q)V (q′) + S(q′)V (q) + V (q)× V (q′),
where · and × represent the dot product vector and the crossproduct vector respectively.
The multiplication of quaternions is not commutative, i.e.,qq′ 6= q′q; but it is associative.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Quaternion polar form
Any quaternion may be represented in polar form as
q = ρeξθ
with
ρ =√a2 + b2 + c2 + d2 is the modulus of q;
ξ = bi+cj+dk√b2+c2+d2
is the pure unitary quaternion associated to q (by
the normalisation, the pure unitary quaternion discards intensityinformation, but retains orientation information), sometimes calledeigenaxis;
θ = arctan(√
b2+c2+d2
a
)is the angle, sometimes called eigenangle,
between the real part and the 3D imaginary part.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Quaternion parallel/perpendicular decomposition
A full quaternion q may be decomposed about a pure unit quaternion pu:
q = q⊥ + q‖,
the parallel part of q according to pu, also called the projection part, isgiven by
q‖ = S(q) + V‖(q),
and the perpendicular part, also named the rejection part, is obtained as
q⊥ = V⊥(q),
where
V‖(q) =1
2(V (q)− puV (q)pu)
and
V⊥(q) =1
2(V (q) + puV (q)pu).
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Total orderings for quaternion images
Working in the polar representation, three alternative total orderings forhypercomplex numbers:
Ordering ≤Ωq01
qn ≤Ωq01
qm ⇔
8<:ρn < ρm or
ρn = ρm and θn ≺θ0 θm or
ρn = ρm and θn =θ0 θm and ‖ξn − ξ0‖ ≥ ‖ξm − ξ0‖
Ordering ≤Ωq02
qn ≤Ωq02
qm ⇔
8<:θn ≺θ0 θm or
θn =θ0 θm and ρn < ρm or
θn =θ0 θm and ρn = ρm and ‖ξn − ξ0‖ ≥ ‖ξm − ξ0‖
Ordering ≤Ωq03
qn ≤Ωq03
qm ⇔
8<:‖ξn − ξ0‖ > ‖ξm − ξ0‖ or
‖ξn − ξ0‖ = ‖ξm − ξ0‖ and ρn < ρm or
‖ξn − ξ0‖ = ‖ξm − ξ0‖ and ρn = ρm and θn θ0 θm
where q0 is the quaternion of reference.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Total orderings for quaternion images
We can also introduce another total ordering based on the ‖ / ⊥decomposition along reference quaternion q0 as follows,
Ordering ≤Ωq04
qn ≤Ωq04
qm ⇔
8>>>>>><>>>>>>:
|q‖ n| < |q‖ m| or|q‖ n| = |q‖ m| and |q⊥ n| > |q⊥ m| or|q‖ n| = |q‖ m| and |q⊥ n| = |q⊥ m| and8<:
bn < bm or
bn = bm and cn < cm or
bn = bm and cn = cm and dn ≤ dm
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Morphological hypercomplex image
~Ψ-hypercomplex image
Given the four-variate transformation
~Ψ =(ψ0B0, ψI
BI, ψJ
BJ, ψK
BK
)applied to the scalar image f (x) ∈ F(E , T ), the ~Ψ-hypercomplex imageis dened by the mapping
f (x) 7→ fH(x) = ψ0B0
(f )(x) + iψIBI
(f )(x) + jψJBJ
(f )(x) + kψKBK
(f )(x).
where the four transformations are scalar operators, i.e., ψB : T → T .
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Hypercomplex dilations and hypercomplex erosions
(~Ψ+,Ωq0
+
)-hypercomplex dilation
A generic pair of(~Ψ+,Ωq0
+
)-hypercomplex dilation is given by the
expression8><>:~Ψ+ = (ψ0+
B0, ψI+
BI, ψJ+
BJ, ψK+
BK) : f (x) 7→ fH(x),
δ〈Ψ+,Ωq0+ ,B〉(f )(x) = f (y) : fH(y) =
WΩq0+
[fH(z)], z ∈ B(x))
(~Ψ−,Ωq0
−
)-hypercomplex erosion
A generic pair of(~Ψ−,Ωq0
−
)-hypercomplex erosion is given by the
expression8><>:~Ψ− = (ψ0−
B0, ψI−
BI, ψJ−
BJ, ψK−
BK) : f (x) 7→ fH(x),
ε〈Ψ−,Ωq0+ ,B〉(f )(x) = f (y) : fH(y) =
VΩq0−
[fH(z)], z ∈ B(x))42 / 56
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Hypercomplex mathematical morphology
Two kinds of degrees of freedom must be set up to have totallystated the operator:
the hypercomplex transformation, which includes the set ofstructuring elements B0,BI ,BJ ,BK and,the quaternionic ordering, which includes the choice of the referencequaternion.
The hypercomplex transformation allows to introduce directionaleects according to the main grid directions in 3D images:
fH(x) = ψ0B0
(f )(x) + iψILx
(f )(x) + jψJLy
(f )(x) + kψKLz
(f )(x).
where B0 is an isotropic (disk) structuring element of size s (whichcan be s = 0 so the transformation is the identity) and where Lx isan linear structuring element of size x according the direction x .For the transformations ψB , the openings/closings and the top-hatsare useful, but also the internal/external gradients, which favor thedilation/erosion of points close to the object contours.Any of the quaternionic orderings can be used; but the ordering Ωq0
4
is particularly interesting.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Hypercomplex mathematical morphology
Two kinds of degrees of freedom must be set up to have totallystated the operator:
the hypercomplex transformation, which includes the set ofstructuring elements B0,BI ,BJ ,BK and,the quaternionic ordering, which includes the choice of the referencequaternion.
The hypercomplex transformation allows to introduce directionaleects according to the main grid directions in 3D images:
fH(x) = ψ0B0
(f )(x) + iψILx
(f )(x) + jψJLy
(f )(x) + kψKLz
(f )(x).
where B0 is an isotropic (disk) structuring element of size s (whichcan be s = 0 so the transformation is the identity) and where Lx isan linear structuring element of size x according the direction x .
For the transformations ψB , the openings/closings and the top-hatsare useful, but also the internal/external gradients, which favor thedilation/erosion of points close to the object contours.Any of the quaternionic orderings can be used; but the ordering Ωq0
4
is particularly interesting.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Hypercomplex mathematical morphology
Two kinds of degrees of freedom must be set up to have totallystated the operator:
the hypercomplex transformation, which includes the set ofstructuring elements B0,BI ,BJ ,BK and,the quaternionic ordering, which includes the choice of the referencequaternion.
The hypercomplex transformation allows to introduce directionaleects according to the main grid directions in 3D images:
fH(x) = ψ0B0
(f )(x) + iψILx
(f )(x) + jψJLy
(f )(x) + kψKLz
(f )(x).
where B0 is an isotropic (disk) structuring element of size s (whichcan be s = 0 so the transformation is the identity) and where Lx isan linear structuring element of size x according the direction x .For the transformations ψB , the openings/closings and the top-hatsare useful, but also the internal/external gradients, which favor thedilation/erosion of points close to the object contours.
Any of the quaternionic orderings can be used; but the ordering Ωq04
is particularly interesting.
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Hypercomplex mathematical morphology
Generalisation to multi-operator cases using real quaternions
Hypercomplex mathematical morphology
Two kinds of degrees of freedom must be set up to have totallystated the operator:
the hypercomplex transformation, which includes the set ofstructuring elements B0,BI ,BJ ,BK and,the quaternionic ordering, which includes the choice of the referencequaternion.
The hypercomplex transformation allows to introduce directionaleects according to the main grid directions in 3D images:
fH(x) = ψ0B0
(f )(x) + iψILx
(f )(x) + jψJLy
(f )(x) + kψKLz
(f )(x).
where B0 is an isotropic (disk) structuring element of size s (whichcan be s = 0 so the transformation is the identity) and where Lx isan linear structuring element of size x according the direction x .For the transformations ψB , the openings/closings and the top-hatsare useful, but also the internal/external gradients, which favor thedilation/erosion of points close to the object contours.Any of the quaternionic orderings can be used; but the ordering Ωq0
4
is particularly interesting.43 / 56
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Hypercomplex mathematical morphology
Comparative examples
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Comparative examples
Example: Center ζ(f ) = [f ∨ (γϕγ(f ) ∧ ϕγϕ(f ))] ∧ (γϕγ(f ) ∨ ϕγϕ(f ))
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c) ~Ψ+/− =“
γD10/ϕD10 , γLx10/ϕLx10
, γLy10
/ϕLy10
, 0”, Ω
q04 , q0 = i + j
(d) ~Ψ+/− =“
γD10/ϕD10 , γLx10/ϕLx10
, γLy10
/ϕLy10
, 0”, Ω
q04 , q0 = i
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Hypercomplex mathematical morphology
Comparative examples
Example: Center ζ(f ) = [f ∨ (γϕγ(f ) ∧ ϕγϕ(f ))] ∧ (γϕγ(f ) ∨ ϕγϕ(f ))
bf , σ = 35
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c) ~Ψ+/− =“
γD10/ϕD10 , γLx10/ϕLx10
, γLy10
/ϕLy10
, 0”, Ω
q04 , q0 = i + j
(d) ~Ψ+/− =“
γD10/ϕD10 , γLx10/ϕLx10
, γLy10
/ϕLy10
, 0”, Ω
q04 , q0 = i
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Hypercomplex mathematical morphology
Comparative examples
Example: Top-hat τ+B
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D10
(c)“Ω
π/22 , γBC
”-complex dilation,
“Ω
π/22 , ϕBC
”-complex erosion, BC = D10
(d) ~Ψ+/− =“Id/Id, %Lx10
/− %Lx10, %
Ly10
/− %Ly10
, 0”, Ω
q04 , q0 = i + j
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Hypercomplex mathematical morphology
Comparative examples
Example: Contrast operator using erosion and dilation
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/42 , τ+
BC
”-complex dilation,
“Ω−π/42 ,−[τ−
BC]c
”-complex erosion, BC = D3
(c) ~Ψ+/− =
„τ+D3
/− [τ−D3
]c , τ+Lx3
/− [τ−Lx3
]c , τ+
Ly3/− [τ−
Ly3]c , 0
«, Ω
q04 , q0 = i + j
(d) ~Ψ+/− =“Id/Id, %Lx10
/− %Lx10, %
Ly10
/− %Ly10
, 0”, Ω
q04 , q0 = i + j
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Hypercomplex mathematical morphology
Comparative examples
Example: Gradient %B(f ) = δB(f )− εB(f )
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/42 , τ+
BC
”-complex dilation,
“Ω−π/42 ,−[τ−
BC]c
”-complex erosion, BC = D5
(c) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
(d) ~Ψ+/− =“Id/Id, %Lx5
/− %Lx5, %
Ly10
/− %Ly10
, 0”, Ω
q04 , q0 = i + j
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Hypercomplex mathematical morphology
Comparative examples
Example: Edge detection
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/42 , τ+
BC
”-complex dilation,
“Ω−π/42 ,−[τ−
BC]c
”-complex erosion, BC = D5
(c) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
(d) ~Ψ+/− =“Id/Id, %Lx5
/− %Lx5, %
Ly10
/− %Ly10
, 0”, Ω
q04 , q0 = i + j
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Hypercomplex mathematical morphology
Comparative examples
Example: Opening by reconstruction
f
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c)“Ω
π/42 , τ+
BC
”-complex dilation,
“Ω−π/42 ,−[τ−
BC]c
”-complex erosion, BC = D5
(d) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
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Hypercomplex mathematical morphology
Comparative examples
Example: Opening by reconstruction
f , σ = 30
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c)“Ω
π/42 , τ+
BC
”-complex dilation,
“Ω−π/42 ,−[τ−
BC]c
”-complex erosion, BC = D5
(d) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
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Hypercomplex mathematical morphology
Comparative examples
Example: Levelling λ(f ,m) (Cartoon + Texture/noiseDecomposition)
f
mrk = f ∗ Gσ
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c) ~Ψ+/− =`Id/Id, γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
(d) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
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Hypercomplex mathematical morphology
Comparative examples
Example: Levelling λ(f ,m) (Cartoon + Texture/noiseDecomposition)
f
mrk = f ∗ Gσ
(a) Standard (b)
(c) (d)
(b)“Ω
π/21 , γBC
”-complex dilation,
“Ω
π/21 , ϕBC
”-complex erosion, BC = D5
(c) ~Ψ+/− =`Id/Id, γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
(d) ~Ψ+/− =`γB0/ϕB0 , γBI /ϕBI
, γBJ /ϕBJ, 0
´, Ω
q04 , q0 = i + j, B0 = D5, BI = Lx5 , BJ = L
y5
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Hypercomplex mathematical morphology
Conclusions and perspectives
1 Introduction
2 Complex representation and associated total orderings
3 Complex dilations and erosions
4 Generalisation to multi-operator cases using real quaternions
5 Comparative examples
6 Conclusions and perspectives
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Hypercomplex mathematical morphology
Conclusions and perspectives
Conclusions and Perspectives
Morphological operators for grey-level images based on indirect totalorderings using hypercomplex image representations.
The motivation was to introduce in the basic erosion/dilationoperators some information on size invariance or on relative contrastof structures.
Other representations using upper dimensional Cliord Algebras canbe foreseen in order to have a more generic framework not limited tofour-variables image representations.
Approach can also be extended to already natural multivariateimages (i.e., multispectral images) → tensor representations andassociated total orderings.
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Hypercomplex mathematical morphology
Conclusions and perspectives
Conclusions and Perspectives
Morphological operators for grey-level images based on indirect totalorderings using hypercomplex image representations.
The motivation was to introduce in the basic erosion/dilationoperators some information on size invariance or on relative contrastof structures.
Other representations using upper dimensional Cliord Algebras canbe foreseen in order to have a more generic framework not limited tofour-variables image representations.
Approach can also be extended to already natural multivariateimages (i.e., multispectral images) → tensor representations andassociated total orderings.
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Hypercomplex mathematical morphology
Conclusions and perspectives
Conclusions and Perspectives
Morphological operators for grey-level images based on indirect totalorderings using hypercomplex image representations.
The motivation was to introduce in the basic erosion/dilationoperators some information on size invariance or on relative contrastof structures.
Other representations using upper dimensional Cliord Algebras canbe foreseen in order to have a more generic framework not limited tofour-variables image representations.
Approach can also be extended to already natural multivariateimages (i.e., multispectral images) → tensor representations andassociated total orderings.
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Hypercomplex mathematical morphology
Conclusions and perspectives
Conclusions and Perspectives
Morphological operators for grey-level images based on indirect totalorderings using hypercomplex image representations.
The motivation was to introduce in the basic erosion/dilationoperators some information on size invariance or on relative contrastof structures.
Other representations using upper dimensional Cliord Algebras canbe foreseen in order to have a more generic framework not limited tofour-variables image representations.
Approach can also be extended to already natural multivariateimages (i.e., multispectral images) → tensor representations andassociated total orderings.
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Hypercomplex mathematical morphology
Conclusions and perspectives
Conclusions and Perspectives
Morphological operators for grey-level images based on indirect totalorderings using hypercomplex image representations.
The motivation was to introduce in the basic erosion/dilationoperators some information on size invariance or on relative contrastof structures.
Other representations using upper dimensional Cliord Algebras canbe foreseen in order to have a more generic framework not limited tofour-variables image representations.
Approach can also be extended to already natural multivariateimages (i.e., multispectral images) → tensor representations andassociated total orderings.
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