Temporal Scale-Spaces ScSp03
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Transcript of Temporal Scale-Spaces ScSp03
Temporal Scale Spaces
Daniel FagerströmCVAP/NADA/[email protected]
Measurement of Temporal Signals
• Real time measurement• Temporal causality• Structure at different resolution• Well defined derivatives
Earlier Approaches
• Koenderink (88), Florack (97), ter Haar Romeny et. al. (01) – Logaritmic mapping of the past time-line, then ordinary scale space
• Lindeberg & Fagerström (96) – Noncreation of local maxima with increasing scale
• Salden (99) – Diffusion on the temporal time-line with absorbing or reflecting boundary condition
Current Approach
• Using axioms from Pauwels et. al. 95, replacing reflectional symmetry with temporal causality
Space vs. Time
SpaceAll directions simultaneously available
TimeThe past can be memorized, the future can not
Memory
Overview
• Motivation of axioms• Form of temporal scale space kernels• Time recursive realization• Numerical scheme• Comparison with earlier work
Principles
• Non committed representation• Embodiment of symmetries in the
surroundings• Extended point
Measurement
Posf : xf
Sensorf : f
Meaurement
0|, 1 CL
•Choose well behaved sensor functions
Endf
fff ,
•Linearity
Examples
dtttgf
•Regular distribution
0
•Dirac ”function”
fffff
•Derivation
Causality
•Causality
0|1 tttt
•Measurement time
: t
Embodiment of Geometry
• The structure of the sensor system should correspond to regularities in the environment
• Translation covariance: the measurement process is the same at each moment
• Scaling covariance: all time spans are treated in the same way
Covariance
af
axfD
xafxfSaxfxfT
a
a
a
11 :Dilatation
:Scaling :nTranslatio
•Transformation groups
aa
aa
DffSTffT
•Corresponding action on a sensor
Orbits
0','' tt TDT
Convolution
'
'
f
dsstsfdstssfTf t
•Definition
ff
•Associativity
Cascade Property (Semi-Group)
Extended Point
•Unit area1
•Positivity 00 ff
ss 0
lim
Temporal Scale Space Kernels
• Continuity• Positivity• Unit area• Causality• Dilatation covariance• Convolution semi-groupPauwels et al used the same axioms, with (4) replaced by reflexion
symmetry, to characterize spatial scale-spaces.
Characterization
Laplace transform
ˆˆˆ
Semi-group and causality
:,ˆ ges sg
Continuity
(Cauchy’s functional equation)
Characterization (Cont) sges
ˆ
Dilatation covariance
sesˆ
Positivity
(Bernstein)
10
Temporal Scale Space Kernels0.3 0.5 0.8
set -1, L
Extremal Stable Density Functions
•Limit densities for sum of stochastic variables (Levy)
•Infinite first and second moments
•Increasing popularity in fysics, finance …
Explicit Form
0,
,1
23
2
,21
,0
!1
24exp4
k
kk
t
tkkt
t
tt
kt
tt
tt
Markov Property?
• The memory is the only access to the past• Convolution directly with the input signal is
unrealistic• Need for an evaluation equation!
t
L(0,)=0
L(t,)=f(t)
LALt tf
tLftL
,,,,
Fractional Derivatives
fxfsfxf xx ,
1, L
•Linear
•Equal to ordinary derivatives for integer order
xxx
•Generalized Leibniz property
gfgf kx
kx
kx
0 k
Evaluation Equations
,
1
-1,
expLxpe
L
t
s
set
tftLL
LL t
,lim0,0
0
,•Signaling equation for
tftLL
LLt
,lim0,0
0
22,
•Fractional Brownian motion
•Diffusion for
L
LLL
t
tt
2,
,,
2
tftLL
LLt
,lim0,0
0
1,
•Scale space as state
•Diffusion for
Uniquenes of the Signaling Equation
tftLL
LLt
,lim0,0
0
1,
•Locality: is a differential operator if 1/ is an integer
•Positivity: have a positive Greens function if 1/
•Locality and positivity is only satisfied for
L1
,
L1
,
Signaling Equation
tftL
LLLt
,lim0,0
0
2
•The temporal scale space is the only memory of earlier input
•The memory diffuses over time
Numerical Scheme
t
3/2-1/2
•Implicit scheme
•Second order stability in time and scale
•4(add)+4(multiply)+2(divide) per mesh point
Example
Example
Comparison with Previous Work
• Florack (97) requires the measurement kernel to be a positive Schwartz test function, this rules out all stable density functions except Gaussians
• Ter Haar Romeny et. al. (01) requires the measurement kernel to have finite first and second moment, only fulfilled for Gaussians, extremal (causal) stable density functions even has infinite first moment
Comparison with Earlier Work
• Koenderink (88) and Lindeberg & Fagerström (96) uses stronger requirements (than semi-group) on causality in the resolution domain
• Salden (99) does not require translational covariance on the kernel only on the generator
Comparison with Earlier Work
• Lindeberg & Fagerström (96) and the current approach are the only ones that has a (known) time recursive formulation
• Lindeberg & Fagerström (96) requires either time or scale to be discrete and lack scale covariance
• The kernels in the current approach are less well localized than in the other approaches
Conclusion
• Causal scale invariant convolution semi group
• Time recursive realization• Signaling equation• Efficient numerical scheme