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[[Image:step signal.png|thumb|right|452px|Examples of signals that may contain steps corrupted by noise. (a) [[Copy-number variation|DNA copy-number ratios]] from [[DNA microarray|microarray]] data, (b) [[cosmic ray]] intensity from a [[neutron monitor]], (c) rotation speed against time of [[Rhodobacter sphaeroides|R. Sphaeroides]] [[Molecular motors|flagellar motor]], and (d) red pixel intensity from a single scan line of a digital image.]]
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In [[statistics]] and [[signal processing]], '''step detection''' (also known as '''step smoothing''', '''step filtering''', '''shift detection''', '''jump detection''' or '''[[edge detection]]''') is the process of finding abrupt changes (steps, jumps, shifts) in the mean level of a [[time series]] or signal. It is usually considered as a special case of the statistical method known as [[change detection]] or change point detection. Often, the step is small and the time series is corrupted by some kind of [[noise]], and this makes the problem challenging because the step may be hidden by the noise. Therefore, statistical and/or signal processing algorithms are often required.
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The step detection problem occurs in multiple scientific and engineering contexts, for example in [[statistical process control]]<ref name="Page1955">{{cite journal
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    | author=E.S. Page
    | title=A test for a change in a parameter occurring at an unknown point
    | journal=Biometrika
    | volume=42
    | year=1955
    | pages=523−527
}}</ref> (the [[control chart]] being the most directly related method), in exploration [[geophysics]] (where the problem is to segment a [[Well logging|well-log]] recording into [[Stratigraphy|stratigraphic zones]]<ref name="Gill1970"/>), in [[genetics]] (the problem of separating [[microarray]] data into similar [[Copy-number variation|copy-number]] regimes<ref name="Snijders">{{cite journal
| first = A.M.
| last = Snijders
| coauthors = et al.
| year = 2001
| title = Assembly of microarrays for genome-wide measurement of DNA copy number
| journal = Nature Genetics
| volume = 29
| pages = 263–264
| doi = 10.1038/ng754
| issue=3
| pmid = 11687795
}}</ref>), and in [[biophysics]] (detecting state transitions in a [[molecular machine]] as recorded in time-position traces<ref name="">{{cite journal
| first = Y.
| last = Sowa
| coauthors = Rowe, A. D., Leake, M. C., Yakushi, T., Homma, M., Ishijima, A., Berry, R. M.
| year = 2005
| title = Direct observation of steps in rotation of the bacterial flagellar motor
| journal = Nature
| volume = 437
| pages = 916–919
| doi = 10.1038/nature04003
| issue=7060
|bibcode = 2005Natur.437..916S
| pmid = 16208378 }}</ref>). For 2D signals, the related problem of [[edge detection]] has been studied intensively for [[image processing]].<ref name="Serra1982">{{cite book
  | last = Serra
  | first = J.P.
  | title = Image analysis and mathematical morphology
  | publisher = Academic Press
  | location = London; New York
  | year = 1982}}</ref>
 
== Algorithms ==
When the step detection must be performed as and when the data arrives, then [[online algorithm]]s are usually used,<ref>{{cite book
  | last = Basseville
  | first = M.
  | coauthors = I.V. Nikiforov
  | title = Detection of Abrupt Changes: Theory and Application
  | publisher = Prentice Hall
  | year = 1993}}</ref>
and it becomes a special case of [[sequential analysis]].
Such algorithms include the classical [[CUSUM]] method applied to changes in mean.
<ref>
Rodionov, S.N., 2005a: A brief overview of the regime shift detection methods. [http://www.beringclimate.noaa.gov/regimes/rodionov_overview.pdf link to PDF] In: Large-Scale Disturbances (Regime Shifts) and Recovery in Aquatic Ecosystems: Challenges for Management Toward Sustainability, V. Velikova and N. Chipev (Eds.), UNESCO-ROSTE/BAS Workshop on Regime Shifts, 14–16 June 2005, Varna, Bulgaria, 17-24.
</ref>
 
By contrast, ''offline'' algorithms are applied to the data potentially long after it has been received. Most offline algorithms for step detection in digital data can be categorised as ''top-down'', ''bottom-up'', ''sliding window'', or ''global'' methods.
 
=== Top-down ===
These algorithms start with the assumption that there are no steps and introduce possible candidate steps one at a time, testing each candidate to find the one that minimizes some criteria (such as the least-squares fit of the estimated, underlying piecewise constant signal). An example is the  ''stepwise jump placement'' algorithm, first studied in geophysical problems,<ref name="Gill1970">{{cite journal
| last = Gill
| first = D.
| year = 1970
| title = Application of a statistical zonation method to reservoir evaluation and digitized log analysis
| journal = American Association of Petroleum Geologists Bulletin
| volume = 54
| pages = 719−729}}</ref> that has found recent uses in modern biophysics.<ref name="Kers2006">{{cite journal
| last = Kerssemakers
| first = J.W.J.
| coauthors = Munteanu, E.L., Laan, L., Noetzel, T.L., Janson, M.E., Dogterom, M.
| year = 2006
| title = Assembly dynamics of microtubules at molecular resolution
| journal = Nature
| volume = 442
| doi = 10.1038/nature04928
| pages = 709−712
| issue=7103|bibcode = 2006Natur.442..709K }}</ref>
 
=== Bottom-up ===
Bottom-up algorithms take the "opposite" approach to top-down methods, first assuming that there is a step in between every sample in the digital signal, and then successively merging steps based on some criteria tested for every candidate merge.
 
=== Sliding window ===
By considering a small "window" of the signal, these algorithms look for evidence of a step occurring within the window. The window "slides" across the time series, one time step at a time. The evidence for a step is tested by statistical procedures, for example, by use of the two-sample [[Student's t-test]]. Alternatively, a [[nonlinear filter]] such as the [[median filter]] is applied to the signal. Filters such as these attempt to remove the noise whilst preserving the abrupt steps.
 
=== Global ===
Global algorithms consider the entire signal in one go, and attempt to find the steps in the signal by some kind of optimization procedure. Algorithms include [[wavelet]] methods,<ref name="Mallat2009">{{cite journal
| last = Mallat
| first = S.
| coauthors = Hwang, W.L.
| year = 1992
| title = Singularity detection and processing with wavelets
| journal = IEEE Transactions on Information Theory
| volume = 38
| pages = 617−643
| doi = 10.1109/18.119727
| issue = 2}}</ref> and [[total variation denoising]] which uses methods from [[convex optimization]]. Where the steps can be modelled as a [[Markov chain]], then [[Hidden Markov Models]] are also often used (a popular approach in the biophysics community<ref>{{cite doi | 10.1529/biophysj.106.082487 }}</ref>). When there are only a few unique values of the mean, then [[k-means clustering]] can also be used.
 
== Linear versus nonlinear signal processing methods for step detection ==
Because steps and (independent) noise have theoretically infinite [[Bandwidth (signal processing)|bandwidth]] and so overlap in the [[Fourier transform|Fourier basis]], [[signal processing]] approaches to step detection generally do not use classical smoothing techniques such as the [[low pass filter]]. Instead, most algorithms are explicitly nonlinear or time-varying.<ref name="Little2011">
{{Cite journal
  | last = Little
  | first = M.A.
  | coauthors = Jones, N.S.
  | title = Generalized Methods and Solvers for Piecewise Constant Signals: Part I
  | journal = [[Proceedings of the Royal Society A]]
  | url = http://web.media.mit.edu/~maxl/publications/pwc_filtering_arxiv.pdf
  | year = 2011 }}</ref>
 
== Step detection and piecewise constant signals ==
Because the aim of step detection is to find a series of instantaneous jumps in the mean of a signal, the wanted, underlying, mean signal is [[piecewise constant]]. For this reason, step detection can be profitably viewed as the problem of recovering a piecewise constant signal corrupted by noise. There are two complementary models for piecewise constant signals: as [[spline (mathematics)|0-degree splines]] with a few knots, or as [[level set]]s with a few unique levels. Many algorithms for step detection are therefore best understood as either 0-degree spline fitting, or level set recovery, methods.
 
=== Step detection as level set recovery ===
When there are only a few unique values of the mean, clustering techniques such as [[k-means clustering]] or [[mean-shift]] are appropriate. These techniques are best understood as methods for finding a level set description of the underlying piecewise constant signal.
 
=== Step detection as 0-degree spline fitting ===
Many algorithms explicitly fit 0-degree splines to the noisy signal in order to detect steps (including stepwise jump placement methods<ref name="Gill1970"/><ref name="Kers2006"/>), but there are other popular algorithms that can also be seen to be spline fitting methods after some transformation, for example [[total variation denoising]].<ref name="Chan">
{{cite journal
| last = Chan
| first = D.
| coauthors = T. Chan
| year = 2003
| title = Edge-preserving and scale-dependent properties of total variation regularization
| journal = Inverse Problems
| volume = 19
| pages = S165–S187
| bibcode = 2003InvPr..19S.165S
| doi = 10.1088/0266-5611/19/6/059
| issue = 6}}
</ref>
 
=== Generalized step detection by piecewise constant denoising ===
All the algorithms mentioned above have certain advantages and disadvantages in particular circumstances, yet, a surprisingly large number of these step detection algorithms are special cases of a more general algorithm.<ref name="Little2011"/> This algorithm involves the minimization of a global functional:<ref name="Mrazek">
{{Cite conference
  | first = P.
  | last = Mrazek
  | coauthors = Weickert, J., Bruhn, A.
  | title = On robust estimation and smoothing with spatial and tonal kernels
  | booktitle = Geometric properties for incomplete data
  | publisher = Springer
  | location = Berlin, Germany
  | year = 2006 }}</ref>
 
{{NumBlk|:|<math>H[m]=\sum_{i=1}^N\sum_{j=1}^N \Lambda(x_i-m_j,m_i-m_j,x_i-x_j,i-j)</math>|{{EquationRef|1}}}}
 
Here, ''x''<sub>''i''</sub> for ''i''&nbsp;=&nbsp;1,&nbsp;....,&nbsp;''N'' is the input discrete-time input signal of length ''N'', and ''m''<sub>''i''</sub> is the signal output from the algorithm. The goal is to minimize ''H''[''m''] with respect to the output signal&nbsp;''m''. The form of the function <math>\scriptstyle \Lambda</math> determines the particular algorithm. For example, choosing:
 
:<math>\Lambda = \frac12 \left|x_i-m_j\right|^2 I(i-j=0) + \gamma\left|m_i-m_j\right| I(i-j=1)</math>
 
where ''I''(''S'')&nbsp;=&nbsp;0 if the condition ''S'' is false, and one otherwise, obtains the [[total variation denoising]] algorithm with regularization parameter <math>\gamma</math>. Similarly:
 
:<math>\Lambda=\min \left\{\frac12 \left|m_i-m_j\right|^2,W\right\}</math>
 
leads to the [[mean shift]] algorithm, when using an adaptive step size Euler integrator initialized with the input signal&nbsp;''x''.<ref name="Mrazek"/> Here ''W''&nbsp;>&nbsp;0 is a parameter that determines the support of the mean shift kernel. Another example is:
 
:<math>\Lambda = \frac{1-\exp(-\beta|m_i-m_j|^2/2)}{\beta} \cdot I(|i-j|\le W)</math>
 
leading to the [[bilateral filter]], where <math>\scriptstyle \beta>0</math> is the tonal kernel parameter, and ''W'' is the spatial kernel support. Yet another special case is:
 
:<math>\Lambda= \frac12 \left|x_i-m_j\right|^2 I(i-j=0) + \gamma \left|m_i-m_j\right|^0 I(i-j=1)</math>
 
specifying a group of algorithms that attempt to greedily fit 0-degree splines to the signal.<ref name="Gill1970"/><ref name="Kers2006"/> Here, <math>\scriptstyle \left|x\right|^0</math> is defined as zero if ''x''&nbsp;=&nbsp;0, and one otherwise.
 
Many of the functionals in equation ({{EquationNote|1}}) defined by the particular choice of <math>\scriptstyle \Lambda</math> are [[Convex function|convex]]: they can be minimized using methods from [[convex optimization]]. Still others are non-convex but a range of algorithms for minimizing these functionals have been devised.<ref name="Mrazek"/>
 
== References ==
{{Reflist}}
 
== External links ==
*[http://www.maxlittle.net/software/ PWCTools: Flexible Matlab software for step detection by piecewise constant denoising]
 
{{DEFAULTSORT:Step Detection}}
[[Category:Change detection]]
[[Category:Nonlinear filters]]
[[Category:Signal processing]]
[[Category:Feature detection]]
[[Category:Time series analysis]]

Latest revision as of 02:17, 19 December 2014

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