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| == M-Step == | | == M-Step == |
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| = Results - What you found = | | = Results = |
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| == Retinotopic models in native space ==
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| Some text. Some analysis. Some figures.
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| == Retinotopic models in individual subjects transformed into MNI space ==
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| Some text. Some analysis. Some figures.
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| == Retinotopic models in group-averaged data on the MNI template brain ==
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| Some text. Some analysis. Some figures. Maybe some equations.
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| === Equations===
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| If you want to use equations, you can use the same formats that are use on wikipedia. <br>
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| ''See wikimedia help on [http://meta.wikimedia.org/wiki/Help:Displaying_a_formula formulas] for help.'' <br>
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| This example of equation use is copied and pasted from [http://en.wikipedia.org/wiki/Discrete_Fourier_transform wikipedia's article on the DFT].
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| The [[sequence]] of ''N'' [[complex number]]s ''x''<sub>0</sub>, ..., ''x''<sub>''N''−1</sub> is transformed into the sequence of ''N'' complex numbers ''X''<sub>0</sub>, ..., ''X''<sub>''N''−1</sub> by the DFT according to the formula:
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| :<math>X_k = \sum_{n=0}^{N-1} x_n e^{-\frac{2 \pi i}{N} k n} \quad \quad k = 0, \dots, N-1</math>
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| where i is the imaginary unit and <math>e^{\frac{2 \pi i}{N}}</math> is a primitive N'th [[root of unity]]. (This expression can also be written in terms of a [[DFT matrix]]; when scaled appropriately it becomes a [[unitary matrix]] and the ''X''<sub>''k''</sub> can thus be viewed as coefficients of ''x'' in an [[orthonormal basis]].)
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| The transform is sometimes denoted by the symbol <math>\mathcal{F}</math>, as in <math>\mathbf{X} = \mathcal{F} \left \{ \mathbf{x} \right \} </math> or <math>\mathcal{F} \left ( \mathbf{x} \right )</math> or <math>\mathcal{F} \mathbf{x}</math>.
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| The '''inverse discrete Fourier transform (IDFT)''' is given by
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| :<math>x_n = \frac{1}{N} \sum_{k=0}^{N-1} X_k e^{\frac{2\pi i}{N} k n} \quad \quad n = 0,\dots,N-1.</math>
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| == Retinotopic models in group-averaged data projected back into native space ==
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| Some text. Some analysis. Some figures.
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| = Conclusions = | | = Conclusions = |
Back to Psych 221 Projects 2013
Introduction
The purpose of the camera forensics project is to automatically detect whether an image, that was produced by CFA interpolation, was tampered with. CFA interpolation is used by digital camera to generate digital images. The interpolation will result in specific statistical patterns in the pixels of an image, which and then be utilized to determined whether or not an image has been altered.
Background
What is CFA Interpolation?
Different Types of CFA Interpolation Techniques
Bilinear/Bicubic
Smooth Hue Transition
Gradient Based
Adaptive Color Plane
Threshold-Based Variable Number of Gradients
Methods
EM Algorithm
E-Step
M-Step
Results
Conclusions
Here is where you say what your results mean.
References
Software
Appendix I - Code and Data
Code
File:CodeFile.zip
Data
zip file with my data
Appendix II - Work partition (if a group project)
Brian and Bob gave the lectures. Jon mucked around on the wiki.