QuachFu

Back to Psych 221 Projects 2013

Background

Digital cameras have long since replaced traditional cameras as the dominant form of photography. Their practicality coupled with the combination of low cost and high performance have made them extremely popular. Additionally, with the advent of the camera phone, nearly every person now has a camera on them at all times. This has led to an explosion of images of nearly anything that can be easily found on the internet.

Digital Image Forgery

Combined with very power photo editing software such as Photoshop, the amount of forged images has also dramatically increased.

Methods

Expectation Maximization

Probability Map

Subjects were 5 healthy volunteers.

2D FFT

Data were obtained on a GE scanner. Et cetera.

Similarity Measure

The MR data was analyzed using mrVista software tools.

Thresholding

All data were slice-time corrected, motion corrected, and repeated scans were averaged together to create a single average scan for each subject. Et cetera.

Results

Uncompressed Images

Some text. Some analysis. Some figures.

JPEG Images

Equations

If you want to use equations, you can use the same formats that are use on wikipedia.
See wikimedia help on formulas for help.
This example of equation use is copied and pasted from wikipedia's article on the DFT.

The sequence of N complex numbers x₀, ..., x_N−1 is transformed into the sequence of N complex numbers X₀, ..., X_N−1 by the DFT according to the formula:

$X_k=\sum _{n=0}^{N-1}{x}_n{e}^{-\frac{2\pi i}{N}kn}k=0,\dots ,N-1$

where i is the imaginary unit and $e^{\frac{2\pi i}{N}}$ 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_k can thus be viewed as coefficients of x in an orthonormal basis.)

The transform is sometimes denoted by the symbol $ℱ$, as in $𝐗=ℱ\left\{𝐱\right\}$ or $ℱ\left(𝐱\right)$ or $ℱ𝐱$.

The inverse discrete Fourier transform (IDFT) is given by

$x_n=\frac{1}{N}\sum _{k=0}^{N-1}{X}_k{e}^{\frac{2\pi i}{N}kn}n=0,\dots ,N-1.$

Retinotopic models in group-averaged data projected back into native space

Some text. Some analysis. Some figures.

Conclusions

Here is where you say what your results mean.

References - Resources and related work

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.