NPorfilio

Back to Psych 204 Projects 2009

Background

Image quality is one of the most important considerations when deciding which camera to purchase. Image quality is affected by a camera’s internal mechanics but also by external factors.

With respect to a camera’s internal factors, image quality is affected by:

Lens: The lens transfers the visual information of the object to be depicted onto the plane of the image sensor. Sensor: The sensor is an array of millions of tiny pixels that produce the final image. When you press your camera's shutter button and the exposure begins, each of these pixels has a photosite which is uncovered to collect and store photons in a cavity. Once the exposure finishes, the camera closes each of these photosites, and then tries to assess how many photons fell into each (Cambridge in Colour). http://www.cambridgeincolour.com/tutorials/camera-sensors.htm Sharpness, distortion, vignetting, Lateral Chromatic Aberration, noise, and dynamic range are the principal factors that can be measured at this stage. IMATEST • Image processing pipeline: The image processing pipeline is a set of digital adjustments made to the pre-processed image taken from the lens and sensor, and then transferred into the pipeline. It typically includes demosaicing, color correction, white balance, application of gamma and tonal response curves, sharpening, and noise reduction. The output of the pipeline may be compared to the minimally-processed lens and sensor measurements. However, the effect of the pipeline on subjective image quality can be highly scene and application-dependent, making it difficult to assign objective rankings. IMATEST REFERNCE

Oftentimes, image quality is a function of camera price—many consumers expect a more expensive camera to deliver a higher quality image. But is this always the case? Does an expensive DSLR outperform a compact digital camera that is one-third its price? My project investigates two cameras on the market--the Nikon D5100 and the Sony Cyber-shot DSC-T700—using a subset of the factors discussed above and compares the quality of the image outputted from each.

You can use subsections if you like. Below is an example of a retinotopic map. Or, to be precise, below will be an example of a retinotopic map once the image is uploaded. To add an image, simply put text like this inside double brackets 'MyFile.jpg | My figure caption'. When you save this text and click on the link, the wiki will ask you for the figure.
Figure 1

Below is another example of a reinotopic map in a different subject.
Figure 2

Once you upload the images, they look like this. Note that you can control many features of the images, like whether to show a thumbnail, and the display resolution.

Figure 3

MNI space

MNI is an abbreviation for Montreal Neurological Institute.

Methods

Measuring retinotopic maps

Retinotopic maps were obtained in 5 subjects using Population Receptive Field mapping methods Dumoulin and Wandell (2008). These data were collected for another research project in the Wandell lab. We re-analyzed the data for this project, as described below.

Subjects

Subjects were 5 healthy volunteers.

MR acquisition

Data were obtained on a GE scanner. Et cetera.

MR Analysis

The MR data was analyzed using mrVista software tools.

Pre-processing

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.

PRF model fits

PRF models were fit with a 2-gaussian model.

MNI space

After a pRF model was solved for each subject, the model was trasnformed into MNI template space. This was done by first aligning the high resolution t1-weighted anatomical scan from each subject to an MNI template. Since the pRF model was coregistered to the t1-anatomical scan, the same alignment matrix could then be applied to the pRF model.
Once each pRF model was aligned to MNI space, 4 model parameters - x, y, sigma, and r^2 - were averaged across each of the 6 subjects in each voxel.

Et cetera.

Results - What you found

Retinotopic models in native space

Some text. Some analysis. Some figures.

Retinotopic models in individual subjects transformed into MNI space

Some text. Some analysis. Some figures.

Retinotopic models in group-averaged data on the MNI template brain

Some text. Some analysis. Some figures. Maybe some equations.

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

[1] Dr. Karl Lenhardt, Bad Kreuznach http://www.schneiderkreuznach.com/knowhow/opt_quali_e.htm

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.