Brute Force Histograms

I have improved a worked out the few obvious bugs with my former "histograms". Now as you can clearly see they are much more correct, and i have also altered my code to allow overlapping windows but they must differ by a window size of at least 1/4 this can be altered but i think for a start it is a good heuristic. It gives a decent coverage however the random generator doesn't seem to like the corners very much, i am not sure why as when i tested this at the start it gave me a variety of numbers from the minimum allowed to the maximum in the range, so i would expect them now maybe its simply a bad case?

The chi squared results though are lower than i had hoped, as you can see from these results they do not have a particular one which shines out as having or containing any useful information in fact the value is quite low and the margins not overly noticeable.

Chi Squared for sample image, larger values bolded.

0.0152         0.0146         0.0097         0.0097
0.0139         0.0143         0.0847         0.0050
0.0107         0.0022         0.0131         0.0086
0.0147         0.0125         0.0031         0.0152
0.0378         0.0108         0.0059         0.0035
0.0134         0.0511         0.0535         0.0062
0.0152         0.0052         0.0141         0.0929
0.0152         0.0470         0.0031         0.0102
0.0045         0.0029         0.0037         0.0071
0.0030         0.0038         0.0073         0.0105
0.0180         0.0050         0.0142         0.0061
0.0025         0.0074         0.0031         0.0138

Histogram Comparisons In Matlab

I have made some noticeable progress since Monday in that i have devised a system that finds me squares of images depending on a density and window size and for each creates a histogram and compares against a blank one. However i ahve had issues normalising these so that they equal 1. Some issue with the fact matlab doesn't properly bin the data but rather ranges it...explained better by an example image. It is infact the way matlab bins these histograms that ruins my chi squared algorithm.

Integral Histograms

Integral Histograms:

It has been decided that rgb is actually a useless colorspace for this project so after much playing around HSV has been chosen as an alternative. See bottom of post for examples of the HSI images...

After issues in OpenCv trying to reprogram again in Matlab. Can't really see the difference here, but the last bin is about 1%. Which may not seem substantial but here is the image the histogram was created from.



















HSI Image Examples:

Histogram Errors...

I have debugged my code and compared the histogram outputs to several other methods, i.e matlabs built in histogram functions, plus one histogram function i wrote for CSE166. They all give the save values as what i have in OpenCV so i think that this must just be an artifact of the image?

Meeting With AUVSI Leader Scheduled

Meeting: I have eventually managed to tie down the leader of the payload section of the AUVSI to a meeting with me on Monday. He has sent me links to some of the papers they used previously. Such as a paper: here it seems to be quite useful from the images displayed, however i cannot find source code to sue based on this and the maths looks quite rigorous. I plan on viewing this over the weekend as well as securing the histogram analysis. I would like to get the previous code from the team to find out exactly how they interacted the system together as i am wary of building up an entire system in one direction to find it is difficult to combine with the system they will be using.

To Complete:
- method for histogram standardization (windows of images)
- classifier based on distance from training data mean and std (distance based classifier)
- read paper in entirety and try and find source code
- attempt to find reasonable method for location the targets within images

Colourspace Histograms

Colourspace Histogram's: I have managed to create a solution for appending images in open CV that lets me now display the histograms for different colour planes on one image. The results are shown below:



HSI



LAB



RGB






HSI



LAB




RGB








HSI



LAB



RGB












Idea's for Future: Do the same comparisons for hsi and lab with the training set and see if they have any useful comparisions with the chi squared results or if they ahve any cleaner results that may be used. I.e replicate rgb functionality in hsi and lab and compare classification results.


Problems Ran into this Week: so far i've not been able to figure out a way of only creating histograms on a portion of an image, using the set window size. However i think today i may have stumbled upon something called setImageROI which operates only on a selected region of an image, however to do this over an image in the same way as a convolution would be very computational expensive and this first stage is supposed to be a quick check of a present target or not...and even before this inclusion it is already quite slow with the multiple operations being computed.

Colourspace Histograms Created

The varying colourspace histograms have been created, however, i can only display them as individual histograms for each plane (ie r,g,b or l,a,b or h,s,i) seperately. This is difficult to visualise the properties of each i have found, therefore i wish to find a way of appending images together using OpenCv before publishing the results here.

If i do not find a way by wednesday i shall manually do it, or simply submit the images as they are simply to show the results, but if possible i would like to have the images appended together so that RGB, LAB and HSI display together rather than each individual colourspace.