Improvements To Illumination Variation

I have made some changes to the base images that are used for the illumination variation, however i have found that the system required some tweaking in the range of what is classified as a target as often i have found that the avc difference in chi squared between the tests is too strick, to loosen this i have simply drawn a boundary of either the average chi squared or 0.1 which is the larger.

By doing so i get a better classification result for the images.

AVC: 0.191358 AVD: 0.032299 AVL: 0.014278

2: 0.109081 Present - Normal
3: 0.045300 Present - Light
4: 0.022777 Present - Light
5: 0.058292 Present - Light
6: 0.083343 Present - Dark
7: 0.030796 Present - Light
8: 0.063477 Present - Light
9: 1.000000 Empty                      //Actually Present just extremely bright.
10: 0.060175 Present - Light
11: 0.167384 Present - Normal
12: 1.000000 Empty                     //Actually Present just extremely dark.
13: 0.156815 Present - Normal
14: 1.000000 Empty                     //Actually Present images are blurred.
15: 1.000000 Empty                     //Actually Present images are bluured.
16: 1.000000 Empty
17: 1.000000 Empty
18: 1.000000 Empty
19: 0.187468 Present - Normal          //Actually Empty image of bag strap
20: 0.171188 Present - Normal          //Actually Empty image of chemistry book
21: 0.186641 Present - Normal          //Actually Empty image of penny

I see this as a massive improvement. I was always dubious about blurring and now i can see clearly that this is an issue i have still to fix but i hope that with a test of such an image with no blurring it is picked up. This would prove to me that the blurring is the issue and not the classification of that model/style of image.


Immediate Possibilities:

1. Test Blur Theory Against Non Blurry Images


2. Check Dark Images for Improvements


3. Target Location