Complementing the Original Code

1. Revise the "confusing matrix" to confusion matrix
   I have studied the confusionmat function in Matlab and used it to better represent the result:

From the confusion matrix above we can see the wrong predition of material 5(based on the low exposure pictures) .


2. Multi-classification
  I have used variance to recognize metal, and it works well to modify the wrong predition above(based on the low exposure pictures) .

3. Try to use two exposures
  Last week, I thought I could use the low exposure pictures to extract from the high exposure pictures, thus getting the pictures without background. But now I find that the low exposure pictures are good enough to analyse this problem. They already do not have any background information. (As below)

 What's more important is that when the high exposure pictures get abstracted by the low exposure pictures, they are just losing the important information, like the pictures below. So it's not a good way to supress the ambient light.

But since the pictures with low exposure have much less information than the high exposure pictures , I'm still trying to figure out a way to combine those two together. (Getting the average value of the two pictures just get the high exposure pictures darker)

Any suggestions?

And also there is a severe problem of alignment. Many low exposure pictures are not aligned, I suspect the good result I get in part1,2 is resulted from their location differences. My next step is to do the alignment between pictures or even within pictures(like the picture shown below). I know there are several groups in class using alignment, so I think maybe it's a better choice to ask these groups first.

Any suggestions?

To sum up:

• Questions: 1.How to combine two exposures?  2. How to align pictures?(Existing function?)
• Next step: 1.Combination and Alignment  2.Next collection of dataset.