Fixing the bug in the Feature Vector generation for SIFT key points

This is a continuation of the previous blog. This section explains the change in the momentCalc6 function

Bug & Fix: The calculation of the circular regions around the point was done incorrectly. This problem was due to the MATLAB image representation. MATLAB stores top left as (0, 0) where the synapses markup takes the bottom left as the image origin (0,0). To over come this the original image is just flipped upside down so that the region is grabbed correctly. The new data set is stored in the following location:
/usr/sci/crcnsdata/CRCNS/Synapses/data/roiExport3/
The data file names are:
Layer1_0_0_card_resize_p25-nearestSynapseDistance-diskSizes15-30-60-90-120.mat
Layer1_0_0_card_resize_p25-regionAttributes-diskSizes15-30-60-90-120.mat
Layer1_0_0_card_resize_p25-keyPoints-diskSizes15-30-60-90-120.mat
The key points data format has also be changed so as to include the scale and the orientation data from the SIFT data set along with the x, y location of the key points.

After fixing this the learning was done using the Decision Stump based Cascade Classifier. Below is are graphs of the training of the cascade classifier. In the below run, the dataset is skewed ie. Negative examples taken are twice the number of positive examples.


In the next 4 runs, the dataset used is a balanced one. having equal number of positive and negative examples. The positive examples and negative examples get washed out of that particular node as non-synapses, thus after 3 or 4 nodes the cascade runs out of examples to train. I think there is a bug in the DT classifier code with the inequality decision.