Gaussian blurring

[nontas]

We need to decide whether to add the option of Gaussian blurring in our builders. I ran some indicative experiments with holistic AAMs (features=double_igo, diagonal=180, scales=(1., 0.5, 0.25), scale_shapes=True, scale_features=True, n_shape=[5, 10, 15], n_appearance=100, max_iters=60) and the blurring seems to constantly increase the performance. I used LFPW training and testing sets. See the following tables for details:

1) ModifiedAlternatingInverseCompositional algorithm:

Blurring	noise_std	0.02	0.03	0.04	0.05	mean	median	std
False	0.0	31.2	78.6	92.4	98.7	0.0252	0.0231	0.0096
True	0.0	31.2	81.7	94.6	99.1	0.0246	0.0229	0.0083
False	0.04	30.4	76.3	87.5	92.0	0.0286	0.0230	0.0190
True	0.04	32.1	78.6	90.6	93.8	0.0269	0.0230	0.0150

2) WibergInverseCompositional algorithm:

Blurring	noise_std	0.02	0.03	0.04	0.05	mean	median	std
False	0.0	31.2	78.1	92.4	98.2	0.0253	0.0232	0.0096
True	0.0	31.7	81.2	94.2	98.7	0.0248	0.0231	0.0087
False	0.04	30.4	75.9	87.5	92.0	0.0286	0.0230	0.0188
True	0.04	30.4	79.5	91.5	95.5	0.0265	0.0231	0.0142

However, after mentioning this to @jalabort , it seems that it is not easy to add this option for all builders. The biggest problem seems to be how to determine the value of sigma that would be used in the gaussian_pyramid method.