We were discussing Signature verification methods. We reviewed the stages involved with Signature verification yesterday. Let us continue to list and compare online and offline verification techniques.

The feature extraction techniques involved include:
1) using an SVM classifier to extract random transform and fractal dimension
2) using neural network to extract curvlet transform, Hough transform
3) using Euclidean distance and least square error classifier for point density and spatial frequency
4) using statistical analysis techniques and chi-square test
5) using feature vector correlation for projection and local point density
6) using svm for Radon transformation
7) using learning techniques
8) using neural network for directional features

Online signature feature extraction also include:
1) signing time
2) signature width and height
3) number of pen-ups and pen-downs
4) total signature length and
5) velocity of pen

Feature extraction depends on pre-processing. Images may need to be loaded, resized, thinned, rotated and cropped.

grayscale is made into binary image with the use of threshold as
(mu1 +mu2) / 2

#codingexercise
We were discussing a coding exercise as shown below:
A person wants to buy L items from her favorite store such that a subset of N items must contain D distinct items.  the items range from 1 to A in price. Determine the maximum amount of money the person can spend.

We discussed a technique for building the combinations in a greedy manner by choosing the highest priced items first. We also discussed an alternate way to enumerate all possible combinations and select only the ones that match the criteria and return the one that has the maximum purchase.
Another way to reduce enumerations of unnecessary combinations would be to use the enumerations only from combinations with repetitions instead of exhaustive combinations.