I need a mathematical formula to give a decision based on given situation :

in my issue, there is a decision must be taken based on the following values where :

  • Total extracted feature Number in origin image is X , in my test example X is = (1496)
  • Total extracted feature Number in object is Y , in my example Y is (273).

Now starting Matching Process ... then thereis a map function takes values from X and try to find matched features in object ... this function take a values called Z where Z is less than X ( must be) in my example Z = 381 ..

Now Z include two parts where

- N = noise features number where N. 
- M = real feature number where M in my example M= 134. 

i would like to use a mathematical formula that give me a decision based on M value for example :

if score(M) for example > 0.25 then object is there otherwise not.

mu issue in every time when i changed image or object numbers are changes .. if i changed image X is changed , when i changed object Y is changed ..

I’m not familiar with mathematics formula but I would ask if log or others can applied


For me what matters are only the matching features and for all these matching feature evaluating the degree of correlation between the scores.

That is something like $\frac1V\sum\limits_{i=1}^{V}correlation(W_i,Z_i)$

This is a candid approach, but this page Wikipedia : Interclass correlation seems to bring better formulas than mine.

  • $\begingroup$ sorry, i have changed my question issue please refine your answer based on it, waiting for your modification $\endgroup$ – Mr. kiko Feb 16 '17 at 17:36

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