Patent ID: 11887279
Assignee: SHARIF UNIVERSITY OF TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 3:
4. The method of claim 1, wherein applying the FCN on the nth training image comprises:
extracting an (L+1)th plurality of training feature maps from an output of an Lth convolutional layer of a plurality of convolutional layers associated with the FCN where L is a number of the plurality of convolutional layers, extracting the (L+1)th plurality of training feature maps comprising obtaining an (l+1)th plurality of training feature maps where 1≤l≤L, by:
generating an (l+1)th plurality of filtered training feature maps by applying an lth plurality of filters on an lth plurality of training feature maps, a first plurality of training feature maps comprising the plurality of training channels, a number of the lth plurality of filters equal to Mr, where:, r
  =
  
   
    ⌈
    
     l
     R
    
    ⌉
   
   ⁢
      
   where
   ⁢
      
   
    ⌈
    .
    ⌉
   
  
 

is a ceiling operator,
R is a positive integer,
Mt<Mt+1 where, t
    ≤
    
     
      ⌈
      
       l
       
        2
        ⁢
        R
       
      
      ⌉
     
     -
     1
    
   
   ,
  
 

 and
Ms+1<Ms where, s
   ≥
   
    ⌈
    
     l
     
      2
      ⁢
      R
     
    
    ⌉
   
  
  ;
 

generating an (l+1)th plurality of normalized training feature maps by applying a batch normalization process on the (l+1)th plurality of filtered training feature maps, each normalized training feature map of the (l+1)th plurality of normalized training feature maps associated with a respective filtered training feature map of the (l+1)th plurality of filtered training feature maps; and
generating the (l+1)th plurality of training feature maps by implementing an lth non-linear activation function on each of the (l+1)th plurality of normalized training feature maps; and

applying a sigmoid function on each of the (L+1)th plurality of training feature maps.