Patent ID: 11967066
Assignee: DAEGU GYEONGBUK INSTITUTE OF SCIENCE AND TECHNOLOGY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. The transitory computer-readable medium according to claim 9, wherein the octave convolution-based neural network further comprises:
a link block configured to connect the plurality of octave encoder blocks and the plurality of octave decoder blocks; and
an edge decoder block configured to be connected to the link block and output an edge image for the received image, and
wherein the link block is configured to provide, to the edge decoder block and the octave decoder block, a low-frequency feature map for the image received from the plurality of octave encoder blocks,
wherein the octave convolution-based neural network is configured to use an L1 loss function and an edge loss function,
wherein the L1 loss function is defined as:, L
     1
    
    (
    
     
      y
      ^
     
     ,
     y
    
    )
   
   =
   
    
     1
     K
    
    ⁢
    
     
      ∑
      
       k
       =
       1
      
      K
     
      
     
      
       1
       
        
         H
         k
        
        ⁢
        
         W
         k
        
        ⁢
        
         C
         k
        
       
      
      ⁢
      
       
        
        
         
          
           y
           ^
          
          k
         
         -
         
          y
          k
         
        
        
       
       1
      
     
    
   
  
  ,, and
wherein the edge loss function is defined as:, L
     E
    
    (
    
     
      y
      ^
     
     ,
     y
    
    )
   
   =
   
    
     1
     K
    
    ⁢
    
     
      ∑
      
       k
       =
       1
      
      K
     
      
     
      
       1
       
        
         H
         k
        
        ⁢
        
         W
         k
        
        ⁢
        
         C
         k
        
       
      
      ⁢
      
       
        
        
         
          sobel
          ⁡
          (
          
           
            y
            ^
           
           k
          
          )
         
         -
         
          sobel
          ⁡
          (
          
           y
           k
          
          )
         
        
        
       
       2
       2
      
     
    
   
  
  ,
 

where K is a batch size, Ck is a number of channels, Hk is a number of channels, Wk is a height of the high-frequency feature map, Wk is a width of the high-frequency feature map, yk is a label, {right arrow over (y)}k is a predicted value, and sobel( ) is a sobel filter.