Body: <p>I am currently reading the research paper <a href="https://arxiv.org/pdf/1706.03686.pdf" rel="nofollow noreferrer">Image Crowd Counting Using Convolutional Neural Network and Markov Random Field</a>  by Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou. <br/>
  I did not understand the following context properly: </p>
  
  <blockquote>
    <p>Formally, the
    Markov random field framework for the crowd counting
    can be defined as follows (we follow the notation in [18]).
    Let P be the set of patches in an image and C be a possi-
    ble set of counts. A counting c assigns a count c p ∈ C to
    each patch p ∈ P. The quality of a counting is given by an
    energy function:</p>
    
    <p>E(c) =
    ∑ D p (c p ) + ∑
    p∈P
    V (c p − c q )
    . . . (2)
    (p,q)∈N</p>
    
    <p>where N are the (undirected) edges in the four-connected
    image patch graph. D p (c p ) is the cost of assigning count
    c p to patch p, and is referred to as the data cost. V (c p −c q )
    measures the cost of assigning count c p and c q to two
    neighboring patch, and is normally referred to as the dis-
    continuity cost.
    For the problem of smoothing the adjacent patches
    count, D p (c p ) and V (c p − c q ) can take the form of the
    following functions:
    D p (c p ) = λ min((I(p) − c p ) 2 , DATA K) . . . (3)
    V (c p − c q ) = min((c p − c q ) 2 , DISC K)
    . . . (4)
    where λ is a weight of the energy items, I(p) is the ground
    truth count of the patch p, DATA K and DISC K are the
    truncating item of D p (c p ) and V (c p − c q ), respectively.</p>
  </blockquote>
  
  <hr>
  
  <p>Can anyone explain the above part in detail and give me a detailed insight on how should I implement this part of the project? </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-09-13T14:20:51.467Z
Comments.Text: Is there any way in which you could come up with a more descriptive title? Particularly for others who face the same issue as you do. Right now the title is so generic and non-descriptive, that any great answers you may receive will not be found by others in a search.
Comments.UserDisplayName: Bart
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-09-13T14:23:20.550Z
Comments.Text: @Bart I have edited the title.
Comments.UserDisplayName: Ronith 
Tags: <convolutional-neural-networks>
Title: Doubt regarding research paper on Crowd Counting using Convolutional neural networks and Markov Random Field
UserDisplayName: Ronith 
ThreadText: # Doubt regarding research paper on Crowd Counting using Convolutional neural networks and Markov Random Field
  
  (Asked by: username_0 on 2018-09-13)
  
  I am currently reading the research paper [Image Crowd Counting Using Convolutional Neural Network and Markov Random Field](https://arxiv.org/pdf/1706.03686.pdf) by Kang Han, Wanggen Wan, Haiyan Yao, and Li Hou.   
  I did not understand the following context properly:
  
  > Formally, the
  > Markov random field framework for the crowd counting
  > can be defined as follows (we follow the notation in [18]).
  > Let P be the set of patches in an image and C be a possi-
  > ble set of counts. A counting c assigns a count c p ∈ C to
  > each patch p ∈ P. The quality of a counting is given by an
  > energy function:
  >
  > E(c) =
  > ∑ D p (c p ) + ∑
  > p∈P
  > V (c p − c q )
  > . . . (2)
  > (p,q)∈N
  >
  > where N are the (undirected) edges in the four-connected
  > image patch graph. D p (c p ) is the cost of assigning count
  > c p to patch p, and is referred to as the data cost. V (c p −c q )
  > measures the cost of assigning count c p and c q to two
  > neighboring patch, and is normally referred to as the dis-
  > continuity cost.
  > For the problem of smoothing the adjacent patches
  > count, D p (c p ) and V (c p − c q ) can take the form of the
  > following functions:
  > D p (c p ) = λ min((I(p) − c p ) 2 , DATA K) . . . (3)
  > V (c p − c q ) = min((c p − c q ) 2 , DISC K)
  > . . . (4)
  > where λ is a weight of the energy items, I(p) is the ground
  > truth count of the patch p, DATA K and DISC K are the
  > truncating item of D p (c p ) and V (c p − c q ), respectively.
  
  ---
  
  Can anyone explain the above part in detail and give me a detailed insight on how should I implement this part of the project?
  
  **Tags:** convolutional-neural-networks
  
  > **Comments:**
  > **username_1**: Is there any way in which you could come up with a more descriptive title? Particularly for others who face the same issue as you do. Right now the title is so generic and non-descriptive, that any great answers you may receive will not be found by others in a search.  
  > **username_0**: @username_1 I have edited the title.  
  
  ---
  
  ## 0 answers
  
  