<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>

CC BY-SA 4.0
2018-09-13T14:20:51.467Z
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.
Bart
CC BY-SA 4.0
2018-09-13T14:23:20.550Z
@Bart I have edited the title.
Ronith 
<convolutional-neural-networks>
Doubt regarding research paper on Crowd Counting using Convolutional neural networks and Markov Random Field
Ronith 
# 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.

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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.  

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## 0 answers

