Answers.Body: <p>You should first know about the layer-wise working of a convolutional neural network. Read this <a href="https://distill.pub/2018/building-blocks/" rel="nofollow noreferrer">https://distill.pub/2018/building-blocks/</a></p>
  
  <p>Each layer of a cnn forms representations that are increasingly complex , which are  combinations of simpler representations formed by its previous layers. 
  "shine" is not a pattern (from image processing perspective). shine occurs due to change in intensity values. </p>
  
  <p>And for quantifying "shine" you dont have to go for convolutional networks only , there are many more simpler methods for such tasks you could try fitting simple linear model and progress towards more complex ones.</p>
  
  <p>and for classifying textures , there is interesting work going with "invariant scattering convolutional networks" you should check that.</p>
  
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Answers.CreationDate: 2018-06-29T07:41:51.553Z
Answers.LastActivityDate: 2018-06-29T07:41:51.553Z
Answers.UserDisplayName: thecomplexitytheorist
Body: <p>I am working on a problem where I have to train a CNN to recognize different kinds of surfaces. One important characteristic of the surfaces I am interested is is how reflective they are. I have been trying to find a method that quantifies how "shiny" a surface is, but I have not found much. I am hoping that someone can point me toward a method or some research into this kind of problem.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-06-29T03:31:50.253Z
Comments.Text: It would be difficult to determine how shiny a surface is _in absolute terms_ without a reference value of some sort, but you could probably estimate the _relative_ reflectance by comparing the apparent luminance of surfaces with the background luminance (I would expect shinier surfaces to appear brighter). However, this depends on a lot of factors, mainly orientation (if a surface is facing away from a light source, it would appear darker) and internal illumination (what is the reflectance of a light bulb when it is turned on?).
Comments.UserDisplayName: cantordust
Tags: <computer-vision>
Title: How to quantify the reflectance in an image?
UserDisplayName: jdowner
ThreadText: # How to quantify the reflectance in an image?
  
  (Asked by: username_0 on 2018-06-28)
  
  I am working on a problem where I have to train a CNN to recognize different kinds of surfaces. One important characteristic of the surfaces I am interested is is how reflective they are. I have been trying to find a method that quantifies how "shiny" a surface is, but I have not found much. I am hoping that someone can point me toward a method or some research into this kind of problem.
  
  **Tags:** computer-vision
  > **Comments:**
  > **username_1**: It would be difficult to determine how shiny a surface is _in absolute terms_ without a reference value of some sort, but you could probably estimate the _relative_ reflectance by comparing the apparent luminance of surfaces with the background luminance (I would expect shinier surfaces to appear brighter). However, this depends on a lot of factors, mainly orientation (if a surface is facing away from a light source, it would appear darker) and internal illumination (what is the reflectance of a light bulb when it is turned on?).  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2018-06-29. Score: 1
  
  You should first know about the layer-wise working of a convolutional neural network. Read this <https://distill.pub/2018/building-blocks/>
  
  Each layer of a cnn forms representations that are increasingly complex , which are combinations of simpler representations formed by its previous layers.
  "shine" is not a pattern (from image processing perspective). shine occurs due to change in intensity values.
  
  And for quantifying "shine" you dont have to go for convolutional networks only , there are many more simpler methods for such tasks you could try fitting simple linear model and progress towards more complex ones.
  
  and for classifying textures , there is interesting work going with "invariant scattering convolutional networks" you should check that.
  
  
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