Answers.Body: <p>Rules based on Gestalt psychology could be seen as a 'local minima' in terms of optimal image processing. Some of them could be surprisingly effective, but difficult to extend and improve upon, since they assume certain high level attributes that might not generalize well.</p>
  <p><em>&quot;If it's circular, then it's a fruit 90% of the time&quot;</em></p>
  <p>Modern methods like neural networks come at the problem from another direction, and try to build towards those high level attributes incrementally. You could say that a goal of these modern methods is to recreate a version of those 'assumptions' or 'Gestalt rules' empirically. That way, we can feel more confident that we're not using a locally optimal approach.</p>
  <p><em>&quot;This is the smallest algorithm that will accurately identify fruit 90% of the time&quot;</em></p>
  <p><strong>Are such methods being used or worked on today?</strong></p>
  <p>Yes, and not just in image processing. If you look at any industry where fast processing of ambiguous domain-specific data is needed, there are usually systems that were designed by experts in that field using 'hand-crafted' heuristics. A benefit of these approaches is that the 'Gestalt rules' are understandable apart from the system that uses it, and is therefore easier to trust.</p>
  <p><strong>Was any progress made on this? Or was this research program dropped?</strong></p>
  <p>Aside from trivial cases, employing the gestalt approach in a particular domain requires expertise. &quot;Image Expert&quot; is an expensive and hard to fill role. Even then, you're bounded by that expert's ability to effectively generalize an extremely complex problem. Over the past decades, machine learning has become a lot easier and less expensive to employ, and has been slowly replacing all those 'hand-crafted' approaches.</p>
  <p>However, one of the interesting things we're finding when we let the machine learning approaches loose is that they sometimes don't agree with our previous rules. Why they don't agree, and what this means about automation and our psychological biases, is one of the big open questions in AI.</p>
  <p>You might also be interested in Saliency Maps, and similar techniques intended to visualize the general rules used by neural networks: <a href="https://en.wikipedia.org/wiki/Saliency_map" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/Saliency_map</a></p>
  <p>References:</p>
  <p><a href="http://wayback.archive-it.org/219/20121229060421/http://www.cs.indiana.edu/%7Ejwmills/ANALOG.NOTEBOOK/klm/klm.html" rel="nofollow noreferrer">http://wayback.archive-it.org/219/20121229060421/http://www.cs.indiana.edu/~jwmills/ANALOG.NOTEBOOK/klm/klm.html</a></p>
  <p><a href="https://journals.sagepub.com/doi/pdf/10.1177/002029400403701001" rel="nofollow noreferrer">https://journals.sagepub.com/doi/pdf/10.1177/002029400403701001</a></p>
  <p><a href="https://maritime.org/doc/op1140/index.htm" rel="nofollow noreferrer">https://maritime.org/doc/op1140/index.htm</a></p>
  <p><a href="https://airandspace.si.edu/stories/editorial/inventing-apollo-spaceflight-biomedical-sensors" rel="nofollow noreferrer">https://airandspace.si.edu/stories/editorial/inventing-apollo-spaceflight-biomedical-sensors</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-30T21:14:59.023Z
Answers.Text: Maybe you could provide some references that support your claims to your first question.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-07-01T13:49:59.550Z
Answers.Text: I'd recommend reading about the transition from analog to digital computing that took place in the mid 20th century. Some examples of this shift are 70 years old, but others are still taking place. I'll add a few links for examples.
Answers.UserDisplayName: bey
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-27T19:21:31.247Z
Answers.LastActivityDate: 2020-07-01T13:53:58.750Z
Answers.LastEditDate: 2020-07-01T13:53:58.750Z
Answers.UserDisplayName: bey
Body: <p>Decades ago there were and are books in machine vision, which by implementing various information processing rules from gestalt psychology, got impressive results with little code or special hardware in image identification and visual processing.</p>
  
  <p>Are such methods being used or worked on today? Was any progress made on this? Or was this research program dropped? By today, I mean 2016, not 1995 or 2005.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2018-07-31T17:02:42.390Z
Comments.Text: Could you cite some papers / authors / textbooks that you would say represented this trend?
Comments.UserDisplayName: bukwyrm
Tags: <machine-learning><algorithm><computer-vision>
Title: Are information processing rules from Gestalt psychology still used in computer vision today?
UserDisplayName: Gottfried William
ThreadText: # Are information processing rules from Gestalt psychology still used in computer vision today?
  
  (Asked by: username_0 on 2016-10-29)
  
  Decades ago there were and are books in machine vision, which by implementing various information processing rules from gestalt psychology, got impressive results with little code or special hardware in image identification and visual processing.
  
  Are such methods being used or worked on today? Was any progress made on this? Or was this research program dropped? By today, I mean 2016, not 1995 or 2005.
  
  **Tags:** machine-learning, algorithm, computer-vision
  
  > **Comments:**
  > **username_1**: Could you cite some papers / authors / textbooks that you would say represented this trend?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-06-27. Score: 1
  
  Rules based on Gestalt psychology could be seen as a 'local minima' in terms of optimal image processing. Some of them could be surprisingly effective, but difficult to extend and improve upon, since they assume certain high level attributes that might not generalize well.
  
  *"If it's circular, then it's a fruit 90% of the time"*
  
  Modern methods like neural networks come at the problem from another direction, and try to build towards those high level attributes incrementally. You could say that a goal of these modern methods is to recreate a version of those 'assumptions' or 'Gestalt rules' empirically. That way, we can feel more confident that we're not using a locally optimal approach.
  
  *"This is the smallest algorithm that will accurately identify fruit 90% of the time"*
  
  **Are such methods being used or worked on today?**
  
  Yes, and not just in image processing. If you look at any industry where fast processing of ambiguous domain-specific data is needed, there are usually systems that were designed by experts in that field using 'hand-crafted' heuristics. A benefit of these approaches is that the 'Gestalt rules' are understandable apart from the system that uses it, and is therefore easier to trust.
  
  **Was any progress made on this? Or was this research program dropped?**
  
  Aside from trivial cases, employing the gestalt approach in a particular domain requires expertise. "Image Expert" is an expensive and hard to fill role. Even then, you're bounded by that expert's ability to effectively generalize an extremely complex problem. Over the past decades, machine learning has become a lot easier and less expensive to employ, and has been slowly replacing all those 'hand-crafted' approaches.
  
  However, one of the interesting things we're finding when we let the machine learning approaches loose is that they sometimes don't agree with our previous rules. Why they don't agree, and what this means about automation and our psychological biases, is one of the big open questions in AI.
  
  You might also be interested in Saliency Maps, and similar techniques intended to visualize the general rules used by neural networks: <https://en.wikipedia.org/wiki/Saliency_map>
  
  References:
  
  [http://wayback.archive-it.org/219/20121229060421/http://www.cs.indiana.edu/~jwmills/ANALOG.NOTEBOOK/klm/klm.html](http://wayback.archive-it.org/219/20121229060421/http://www.cs.indiana.edu/%7Ejwmills/ANALOG.NOTEBOOK/klm/klm.html)
  
  <https://journals.sagepub.com/doi/pdf/10.1177/002029400403701001>
  
  <https://maritime.org/doc/op1140/index.htm>
  
  <https://airandspace.si.edu/stories/editorial/inventing-apollo-spaceflight-biomedical-sensors>
  
  > **Comments:**
  > **username_3**: Maybe you could provide some references that support your claims to your first question.  
  > **username_2**: I'd recommend reading about the transition from analog to digital computing that took place in the mid 20th century. Some examples of this shift are 70 years old, but others are still taking place. I'll add a few links for examples.  
  
  ---
  
  