Is there something wrong with MedianFilter?

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Note: Wolfram Support confirmed that the behaviour of MedianFilter is as intended, but the description in the documentation in incorrect. M11.1 and earlier have the correct description:




For multichannel images, MedianFilter[image,...] replaces each pixel by a pixel in its neighborhood that has the median total intensity, averaged over all channels.




Original post:



MedianFilter does not give the result I expect, or the result I computed using other methods. Is it buggy?



Example:



im = ExampleData["TestImage", "Sailboat"];

m1 = MedianFilter[im, 5]


Mathematica graphics



Look at the pepper-like pinkish noise at the middle of the image. I'd expect a median filter to give a smooth result.



enter image description here



Let's implement median filtering through ImageFilter.



m2 = ImageFilter[Median@*Flatten, im, 5]


enter image description here



It looks much better. I have a small personal library to access SimpleITK. Let's try that.



m3 = obj@"median"[im, 5]


enter image description here



Not only does the ITK result look identical to the ImageFilter result, it is identical, as confirmed with ImageAdjust[m2-m3]. m1 is quite different, even in the middle (differences near edges could be due to different padding).



What's going on? Why does MedianFilter give a different result than other methods of computing the same? As far as I can tell, the neighbourhood range specification works identically for all three methods: 5 means using an 11 by 11 rectangle window.



Is there a bug?



Update: I am now convinced that this is a bug because if I manually filter each channel separately with MedianFilter, I get the expected result (the same as with ImageFilter and ITK, except around the edges).



ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]]


The documentation says that it should operate separately on each channel of multi-channel images.




  • For multichannel images and audio signals, MedianFilter operates separately on each channel.



But it clearly doesn't.



Update 2: It seems like it's not a bug after all (rather a documentation bug). As Niki says, there are ways to compute a "colour median", e.g. sort the neighbourhood pixels based on their luminance and pick the "middle one". One possible direct implementation of this is



ImageFilter[
 With[flat = Join @@ #, 
 SortBy[flat, 0.299`, 0.587`, 0.114`.# &][[Round[Length[flat]/2]]]
 ] &, 
 im, 5, Interleaving -> True
]


which gives a comparable but non-identical result.




image-processing




share|improve this question






















  • I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
    – Szabolcs
    Aug 27 at 12:12










  • MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
    – Carl Lange
    Aug 27 at 12:23










  • @CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
    – Szabolcs
    Aug 27 at 12:25










  • Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
    – Carl Lange
    Aug 27 at 12:36














up vote
10
down vote

favorite
1












Note: Wolfram Support confirmed that the behaviour of MedianFilter is as intended, but the description in the documentation in incorrect. M11.1 and earlier have the correct description:




For multichannel images, MedianFilter[image,...] replaces each pixel by a pixel in its neighborhood that has the median total intensity, averaged over all channels.




Original post:



MedianFilter does not give the result I expect, or the result I computed using other methods. Is it buggy?



Example:



im = ExampleData["TestImage", "Sailboat"];

m1 = MedianFilter[im, 5]


Mathematica graphics



Look at the pepper-like pinkish noise at the middle of the image. I'd expect a median filter to give a smooth result.



enter image description here



Let's implement median filtering through ImageFilter.



m2 = ImageFilter[Median@*Flatten, im, 5]


enter image description here



It looks much better. I have a small personal library to access SimpleITK. Let's try that.



m3 = obj@"median"[im, 5]


enter image description here



Not only does the ITK result look identical to the ImageFilter result, it is identical, as confirmed with ImageAdjust[m2-m3]. m1 is quite different, even in the middle (differences near edges could be due to different padding).



What's going on? Why does MedianFilter give a different result than other methods of computing the same? As far as I can tell, the neighbourhood range specification works identically for all three methods: 5 means using an 11 by 11 rectangle window.



Is there a bug?



Update: I am now convinced that this is a bug because if I manually filter each channel separately with MedianFilter, I get the expected result (the same as with ImageFilter and ITK, except around the edges).



ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]]


The documentation says that it should operate separately on each channel of multi-channel images.




  • For multichannel images and audio signals, MedianFilter operates separately on each channel.



But it clearly doesn't.



Update 2: It seems like it's not a bug after all (rather a documentation bug). As Niki says, there are ways to compute a "colour median", e.g. sort the neighbourhood pixels based on their luminance and pick the "middle one". One possible direct implementation of this is



ImageFilter[
 With[flat = Join @@ #, 
 SortBy[flat, 0.299`, 0.587`, 0.114`.# &][[Round[Length[flat]/2]]]
 ] &, 
 im, 5, Interleaving -> True
]


which gives a comparable but non-identical result.




image-processing




share|improve this question






















  • I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
    – Szabolcs
    Aug 27 at 12:12










  • MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
    – Carl Lange
    Aug 27 at 12:23










  • @CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
    – Szabolcs
    Aug 27 at 12:25










  • Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
    – Carl Lange
    Aug 27 at 12:36












up vote
10
down vote

favorite
1









up vote
10
down vote

favorite
1






1





Note: Wolfram Support confirmed that the behaviour of MedianFilter is as intended, but the description in the documentation in incorrect. M11.1 and earlier have the correct description:




For multichannel images, MedianFilter[image,...] replaces each pixel by a pixel in its neighborhood that has the median total intensity, averaged over all channels.




Original post:



MedianFilter does not give the result I expect, or the result I computed using other methods. Is it buggy?



Example:



im = ExampleData["TestImage", "Sailboat"];

m1 = MedianFilter[im, 5]


Mathematica graphics



Look at the pepper-like pinkish noise at the middle of the image. I'd expect a median filter to give a smooth result.



enter image description here



Let's implement median filtering through ImageFilter.



m2 = ImageFilter[Median@*Flatten, im, 5]


enter image description here



It looks much better. I have a small personal library to access SimpleITK. Let's try that.



m3 = obj@"median"[im, 5]


enter image description here



Not only does the ITK result look identical to the ImageFilter result, it is identical, as confirmed with ImageAdjust[m2-m3]. m1 is quite different, even in the middle (differences near edges could be due to different padding).



What's going on? Why does MedianFilter give a different result than other methods of computing the same? As far as I can tell, the neighbourhood range specification works identically for all three methods: 5 means using an 11 by 11 rectangle window.



Is there a bug?



Update: I am now convinced that this is a bug because if I manually filter each channel separately with MedianFilter, I get the expected result (the same as with ImageFilter and ITK, except around the edges).



ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]]


The documentation says that it should operate separately on each channel of multi-channel images.




  • For multichannel images and audio signals, MedianFilter operates separately on each channel.



But it clearly doesn't.



Update 2: It seems like it's not a bug after all (rather a documentation bug). As Niki says, there are ways to compute a "colour median", e.g. sort the neighbourhood pixels based on their luminance and pick the "middle one". One possible direct implementation of this is



ImageFilter[
 With[flat = Join @@ #, 
 SortBy[flat, 0.299`, 0.587`, 0.114`.# &][[Round[Length[flat]/2]]]
 ] &, 
 im, 5, Interleaving -> True
]


which gives a comparable but non-identical result.




image-processing




share|improve this question














Note: Wolfram Support confirmed that the behaviour of MedianFilter is as intended, but the description in the documentation in incorrect. M11.1 and earlier have the correct description:




For multichannel images, MedianFilter[image,...] replaces each pixel by a pixel in its neighborhood that has the median total intensity, averaged over all channels.




Original post:



MedianFilter does not give the result I expect, or the result I computed using other methods. Is it buggy?



Example:



im = ExampleData["TestImage", "Sailboat"];

m1 = MedianFilter[im, 5]


Mathematica graphics



Look at the pepper-like pinkish noise at the middle of the image. I'd expect a median filter to give a smooth result.



enter image description here



Let's implement median filtering through ImageFilter.



m2 = ImageFilter[Median@*Flatten, im, 5]


enter image description here



It looks much better. I have a small personal library to access SimpleITK. Let's try that.



m3 = obj@"median"[im, 5]


enter image description here



Not only does the ITK result look identical to the ImageFilter result, it is identical, as confirmed with ImageAdjust[m2-m3]. m1 is quite different, even in the middle (differences near edges could be due to different padding).



What's going on? Why does MedianFilter give a different result than other methods of computing the same? As far as I can tell, the neighbourhood range specification works identically for all three methods: 5 means using an 11 by 11 rectangle window.



Is there a bug?



Update: I am now convinced that this is a bug because if I manually filter each channel separately with MedianFilter, I get the expected result (the same as with ImageFilter and ITK, except around the edges).



ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]]


The documentation says that it should operate separately on each channel of multi-channel images.




  • For multichannel images and audio signals, MedianFilter operates separately on each channel.



But it clearly doesn't.



Update 2: It seems like it's not a bug after all (rather a documentation bug). As Niki says, there are ways to compute a "colour median", e.g. sort the neighbourhood pixels based on their luminance and pick the "middle one". One possible direct implementation of this is



ImageFilter[
 With[flat = Join @@ #, 
 SortBy[flat, 0.299`, 0.587`, 0.114`.# &][[Round[Length[flat]/2]]]
 ] &, 
 im, 5, Interleaving -> True
]


which gives a comparable but non-identical result.





image-processing





share|improve this question













share|improve this question




share|improve this question








edited Aug 31 at 14:26

























asked Aug 27 at 12:08









Szabolcs

152k13415896




152k13415896











  • I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
    – Szabolcs
    Aug 27 at 12:12










  • MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
    – Carl Lange
    Aug 27 at 12:23










  • @CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
    – Szabolcs
    Aug 27 at 12:25










  • Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
    – Carl Lange
    Aug 27 at 12:36
















  • I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
    – Szabolcs
    Aug 27 at 12:12










  • MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
    – Carl Lange
    Aug 27 at 12:23










  • @CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
    – Szabolcs
    Aug 27 at 12:25










  • Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
    – Carl Lange
    Aug 27 at 12:36















I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
– Szabolcs
Aug 27 at 12:12




I did test that this is not because MedianFilter is able to use integers (bytes) while the other two methods effectively work with floating point numbers. We can test on Image[im, "Real"].
– Szabolcs
Aug 27 at 12:12












MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
– Carl Lange
Aug 27 at 12:23




MedianFilter operates on each channel separately, I'm unsure whether ImageFilter has the same method for doing that
– Carl Lange
Aug 27 at 12:23












@CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
– Szabolcs
Aug 27 at 12:25




@CarlLange It should operate on each channel separately, but it does not (I just tested that ColorCombine[MedianFilter[#, 5] & /@ ColorSeparate[im]] gives me the result I expected)
– Szabolcs
Aug 27 at 12:25












Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
– Carl Lange
Aug 27 at 12:36




Ah, very good. That's a weird one! Unfortunately I hit a dead end after a bit of PrintDefinitionsing, no luck trying to find the specific behaviour.
– Carl Lange
Aug 27 at 12:36










1 Answer
1






active

oldest

votes

















up vote
10
down vote



accepted










This looks like a documentation bug. Apparently, MedianFilter doesn't process each channel separately. Instead, it applies a proper color median filter, like e.g. the one from the IPP. Example:



img = Image[
 Table[
 Mod[i*84 + j*83 + 0, 85, 170, 255], i, 16, j, 16], "Byte"]


enter image description here



And the median filter results:



enter image description here



The per-channel median is more or less gray. But there isn't a single gray pixel in the image. MedianFilter always seems to choose an RGB value from the filter window, i.e.:



ContainsAll[Union[Flatten[ImageData[img], 1]], 
 Union[Flatten[ImageData[MedianFilter[img, 3]], 1]]]



True







share|improve this answer






















  • "the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
    – Szabolcs
    Aug 27 at 12:59











  • I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
    – Szabolcs
    Aug 27 at 13:11










  • I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
    – Szabolcs
    Aug 27 at 13:11











  • This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
    – Szabolcs
    Aug 27 at 13:27










  • @Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
    – Niki Estner
    Aug 27 at 13:30










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1 Answer
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active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes








up vote
10
down vote



accepted










This looks like a documentation bug. Apparently, MedianFilter doesn't process each channel separately. Instead, it applies a proper color median filter, like e.g. the one from the IPP. Example:



img = Image[
 Table[
 Mod[i*84 + j*83 + 0, 85, 170, 255], i, 16, j, 16], "Byte"]


enter image description here



And the median filter results:



enter image description here



The per-channel median is more or less gray. But there isn't a single gray pixel in the image. MedianFilter always seems to choose an RGB value from the filter window, i.e.:



ContainsAll[Union[Flatten[ImageData[img], 1]], 
 Union[Flatten[ImageData[MedianFilter[img, 3]], 1]]]



True







share|improve this answer






















  • "the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
    – Szabolcs
    Aug 27 at 12:59











  • I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
    – Szabolcs
    Aug 27 at 13:11










  • I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
    – Szabolcs
    Aug 27 at 13:11











  • This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
    – Szabolcs
    Aug 27 at 13:27










  • @Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
    – Niki Estner
    Aug 27 at 13:30














up vote
10
down vote



accepted










This looks like a documentation bug. Apparently, MedianFilter doesn't process each channel separately. Instead, it applies a proper color median filter, like e.g. the one from the IPP. Example:



img = Image[
 Table[
 Mod[i*84 + j*83 + 0, 85, 170, 255], i, 16, j, 16], "Byte"]


enter image description here



And the median filter results:



enter image description here



The per-channel median is more or less gray. But there isn't a single gray pixel in the image. MedianFilter always seems to choose an RGB value from the filter window, i.e.:



ContainsAll[Union[Flatten[ImageData[img], 1]], 
 Union[Flatten[ImageData[MedianFilter[img, 3]], 1]]]



True







share|improve this answer






















  • "the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
    – Szabolcs
    Aug 27 at 12:59











  • I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
    – Szabolcs
    Aug 27 at 13:11










  • I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
    – Szabolcs
    Aug 27 at 13:11











  • This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
    – Szabolcs
    Aug 27 at 13:27










  • @Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
    – Niki Estner
    Aug 27 at 13:30












up vote
10
down vote



accepted







up vote
10
down vote



accepted






This looks like a documentation bug. Apparently, MedianFilter doesn't process each channel separately. Instead, it applies a proper color median filter, like e.g. the one from the IPP. Example:



img = Image[
 Table[
 Mod[i*84 + j*83 + 0, 85, 170, 255], i, 16, j, 16], "Byte"]


enter image description here



And the median filter results:



enter image description here



The per-channel median is more or less gray. But there isn't a single gray pixel in the image. MedianFilter always seems to choose an RGB value from the filter window, i.e.:



ContainsAll[Union[Flatten[ImageData[img], 1]], 
 Union[Flatten[ImageData[MedianFilter[img, 3]], 1]]]



True







share|improve this answer














This looks like a documentation bug. Apparently, MedianFilter doesn't process each channel separately. Instead, it applies a proper color median filter, like e.g. the one from the IPP. Example:



img = Image[
 Table[
 Mod[i*84 + j*83 + 0, 85, 170, 255], i, 16, j, 16], "Byte"]


enter image description here



And the median filter results:



enter image description here



The per-channel median is more or less gray. But there isn't a single gray pixel in the image. MedianFilter always seems to choose an RGB value from the filter window, i.e.:



ContainsAll[Union[Flatten[ImageData[img], 1]], 
 Union[Flatten[ImageData[MedianFilter[img, 3]], 1]]]



True








share|improve this answer














share|improve this answer



share|improve this answer








edited Aug 27 at 13:11









Szabolcs

152k13415896




152k13415896










answered Aug 27 at 12:56









Niki Estner

29.7k373129




29.7k373129











  • "the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
    – Szabolcs
    Aug 27 at 12:59











  • I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
    – Szabolcs
    Aug 27 at 13:11










  • I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
    – Szabolcs
    Aug 27 at 13:11











  • This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
    – Szabolcs
    Aug 27 at 13:27










  • @Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
    – Niki Estner
    Aug 27 at 13:30
















  • "the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
    – Szabolcs
    Aug 27 at 12:59











  • I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
    – Szabolcs
    Aug 27 at 13:11










  • I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
    – Szabolcs
    Aug 27 at 13:11











  • This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
    – Szabolcs
    Aug 27 at 13:27










  • @Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
    – Niki Estner
    Aug 27 at 13:30















"the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
– Szabolcs
Aug 27 at 12:59





"the one from the IPP" link is broken. I need to look up how a colour median works. Could you perhaps provide a reference?
– Szabolcs
Aug 27 at 12:59













I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
– Szabolcs
Aug 27 at 13:11




I found the answer (I did not know what IPP was). software.intel.com/en-us/ipp-dev-reference-filtermediancolor
– Szabolcs
Aug 27 at 13:11












I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
– Szabolcs
Aug 27 at 13:11





I tried to fix the link, please edit if incorrect. However, I have to say that the "median" concept described in that link is quite different from the usual statistical meaning. I find it misleading that MedianFilter does this by default, especially considering that it can also operate on arrays, time series and audio, where (I assume) it uses the usual median.
– Szabolcs
Aug 27 at 13:11













This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
– Szabolcs
Aug 27 at 13:27




This page has a more reasonable idea of how a colour median might work: itk.org/ITKExamples/src/Filtering/Smoothing/… It simply needs a comparison operator.
– Szabolcs
Aug 27 at 13:27












@Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
– Niki Estner
Aug 27 at 13:30




@Szabolcs: The common statistical definition for the median is that it's the value that minimizes the L1 distance metric. That's exactly what the IPP median function does, isn't it? (I only skimmed it, to be honest)
– Niki Estner
Aug 27 at 13:30

















 

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White Anglo-Saxon Protestant

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White Anglo-Saxon Protestants ( WASPs ) are a social group of wealthy and well-connected white Americans, of Protestant and predominantly British ancestry, who trace their ancestry to the American colonial period. Until at least the 1960s, this group has dominated American society and culture and dominated in the leadership of the Whig and Republican parties. They usually are very well placed in major financial, business, legal and academic institutions and had close to a monopoly of elite society due to intermarriage and nepotism. [1] During the latter half of the twentieth century, outsider ethnic and racial groups grew in influence and WASP dominance weakened. Americans are increasingly criticizing the WASP hegemony and disparaging WASPs as the epitome of "the Establishment". The Random House Unabridged Dictionary (1998) says the term is "Sometimes Disparaging and Offensive". [2] [3] The term WASP is often used as a pejorative to classify their historical dom...
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Is the Concept of Multiple Fantasy Races Scientifically Flawed? [closed]

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Clash Royale CLAN TAG #URR8PPP up vote 2 down vote favorite Many fantasy worlds have multiple humanoid species (elves, orcs, humans, etc) living in the same world, in addition to that, they often have the ability to interbreed. And I can't help but question their believability... fantasy-races reproduction interspecies-relations share | improve this question edited Aug 9 at 14:46 Michael Kjörling ♦ 21.1k 10 85 161 asked Aug 9 at 13:13 Chlodio 118 6 closed as unclear what you're asking by MichaelK, Gryphon, John, Vincent, Frostfyre Aug 9 at 15:25 Please clarify your specific problem or add additional details to highlight exactly what you need. As it's currently written, it’s hard to tell exactly what you're asking. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question. ...
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One-line joke

A one-liner is a joke that is delivered in a single line. A good one-liner is said to be pithy - concise and meaningful. [1] Comedians and actors use this comedic method as part of their act, e.g. Jimmy Carr, Tommy Cooper, Rodney Dangerfield, Norm Macdonald, Ken Dodd, Stewart Francis, Zach Galifianakis, Mitch Hedberg, Anthony Jeselnik, Milton Jones, Shappi Khorsandi, Jay London, Mark Linn-Baker, Demetri Martin, Groucho Marx, Dan Mintz, Emo Philips, Tim Vine, Steven Wright and Henny Youngman and Arnold Schwarzenegger. [ citation needed ] Many fictional characters are also known to deliver one-liners, including James Bond, who usually includes pithy and laconic quips after disposing of a villain. [ citation needed ] Examples "A baby seal walks into a club." "A dyslexic man walks into a bra." (Anonymous) "There are three types of people, those who can count and those who can't." "Venison's dear, isn't it?" (Jimmy Carr) "An escala...
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