The Statistic Distribution of Image Gradient? The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)Gradient of the imagehow to introduce time into calculus of variations for image processing?Math behind Photoshop's gradient toolMaximum Eigenvalue of the Discrete Laplace Operator (Image Processing)Why not represent discrete multivariate probability distribution as univariate?Solid Ellipse Fitting on 2D Image Using Gradient DescentMagnitude and direction of image gradient geometricallyComparing the Exponential Truncated Distribution with the Exponential DistributionDistribution of sufficient statistic of negative bionomial distributionTaking SRS of a non normal/normal distribution

different output for groups and groups USERNAME after adding a username to a group

Is there a writing software that you can sort scenes like slides in PowerPoint?

Make it rain characters

Identify 80s or 90s comics with ripped creatures (not dwarves)

Didn't get enough time to take a Coding Test - what to do now?

Circular reasoning in L'Hopital's rule

Sub-subscripts in strings cause different spacings than subscripts

60's-70's movie: home appliances revolting against the owners

Deal with toxic manager when you can't quit

Is this wall load bearing? Blueprints and photos attached

should truth entail possible truth

What happens to a Warlock's expended Spell Slots when they gain a Level?

Is 'stolen' appropriate word?

How to read αἱμύλιος or when to aspirate

One-dimensional Japanese puzzle

What information about me do stores get via my credit card?

What do I do when my TA workload is more than expected?

How to type a long/em dash `—`

how can a perfect fourth interval be considered either consonant or dissonant?

1960s short story making fun of James Bond-style spy fiction

Can the DM override racial traits?

US Healthcare consultation for visitors

Drawing vertical/oblique lines in Metrical tree (tikz-qtree, tipa)

Is an up-to-date browser secure on an out-of-date OS?



The Statistic Distribution of Image Gradient?



The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)Gradient of the imagehow to introduce time into calculus of variations for image processing?Math behind Photoshop's gradient toolMaximum Eigenvalue of the Discrete Laplace Operator (Image Processing)Why not represent discrete multivariate probability distribution as univariate?Solid Ellipse Fitting on 2D Image Using Gradient DescentMagnitude and direction of image gradient geometricallyComparing the Exponential Truncated Distribution with the Exponential DistributionDistribution of sufficient statistic of negative bionomial distributionTaking SRS of a non normal/normal distribution










2












$begingroup$


The gradient of an image $f$ is defined as:



$nabla f=beginbmatrix
nabla f_x \
nabla f_y
endbmatrix = beginbmatrix
fracpartial fpartial x \
fracpartial fpartial y
endbmatrix
,
$



Its discrete calculation can be as simple as finite difference. For example



$nabla f_x = fracf_n-f_n-1x_n-x_n-1
$
and
$nabla f_y = fracf_n-f_n-1y_n-y_n-1.
$



I can simply define the totalwhole image gradient is the norm of x and y gradient component:



$||nabla f|| = sqrt(nabla f_x)^2+(nabla f_y)^2.
$ Nothing fancy so far.



Now I am just wondering, what is the distribution of the image gradient in equation above? Here is an example:



enter image description here



In above image, the histogram of the image gradient really looks exponential to me. This is just an example, but I have seen similar shape of the histogram in many cases.



Can I claim the distribution of an image gradient follows exponential? If not, with what condition I can/cannot make this guess? Thanks a lot.










share|cite|improve this question











$endgroup$
















    2












    $begingroup$


    The gradient of an image $f$ is defined as:



    $nabla f=beginbmatrix
    nabla f_x \
    nabla f_y
    endbmatrix = beginbmatrix
    fracpartial fpartial x \
    fracpartial fpartial y
    endbmatrix
    ,
    $



    Its discrete calculation can be as simple as finite difference. For example



    $nabla f_x = fracf_n-f_n-1x_n-x_n-1
    $
    and
    $nabla f_y = fracf_n-f_n-1y_n-y_n-1.
    $



    I can simply define the totalwhole image gradient is the norm of x and y gradient component:



    $||nabla f|| = sqrt(nabla f_x)^2+(nabla f_y)^2.
    $ Nothing fancy so far.



    Now I am just wondering, what is the distribution of the image gradient in equation above? Here is an example:



    enter image description here



    In above image, the histogram of the image gradient really looks exponential to me. This is just an example, but I have seen similar shape of the histogram in many cases.



    Can I claim the distribution of an image gradient follows exponential? If not, with what condition I can/cannot make this guess? Thanks a lot.










    share|cite|improve this question











    $endgroup$














      2












      2








      2





      $begingroup$


      The gradient of an image $f$ is defined as:



      $nabla f=beginbmatrix
      nabla f_x \
      nabla f_y
      endbmatrix = beginbmatrix
      fracpartial fpartial x \
      fracpartial fpartial y
      endbmatrix
      ,
      $



      Its discrete calculation can be as simple as finite difference. For example



      $nabla f_x = fracf_n-f_n-1x_n-x_n-1
      $
      and
      $nabla f_y = fracf_n-f_n-1y_n-y_n-1.
      $



      I can simply define the totalwhole image gradient is the norm of x and y gradient component:



      $||nabla f|| = sqrt(nabla f_x)^2+(nabla f_y)^2.
      $ Nothing fancy so far.



      Now I am just wondering, what is the distribution of the image gradient in equation above? Here is an example:



      enter image description here



      In above image, the histogram of the image gradient really looks exponential to me. This is just an example, but I have seen similar shape of the histogram in many cases.



      Can I claim the distribution of an image gradient follows exponential? If not, with what condition I can/cannot make this guess? Thanks a lot.










      share|cite|improve this question











      $endgroup$




      The gradient of an image $f$ is defined as:



      $nabla f=beginbmatrix
      nabla f_x \
      nabla f_y
      endbmatrix = beginbmatrix
      fracpartial fpartial x \
      fracpartial fpartial y
      endbmatrix
      ,
      $



      Its discrete calculation can be as simple as finite difference. For example



      $nabla f_x = fracf_n-f_n-1x_n-x_n-1
      $
      and
      $nabla f_y = fracf_n-f_n-1y_n-y_n-1.
      $



      I can simply define the totalwhole image gradient is the norm of x and y gradient component:



      $||nabla f|| = sqrt(nabla f_x)^2+(nabla f_y)^2.
      $ Nothing fancy so far.



      Now I am just wondering, what is the distribution of the image gradient in equation above? Here is an example:



      enter image description here



      In above image, the histogram of the image gradient really looks exponential to me. This is just an example, but I have seen similar shape of the histogram in many cases.



      Can I claim the distribution of an image gradient follows exponential? If not, with what condition I can/cannot make this guess? Thanks a lot.







      statistics probability-distributions image-processing exponential-distribution






      share|cite|improve this question















      share|cite|improve this question













      share|cite|improve this question




      share|cite|improve this question








      edited Mar 24 at 17:42









      Royi

      3,65512354




      3,65512354










      asked Mar 5 '17 at 21:56









      Nick X TsuiNick X Tsui

      263212




      263212




















          1 Answer
          1






          active

          oldest

          votes


















          0












          $begingroup$

          You can claim it, but it might be better to do a goodness-of-fit test, which tests whether it is ok to assume some data comes from a specific distribution.



          One method is the Kolmogorov-Smirnov test, which compares the empirical distribution of your sample to your candidate distribution (in this case, the exponential one), which you get by fitting the distribution to the data (i.e. parameter estimation). Implementation-wise, here it is in R and here it is in Python.



          By the way, Ruderman's The Statistics of Natural Images does indeed find something somewhat similar to you, concerning the "un-Gaussian" tail of the gradient magnitudes, across natural images :)






          share|cite|improve this answer









          $endgroup$













            Your Answer








            StackExchange.ready(function()
            var channelOptions =
            tags: "".split(" "),
            id: "69"
            ;
            initTagRenderer("".split(" "), "".split(" "), channelOptions);

            StackExchange.using("externalEditor", function()
            // Have to fire editor after snippets, if snippets enabled
            if (StackExchange.settings.snippets.snippetsEnabled)
            StackExchange.using("snippets", function()
            createEditor();
            );

            else
            createEditor();

            );

            function createEditor()
            StackExchange.prepareEditor(
            heartbeatType: 'answer',
            autoActivateHeartbeat: false,
            convertImagesToLinks: true,
            noModals: true,
            showLowRepImageUploadWarning: true,
            reputationToPostImages: 10,
            bindNavPrevention: true,
            postfix: "",
            imageUploader:
            brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
            contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
            allowUrls: true
            ,
            noCode: true, onDemand: true,
            discardSelector: ".discard-answer"
            ,immediatelyShowMarkdownHelp:true
            );



            );













            draft saved

            draft discarded


















            StackExchange.ready(
            function ()
            StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2173531%2fthe-statistic-distribution-of-image-gradient%23new-answer', 'question_page');

            );

            Post as a guest















            Required, but never shown

























            1 Answer
            1






            active

            oldest

            votes








            1 Answer
            1






            active

            oldest

            votes









            active

            oldest

            votes






            active

            oldest

            votes









            0












            $begingroup$

            You can claim it, but it might be better to do a goodness-of-fit test, which tests whether it is ok to assume some data comes from a specific distribution.



            One method is the Kolmogorov-Smirnov test, which compares the empirical distribution of your sample to your candidate distribution (in this case, the exponential one), which you get by fitting the distribution to the data (i.e. parameter estimation). Implementation-wise, here it is in R and here it is in Python.



            By the way, Ruderman's The Statistics of Natural Images does indeed find something somewhat similar to you, concerning the "un-Gaussian" tail of the gradient magnitudes, across natural images :)






            share|cite|improve this answer









            $endgroup$

















              0












              $begingroup$

              You can claim it, but it might be better to do a goodness-of-fit test, which tests whether it is ok to assume some data comes from a specific distribution.



              One method is the Kolmogorov-Smirnov test, which compares the empirical distribution of your sample to your candidate distribution (in this case, the exponential one), which you get by fitting the distribution to the data (i.e. parameter estimation). Implementation-wise, here it is in R and here it is in Python.



              By the way, Ruderman's The Statistics of Natural Images does indeed find something somewhat similar to you, concerning the "un-Gaussian" tail of the gradient magnitudes, across natural images :)






              share|cite|improve this answer









              $endgroup$















                0












                0








                0





                $begingroup$

                You can claim it, but it might be better to do a goodness-of-fit test, which tests whether it is ok to assume some data comes from a specific distribution.



                One method is the Kolmogorov-Smirnov test, which compares the empirical distribution of your sample to your candidate distribution (in this case, the exponential one), which you get by fitting the distribution to the data (i.e. parameter estimation). Implementation-wise, here it is in R and here it is in Python.



                By the way, Ruderman's The Statistics of Natural Images does indeed find something somewhat similar to you, concerning the "un-Gaussian" tail of the gradient magnitudes, across natural images :)






                share|cite|improve this answer









                $endgroup$



                You can claim it, but it might be better to do a goodness-of-fit test, which tests whether it is ok to assume some data comes from a specific distribution.



                One method is the Kolmogorov-Smirnov test, which compares the empirical distribution of your sample to your candidate distribution (in this case, the exponential one), which you get by fitting the distribution to the data (i.e. parameter estimation). Implementation-wise, here it is in R and here it is in Python.



                By the way, Ruderman's The Statistics of Natural Images does indeed find something somewhat similar to you, concerning the "un-Gaussian" tail of the gradient magnitudes, across natural images :)







                share|cite|improve this answer












                share|cite|improve this answer



                share|cite|improve this answer










                answered Mar 5 '17 at 22:17









                user3658307user3658307

                5,0633949




                5,0633949



























                    draft saved

                    draft discarded
















































                    Thanks for contributing an answer to Mathematics Stack Exchange!


                    • Please be sure to answer the question. Provide details and share your research!

                    But avoid


                    • Asking for help, clarification, or responding to other answers.

                    • Making statements based on opinion; back them up with references or personal experience.

                    Use MathJax to format equations. MathJax reference.


                    To learn more, see our tips on writing great answers.




                    draft saved


                    draft discarded














                    StackExchange.ready(
                    function ()
                    StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fmath.stackexchange.com%2fquestions%2f2173531%2fthe-statistic-distribution-of-image-gradient%23new-answer', 'question_page');

                    );

                    Post as a guest















                    Required, but never shown





















































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown

































                    Required, but never shown














                    Required, but never shown












                    Required, but never shown







                    Required, but never shown







                    Popular posts from this blog

                    How should I support this large drywall patch? Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern) Announcing the arrival of Valued Associate #679: Cesar Manara Unicorn Meta Zoo #1: Why another podcast?How do I cover large gaps in drywall?How do I keep drywall around a patch from crumbling?Can I glue a second layer of drywall?How to patch long strip on drywall?Large drywall patch: how to avoid bulging seams?Drywall Mesh Patch vs. Bulge? To remove or not to remove?How to fix this drywall job?Prep drywall before backsplashWhat's the best way to fix this horrible drywall patch job?Drywall patching using 3M Patch Plus Primer

                    random experiment with two different functions on unit interval Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)Random variable and probability space notionsRandom Walk with EdgesFinding functions where the increase over a random interval is Poisson distributedNumber of days until dayCan an observed event in fact be of zero probability?Unit random processmodels of coins and uniform distributionHow to get the number of successes given $n$ trials , probability $P$ and a random variable $X$Absorbing Markov chain in a computer. Is “almost every” turned into always convergence in computer executions?Stopped random walk is not uniformly integrable

                    Lowndes Grove History Architecture References Navigation menu32°48′6″N 79°57′58″W / 32.80167°N 79.96611°W / 32.80167; -79.9661132°48′6″N 79°57′58″W / 32.80167°N 79.96611°W / 32.80167; -79.9661178002500"National Register Information System"Historic houses of South Carolina"Lowndes Grove""+32° 48' 6.00", −79° 57' 58.00""Lowndes Grove, Charleston County (260 St. Margaret St., Charleston)""Lowndes Grove"The Charleston ExpositionIt Happened in South Carolina"Lowndes Grove (House), Saint Margaret Street & Sixth Avenue, Charleston, Charleston County, SC(Photographs)"Plantations of the Carolina Low Countrye