Mixing
Detecting Gaussians in an image
I have a greyscale image, represented by a histogram below (x and y axes are pixels, z axis is pixel intensity).
Each cluster of bars represents an object, with the local maxima fairly approximating the centroid of the object. My goal is to find the Full Width Half Max of each object – so I'm roughly approximating each object as a Gaussian distribution.
How can I detect each cluster individually? I understand how to mathematically calculate the FWHM, but I'm not sure how to detect each cluster based on its (roughly) Gaussian features. (e.g., in the example below I would want to detect 6 clusters. One can see a small cluster in the middle but its amplitude is so small that I am okay with missing it).
I appreciate any advice - and efficiency is not a major issue, so I can implement relatively expensive solutions. 
matlab image-processing object-detection gaussian
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
add a comment |
I have a greyscale image, represented by a histogram below (x and y axes are pixels, z axis is pixel intensity).
Each cluster of bars represents an object, with the local maxima fairly approximating the centroid of the object. My goal is to find the Full Width Half Max of each object – so I'm roughly approximating each object as a Gaussian distribution.
How can I detect each cluster individually? I understand how to mathematically calculate the FWHM, but I'm not sure how to detect each cluster based on its (roughly) Gaussian features. (e.g., in the example below I would want to detect 6 clusters. One can see a small cluster in the middle but its amplitude is so small that I am okay with missing it).
I appreciate any advice - and efficiency is not a major issue, so I can implement relatively expensive solutions.
matlab image-processing object-detection gaussian
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
add a comment |
I have a greyscale image, represented by a histogram below (x and y axes are pixels, z axis is pixel intensity).
Each cluster of bars represents an object, with the local maxima fairly approximating the centroid of the object. My goal is to find the Full Width Half Max of each object – so I'm roughly approximating each object as a Gaussian distribution.
How can I detect each cluster individually? I understand how to mathematically calculate the FWHM, but I'm not sure how to detect each cluster based on its (roughly) Gaussian features. (e.g., in the example below I would want to detect 6 clusters. One can see a small cluster in the middle but its amplitude is so small that I am okay with missing it).
I appreciate any advice - and efficiency is not a major issue, so I can implement relatively expensive solutions.
matlab image-processing object-detection gaussian
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
I have a greyscale image, represented by a histogram below (x and y axes are pixels, z axis is pixel intensity).
Each cluster of bars represents an object, with the local maxima fairly approximating the centroid of the object. My goal is to find the Full Width Half Max of each object – so I'm roughly approximating each object as a Gaussian distribution.
How can I detect each cluster individually? I understand how to mathematically calculate the FWHM, but I'm not sure how to detect each cluster based on its (roughly) Gaussian features. (e.g., in the example below I would want to detect 6 clusters. One can see a small cluster in the middle but its amplitude is so small that I am okay with missing it).
I appreciate any advice - and efficiency is not a major issue, so I can implement relatively expensive solutions.
matlab image-processing object-detection gaussian
matlab image-processing object-detection gaussian
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
edited Nov 20 '18 at 5:03
Cris Luengo
19.4k51947
19.4k51947
asked Nov 20 '18 at 3:13
oofinoofin
588
asked Nov 20 '18 at 3:13
oofinoofin
588
asked Nov 20 '18 at 3:13
oofinoofin
588
588
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
add a comment |
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
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
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53385677%2fdetecting-gaussians-in-an-image%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
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
add a comment |
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
add a comment |
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
To find the centers of each of these groupings you could use a type of A* search algorithm, or similar linear optimization algorithm.
It will find its way to the maxima of a grouping. The issue after that is you wont know if you are at a local maxima (which in your scenario is likely). After your current search has bottomed out at the highest point, and you have calculated the FWHM for that area, you could set all the nodes your A* has traversed to 0, (or mark each node as visited so as to not be visited again), and start the A* algorithm again, until all nodes have been seen, and all groupings found.
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
answered Nov 20 '18 at 3:27
DMarczakDMarczak
1119
1119
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
add a comment |
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
I like this answer, and if I have time soon I'll try it out. Unfortunately I don't have enough background to implement this strategy quickly enough! Great idea though
– oofin
Nov 20 '18 at 3:30
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
A* might be a bit of overkill, something more naive will probably converge fast enough unless your data is very large/complicated. Good luck.
– DMarczak
Nov 20 '18 at 3:34
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
If you like this answer you can click the green check mark to choose it as the selected answer.
– DMarczak
Nov 20 '18 at 6:00
add a comment |
Thanks for contributing an answer to Stack Overflow!
- 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.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53385677%2fdetecting-gaussians-in-an-image%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
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
