Hi everyone,
Alexandre di Siqueira (one of our core devs) recently started a postdoc
position at the Berkeley Institute for Data Science with me. Alex will
be doing material science research, and will improve scikit-image to
meet the demands of related analysis. Our initial focus will be on
(fairly large) 3-dimensional volume segmentation.
Expect to see a lot more activity from Alex in the coming months, and
let us know if there are any specific N-dimensional issues you'd like us
to pay attention to.
Best regards,
Stéfan
Hello everyone, recently (and for the first time) i had to deal with image processing,
my problem is detecting sferical blobs in a 3D grayscale image shaped like this (20,1000,1000).
So i looked inside scikit, and i found the function blob_dog&log&doh (the three main algorithms),
the procedure is working perfectly for the X and Y coordinates (x,y in [0,1000]x[0,1000])
but failing miserably at identifying the correct Z coord (Z in [0,20]).
I think this has to do with:
the blobs occupy rougly 30x30 pixels in X,Y, but only 3/4 pixels in Z (since my 3D images have to be taken with a lower resolution in Z).
Moreover the algorithms apparently use isotropic gaussian filtering so the variance is the same in all directions (at a given sigma scale) according to the algorithm (Unfortunately not according to my data).
Is there any reasonable/sane way to fix for this? What do you guys suggest?
this question has two direction to be solved, (vector and raster)
1. the vector method is based on polygon, then do buffer, shrink, local curve feature detect and so on. (shapely and cgal is the libraries in this field)
2. raster method is morph algorithm or local feature descriptor. here I have a idea to use dog filter enhance the convex and concave.
----- 原始邮件 -----
发件人：Stefanie_Lück <luecks(a)gmail.com>
收件人："Mailing list for scikit-image (http://scikit-image.org)" <scikit-image(a)python.org>
主题：[scikit-image] Re: Outer points detection
日期：2019年05月09日 02点06分
Hello,
very interesting ideas! Thanks a lot for your replies. I am going to try and compare your suggestions.
For the missing data, I could contribute many types of images, microscopic and macroscopic. I am also planning several publications which include also image datasets. I would be happy to help.
Cheers Stefanie
Am Mi., 8. Mai 2019, 14:55 hat Zach Pincus <zpincus(a)gmail.com> geschrieben:
Just to add another suggestion:
My approach would be to use the contour finding code in skimage to convert the binary boundary into a polygon (https://scikit-image.org/docs/dev/auto_examples/edges/plot_contours.html). After that, I would use the scipy tools to fit a parametric spline to the outline, applying a spatially appropriate bit of smoothing so the outline isn’t as jagged. (Or even better, if you have the original image, find the outline at the same threshold value you would have used to binaries the image and it will start out much less jagged.)
Once you have a reasonably smoothed spline, you can calculate its first and second derivatives, and from there the signed plane curvature values at each point. (https://en.m.wikipedia.org/wiki/Curvature) You will be looking for regions of high curvature — and because it is signed you can distinguish inward from outward bends.
Alternately, if you represent the binary boundary as the zero level-set of a signed distance image, there are ways to calculate the curvature of that image (as a function of x and y) that could I think give you the same information without needing explicit boundary geometry.
Overall though for stuff like this (computations at boundaries) I like having explicit geometry represented as a smooth, differentiable parametric spline. Much simpler than trying to get pixels to do what you want.
Zach
On May 8, 2019, at 3:37 AM, Stefanie Lück <luecks(a)gmail.com> wrote:
Hi everybody,
I am working on a computer vision problem but I got stuck. I am trying to detect outer points of an binary image (see attached images, points which i need are labeled in red on image points.png).
I have tried different approaches (edges, maximas, corner detection) but usually the inner corners will be detected because the outer points I need are rather round.
Any idea of an algorithm which might be useful for this problem?
Thanks a lot in advance,Stefanie
<points.png><test.png>_______________________________________________
scikit-image mailing list -- scikit-image(a)python.org
To unsubscribe send an email to scikit-image-leave(a)python.org
_______________________________________________
scikit-image mailing list -- scikit-image(a)python.org
To unsubscribe send an email to scikit-image-leave(a)python.org
_______________________________________________
scikit-image mailing list -- scikit-image(a)python.org
To unsubscribe send an email to scikit-image-leave(a)python.org
Hi everybody,
I am working on a computer vision problem but I got stuck. I am trying to
detect outer points of an binary image (see attached images, points which i
need are labeled in red on image points.png).
I have tried different approaches (edges, maximas, corner detection) but
usually the inner corners will be detected because the outer points I need
are rather round.
Any idea of an algorithm which might be useful for this problem?
Thanks a lot in advance,
Stefanie
