Mailman 3 February 2013 - scikit-image

Re: Get the mask for a contour
by Stéfan van der Walt Feb. 25, 2013

Feb. 25, 2013

Hi Francesc! On Mon, Feb 25, 2013 at 1:57 PM, Francesc Alted <francesc(a)continuum.io> wrote: > This is my first post here :) I'm using scikits image for getting some Welcome :) > features from images. What I'd like is, given a contour as a serie of dots, > how can I mask (to zero) the points outside the contour? There happens to be an implementation of that both in scikit-image and in matplotlib. We use it only internally at the moment, so it is exposed as: from skimage._shared.geometry cimport point_in_polygon and (with a slightly more Pythonic interface) as skimage.morphology._pnpoly If you think it's useful on a more general level, a pull-request to expose it would be very welcome! Best, Stéfan

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Re: Segmenting contours
by zetah ven Feb. 25, 2013

Feb. 25, 2013

Hi Stefan, and thank you for your reply For some reason I did not receive email notification, and came here to post what I found, when I was pleasantly surprised by your reply :) I was looking through OpenCV functions just yesterday, and I didn't notice this Inpaint() magic function, as I was expecting something in other parts of this library, like segmentation, tracking and countouring. Contouring ringed the most, but it was complicated for me at the time to extract something meaningful for my problem which is topological contour tracing (that I colored with above example images, not to scare potential reader ;) I just tried the function, but I guess it works best for photo images, as line contours aren't reconstructed as expected. Anyhow thanks for the tip, it could be handy in future occasions I guess. I was also reading various papers. There seem to be couple of methods for reconstructing dense contours with gaps, which is part of more general problem of curve reconstruction. The most accurate results are reported by using Delaunay triangulation and properties of Voronoi diagram vertices, and then applying filtering to obtain the reconstruction. It's referenced to Amenta paper "The crust and the beta-skeleton: Combinatorial curve reconstruction" and it seems to me that environment like NetworkX could be my first step in trying to visualize and then understand this method, as although I've heard about Voronoi and Delaunay triangulation, I never used those functions. Then perhaps engage scipy and matplotlib for real example. In slow pace that is my plan.

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Re: Segmenting contours
by Stéfan van der Walt Feb. 24, 2013

Feb. 24, 2013

Hi Zetah On Sun, Feb 24, 2013 at 1:10 AM, zetah ven <otrov(a)hush.ai> wrote: > I'm hunting magenta contour, but I need it without discontinuities (gaps), like I get if I threshold just magenta. So I would like to get this: I think image inpainting might solve your problem. Unfortunately, we do not have an implementation in scikit-image, but OpenCV does: http://docs.opencv.org/modules/photo/doc/inpainting.html Let us know how that works. Stéfan

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Re: Contribution: circles and hough transform
by Anders Klint Feb. 18, 2013

Feb. 18, 2013

Hi, Any hopes of this making it into the 0.8 release? Regards, /Anders On 3 feb 2013, at 20:11, Stéfan van der Walt <stefan(a)sun.ac.za> wrote: > Hi François > > Yes, you may rely on Cython, so no need to code a pure Python version. > > Renaming the function as suggested would break backward compatibility, so I suggest adding hough_line as an alias, and deprecating hough over two releases. > > Regards > Stéfan > > On Feb 3, 2013 9:04 PM, "François" <francois.boulogne(a)gmail.com> wrote: > Hi, > > I have written the hough transform for circles in python and have a good basis for peak detection. > > I have few questions: > 1/ in skimage/transform/hough_transform.py > Cython version of hough() is imported like this (below the python version) > 59 # try to import and use the faster Cython version if it exists > 60 try: > 61 from ._hough_transform import _hough > 62 except ImportError: > 63 pass > > Should I do the same for hough_circle()? or just implement the cython version? > I don't see why cython would not be available... > > 2/ For disambiguation, I suggest to rename hough to hough_line(). The same for peak detection. > Any comment on that? > > Cheers, > François. > > -- > You received this message because you are subscribed to the Google Groups "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/groups/opt_out. > > > > -- > You received this message because you are subscribed to the Google Groups "scikit-image" group. > To unsubscribe from this group and stop receiving emails from it, send an email to scikit-image+unsubscribe(a)googlegroups.com. > For more options, visit https://groups.google.com/groups/opt_out. > >

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SciPy 2013 Announcement -- June 24 - 29, 2013, Austin, TX!
by Anthony Scopatz Feb. 15, 2013

Feb. 15, 2013

Hello All, As this years conference communications co-chair, it is my extreme pleasure to announce the *SciPy 2013 conference from June 24th - 29th in sunny Austin, Texas, USA.* Please see our website<http://conference.scipy.org/scipy2013/about.php>(or below) for more details. A call for presentations, tutorials, and papers will be coming out very soon. Please make sure to sign up on our mailing list<http://eepurl.com/dyoeL>, follow us on twitter <https://twitter.com/SciPy2013>, or Google Plus<https://plus.google.com/u/0/100948873231627513165/posts> so that you don't miss out on any important updates. I sincerely hope that you can attend this year and make 2013 the best year for SciPy yet! Happy Hacking! Anthony Scopatz SciPy 2013 Conference Announcement ---------------------------------- SciPy 2013, the twelfth annual Scientific Computing with Python conference, will be held this June 24th-29th in Austin, Texas. SciPy is a community dedicated to the advancement of scientific computing through open source Python software for mathematics, science, and engineering. The annual SciPy Conference allows participants from academic, commercial, and governmental organizations to showcase their latest projects, learn from skilled users and developers, and collaborate on code development. The conference consists of two days of tutorials by followed by two days of presentations, and concludes with two days of developer sprints on projects of interest to the attendees. Specialized Tracks ------------------ This year we are happy to announce two specialized tracks run in parallel to the general conference: *Machine Learning* In recent years, Python's machine learning libraries rapidly matured with a flurry of new libraries and cutting-edge algorithm implement and development occurring within Python. As Python makes these algorithms more accessible, machine learning algorithm application has spread across disciplines. Showcase your favorite machine learning library or how it has been used as an effective tool in your work! *Reproducible Science* Over recent years, the Open Science movement has stoked a renewed acknowledgement of the importance of reproducible research. The goals of this movement include improving the dissemination of progress, prevent fraud through transparency, and enable deeper/wider development and collaboration. This track is to discuss the tools and methods used to achieve reproducible scientific computing. Domain-specific Mini-symposia ----------------------------- Introduced in 2012, mini-symposia are held to discuss scientific computing applied to a specific scientific domain/industry during a half afternoon after the general conference. Their goal is to promote industry specific libraries and tools, and gather people with similar interests for discussions. Mini-symposia on the following topics will take place this year: - Meteorology, climatology, and atmospheric and oceanic science - Astronomy and astrophysics - Medical imaging - Bio-informatics Tutorials --------- Multiple interactive half-day tutorials will be taught by community experts. The tutorials provide conceptual and practical coverage of tools that have broad interest at both an introductory or advanced level. This year, a third track will be added, targeting specifically programmers with no prior knowledge of scientific python. Developer Sprints ----------------- A hackathon environment is setup for attendees to work on the core SciPy packages or their own personal projects. The conference is an opportunity for developers that are usually physically separated to come together and engage in highly productive sessions. It is also an occasion for new community members to introduce themselves and recieve tips from community experts. This year, some of the sprints will be scheduled and announced ahead of the conference. Birds-of-a-Feather (BOF) Sessions --------------------------------- Birds-of-a-Feather sessions are self-organized discussions that run parallel to the main conference. The BOFs sessions cover primary, tangential, or unrelated topics in an interactive, discussion setting. This year, some of the BOF sessions will be scheduled and announced ahead of the conference. Important Dates --------------- - March 18th: Presentation abstracts, poster, tutorial submission deadline. Application for sponsorship deadline. - April 15th: Speakers selected - April 22nd: Sponsorship acceptance deadline - May 1st: Speaker schedule announced - May 5th: Paper submission deadline - May 6th: Early-bird registration ends - June 24th-29th: 2 days of tutorials, 2 days of conference, 2 days of sprints We look forward to a very exciting conference and hope to see you all at the conference. The SciPy2013 organization team: * Andy Terrel, Co-chair * Jonathan Rocher, Co-chair * Katy Huff, Program Committee co-chair * Matt McCormick, Program Committee co-chair * Dharhas Pothina, Tutorial co-chair * Francesc Alted, Tutorial co-chair * Corran Webster, Sprint co-chair * Peter Wang, Sprint co-chair * Matthew Turk, BoF co-chair * Jarrod Millman, Proceeding co-chair * Stéfan van der Walt, Proceeding co-chair * Anthony Scopatz, Communications co-chair * Majken Tranby, Communications co-chair * Jeff Daily, Financial Aid co-chair * John Wiggins, Financial Aid co-chair * Leah Jones, Operations chair * Brett Murphy, Sponsor chair * Bill Cowan, Financial chair

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Google Summer of Code 2013 Announced
by Vikram Kamath Feb. 14, 2013

Feb. 14, 2013

Maybe skimage might want to consider applying for it? http://www.google-melange.com/gsoc/homepage/google/gsoc2013

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Re: Image registration
by Stéfan van der Walt Feb. 13, 2013

Feb. 13, 2013

Hi Jeff On Wed, Feb 13, 2013 at 5:17 PM, jeff witz <witzjean(a)gmail.com> wrote: > If someone is interested, I can share the code. For now it is > absolutely not ready to be include as a part of library it is just a > simple example that shows how to register to picture using a given > description of the deformation field with the SSD metric. Yes, we're definitely interested! I've also CC'd Nathan Faggian, author of pyimreg, who is interested in reworking his code to be included. Perhaps you can get in touch. I've also got a lot of code for registration in http://github.com/stefanv/supreme (feature based, dense with SSD or mutual information), but also not quite ready for the limelight. It'd be great if we could pull all of these sources together and provide a compelling solution. Registration is such a common problem, but while it is considered "solved", I think practically it really isn't. Regards Stéfan

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Image registration
by jeff witz Feb. 13, 2013

Feb. 13, 2013

Hi all, I have made a little example of Image Registration, in order to identify lens aberration from a analytical target. I use the classical barrel, excentricity, prismatic shape functions. It is based upon the Sum of Square Differences (SSD) metric so it can be directly seen as Optical flow methods. If someone is interested, I can share the code. For now it is absolutely not ready to be include as a part of library it is just a simple example that shows how to register to picture using a given description of the deformation field with the SSD metric. Best regards

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Re: Citing scikit-image
by Stuart Mumford Feb. 13, 2013

Feb. 13, 2013

No problem, I shall let you know! Thanks Stuart On Wednesday, 6 February 2013 09:53:59 UTC, Stefan van der Walt wrote: > > Dear Stuart > > On Tue, Feb 5, 2013 at 3:55 PM, Stuart Mumford <little...(a)gmail.com<javascript:>> > wrote: > > Do you have a preferred citation for scikit-image? It has been very > handy > > for me!! > > That's wonderful to hear! Unfortunately, we have no publications as > of yet (although, you would receive the much adoration from the > developers should you decide to write one :-). For now, if you could > simply cite the website, and please let us know once it's been > published (perhaps we can put a list of skimage publications on the > website?). > > Stéfan >

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Re: removing small connected components
by Stéfan van der Walt Feb. 11, 2013

Feb. 11, 2013

Hi Juan On Thu, Jan 31, 2013 at 5:19 AM, Juan Nunez-Iglesias <jni.soma(a)gmail.com> wrote: > For my own segmentation package, I wrote a small function to remove small > connected components in an ndarray. You can see it here: This would be very useful--I like the implementation. Thanks! Stéfan

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