Lnk.BioYour 3D models deserve more than screenshots 👀
Now you can embed interactive Speckle models right into your LnkBio page.
🎯 Zoom, rotate, explore - all in your linkinbio
Check it out → https://lnk.bio/linkin/embed-your-speckle-3d-models-on-lnkbio
Python Job SupportProject management web app 3D data base
Project management app that allows real-time 3D data base connection to Speckle and firebase #speckle #javascript #firebase ... source
https://quadexcel.com/wp/project-management-web-app-3d-data-base/
JMArts DesignerPrimeiro post a compartilhar renderização de IA e competir por um ano de renderizações gratuitas + brindes Speckle. #Renderização #GendoAI #Speckle
prompt:
Snow, Christmas scene, modern exterior school, hyper realistic, soft glow lights from windows, interior christmas tree with lights
Dion MoultIn Europe and interested in #FreeSoftware, #OpenSource, or #OpenBIM? In this year's #BILTEUR24 there will be a class from #ThatOpenCompany, 3 classes from #Speckle, and 1 double lab about the #BlenderBIM Add-on! https://bilteur2024.dekon.com.tr/schedule/
Low Rank JackSlides and abstract of my talk "Interferometric single-pixel imaging with a multicore fiber" made at ISCS23 (Luxembourg, June 12-14, 2023) now available at https://laurentjacques.gitlab.io/event/interferometric-single-pixel-imaging-with-a-multicore-fiber/ and https://laurentjacques.gitlab.io/publication/interferometric-single-pixel-imaging-with-a-multicore-fiber/ All this is based on the work of Olivier Leblanc. Long journal preprint version "Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations" on https://arxiv.org /abs/2306.12698 in colaboration with S. Sivankutty, M. Hofer, H. Rigneault, #imaging #lensless #compressivesensing #computationalimaging #speckle #compressedsensing #InverseProblem (toot generated thanks to https://tootpick.org directly from my website)
Interferometric single-pixel imaging with a multicore fiber | Laurent Jacques
Abstract: Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of a Hermitian interferometric matrix – a matrix encoding the spectral content of the sample image.
Very happy of this 3-year work on "Interferometric #lensless #imaging: rank-one projections of image frequencies with #speckle illuminations" (https://arxiv.org/abs/2306.12698) carried out by Olivier Leblanc (ICTEAM, UCLouvain, Belgium), in collaboration with Mathias Hofer (Inst. Fresnel, France), Siddharth Sivankutty (U. Lille, France), and Hervé Rigneault (Inst. Fresnel, France).
In this work, we study how lensless imaging with a multicore fiber amounts to probing the spatial frequency content of an image (e.g., a biological sample for potential endoscopic applications).
In particular, by randomly programming the phase of the light emitted by each core (thanks to a spatial light modulator), the sensing model amounts to combining frequencies of the sample image, and this combination is explained by a rank-one projection model of a certain interferometric matrix.
Knowing this model improves our understanding of the model and eases the calibration compared to other speckle imaging approaches.
Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations
Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an interferometric matrix--a matrix encoding the spectral content of the sample image. In this model, each SROP is induced by the complex sketching vector shaping the incident light wavefront with a spatial light modulator (SLM), while the projected interferometric matrix collects up to $O(Q^2)$ image frequencies for a $Q$-core MCF. While this scheme subsumes previous sensing modalities, such as raster scanning (RS) imaging with beamformed illumination, we demonstrate that collecting the measurements of $M$ random SLM configurations--and thus acquiring $M$ SROPs--allows us to estimate an image of interest if $M$ and $Q$ scale log-linearly with the image sparsity level This demonstration is achieved both theoretically, with a specific restricted isometry analysis of the sensing scheme, and with extensive Monte Carlo experiments. On a practical side, we perform a single calibration of the sensing system robust to certain deviations to the theoretical model and independent of the sketching vectors used during the imaging phase. Experimental results made on an actual MCF system demonstrate the effectiveness of this imaging procedure on a benchmark image.
Lauren Juice 🧃Some cross stitch I've done in the past. Haven't picked it up in a while because the last piece I did was symmetrical (The VVitch one) and my OCD made it Very Not Fun. #crossstitch #hobby #crossstitching #thewitch #thevvitch #snowwhite #speckle #tucaandbertie #boldandbrash #spongebob
OSArch - Open Source in AEC#Speckle have launched a new tutorials collection giving you options for getting from closed #BIM to #OpenBIM. Go check it out! https://speckle.systems/tutorials/
Our friends over at #speckle have a brand new viewer rewritten with the 2.9 release. With a brand new viewer, comes a brand new feature: Shadows☂. Go to http://speckle.xyz and try it yourself.