arXiv subjects

Brandon Weissbourd

Publications and source records attributed to Brandon Weissbourd.

4 recordsLinked to original sources

arXiv2026

Motion-guided sparse correction enables expert-quality point tracking across diverse microscopy regimes

Tracking the dynamics of non-canonical biological systems in microscopy videos remains a persistent challenge. Both classical and learning-based trackers depend on expert-reviewed data to be evaluated and adapted, yet exhaustive manual annotation rarely scales to the videos where these tools are needed most. We developed RIPPLE (Refinement Interpolation Platform for Point Location Estimation), which recasts annotation as sparse correction: a user clicks a starting point, RIPPLE proposes a full trajectory, and the user intervenes only where the trajectory drifts. We tested RIPPLE on five challenging microscopy datasets from our laboratories, four from the transparent jellyfish Clytia hemisphaerica and one tracking landmarks on rapidly moving sperm. Across these, RIPPLE matched the quality of exhaustive manual annotation while reducing manual clicks by 3 to 25 times across datasets. RIPPLE thereby fills a missing layer between manual annotation and fully automated tracking, enabling immediate quantification of biological dynamics, method benchmarking, and the production of the gold-standard data needed to adapt future automated microscopy trackers.

cs.CV↗

arXiv2026

Tunable supercontinuum in multimode fiber via bending-induced dispersion modification

Nonlinear pulse propagation in multimode fibers (MMFs) offers a compact, low-cost route to broadband, tunable femtosecond light, but most control schemes act by changing the spatial mode composition, typically resulting in irregular or speckled beams in exchange for maximal spectral tunability. Here we introduce a complementary mechanism: bending-induced local dispersion modification of a high-order mode (HOM) to steer the spectrum while keeping the spatial mode fixed. We launch an LP0,7 mode into a step-index MMF and apply programmable macrobends near the input. With a standard Yb pump at 1030 nm, this yields spatially clean, continuous spectral tuning across 700-1350 nm, while the output profile remains Bessel-like and robust to reconfiguration of controlled bends. A perturbative model explains the observed spatial-spectral decorrelation, showing that moderate curvature produces first- and second-order shifts in group delay and group-velocity dispersion of the HOM with minimal change in its modal composition; these dispersion shifts control soliton fission, dispersive-wave emission, and the soliton self-frequency shift. We further validate application utility by driving multicolor, extended-depth-of-focus multiphoton microscopy directly from this all-fiber source. To our knowledge, this is the first demonstration of bending-induced dispersion modification, rather than mode mixing, used to tune MMF supercontinuum spectra without sacrificing beam quality, laying the foundation for an alternative pathway to tunable femtosecond illumination for imaging and spectroscopy.

physics.optics↗

arXiv2025

3D space-variant modal deconvolution with computed point spread functions

Deconvolution is the most widely used aberration correction technique in microscopy, however most techniques assume that the aberrations are the same for each point in the image, which is rarely true. Methods for tracking spatially varying aberrations require burdensome calibration or computation, or require symmetries in the aberration patterns. Here, we expand on existing modal deconvolution methods to demonstrate 3D fluorescence deconvolution in imaging systems that exhibit no simple symmetry. Our method is based on a space-variant generalization of Richardson-Lucy deconvolution that makes use of ZEMAX\textsuperscript{\textregistered}-derived point spread functions without the requirement of guide stars or calibration measurements. We validate the performance of our method by applying it to snapshot multiplane imaging of both bead samples and biological specimens, and show that modal decomposition is a practical solution for deconvolving spatially varying aberrations that do not display clear symmetries.

physics.optics↗

arXiv2021

Self-Supervised Keypoint Discovery in Behavioral Videos

We propose a method for learning the posture and structure of agents from unlabelled behavioral videos. Starting from the observation that behaving agents are generally the main sources of movement in behavioral videos, our method, Behavioral Keypoint Discovery (B-KinD), uses an encoder-decoder architecture with a geometric bottleneck to reconstruct the spatiotemporal difference between video frames. By focusing only on regions of movement, our approach works directly on input videos without requiring manual annotations. Experiments on a variety of agent types (mouse, fly, human, jellyfish, and trees) demonstrate the generality of our approach and reveal that our discovered keypoints represent semantically meaningful body parts, which achieve state-of-the-art performance on keypoint regression among self-supervised methods. Additionally, B-KinD achieve comparable performance to supervised keypoints on downstream tasks, such as behavior classification, suggesting that our method can dramatically reduce model training costs vis-a-vis supervised methods.

cs.CV↗