Results 71 to 80 of about 2,159,265 (282)
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Estimation of motion blur kernel parameters using regression convolutional neural networks
Many deblurring and blur kernel estimation methods use a maximum a posteriori (MAP) approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel.
Boucheron, Laura E. +4 more
core +1 more source
Blur processing using double discrete wavelet transform
, 2013 We propose a notion of double discrete wavelet trans-form (DDWT) that is designed to sparsify the blurred im-age and the blur kernel simultaneously. DDWT greatly en-hances our ability to analyze, detect, and process blur ker-nels and blurry images—the ...
Yi Zhang, Keigo Hirakawa
core +1 more source
Advanced Intelligent Discovery, EarlyView.AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia +2 more
wiley +1 more source
Recovering the Subpixel PSF from Two Photographs at Different Distances
Image Processing On Line, 2013 In most typical digital cameras, even high-end digital single lens reflex ones (DSLR), the acquired images are sampled at rates below the Nyquist critical rate, causing aliasing effects.
Mauricio Delbracio +2 more
doaj +1 more source
A Generative AI Framework to Predict Cardiomyocyte Contraction Function From Single Static Images
Advanced Intelligent Discovery, EarlyView.A single static hiPSC‐cardiomyocyte image is fed into a U‐Net‐GAN framework, which directly predicts a pixel‐resolved contraction heatmap without time‐lapse imaging. StyleGAN2‐generated synthetic cell–heatmap pairs augment training, improving prediction fidelity (SSIM = 0.84).
Andrew Kowalczewski +5 more
wiley +1 more source
Singular wavelets on a finite interval
Informatika, 2018 Nonparametric methods are used in complex cases where model information is insufficient. A new method of nonparametric approximation, the singular wavelet method, is developed.
V. M. Romanchak
doaj
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R [PDF]
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing.
Tarn Duong
core