Image-based virtual try-on techniques have shown great promise for enhancing the user-experience and improving customer satisfaction on fashion-oriented e-commerce platforms. However, existing ...

Deep learning shows promising results in extracting useful information from medical images. The proposed work applies a Convolutional Neural Network (CNN) on retinal images to extract features that ...

NEW YORK - The nonprofit Art on the Ave is aiming to take Manhattan's Upper East Side beyond just its buildings by showcasing a gallery walk. For a limited time, residents can learn more about the ...

In satellite remote sensing applications, waterbody segmentation plays an essential role in mapping and monitoring the dynamics of surface water. Satellite image segmentation-examining a relevant ...

A major challenge in the analysis of tissue imaging data is cell segmentation, the task of identifying the precise boundary of every cell in an image. To address this problem we constructed TissueNet, ...

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating ...

We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for ...

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of ...