Search Results for author:
Found 12 papers, 2 papers with code
Hyperspectral Image Compression Using Sampling and Implicit Neural Representations
Hyperspectral images, which record the electromagnetic spectrum for a pixel in the image of a scene, often store hundreds of channels per pixel and contain an order of magnitude more information than a similarly-sized RBG color image.
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing
The Hyperspectral Unxming problem is to find the pure spectral signal of the underlying materials (endmembers) and their proportions (abundances).
Error Estimation for Single-Image Human Body Mesh Reconstruction
Human pose and shape estimation methods continue to suffer in situations where one or more parts of the body are occluded.
Hyperspectral Image Compression Using Implicit Neural Representation
Hyperspectral images, which record the electromagnetic spectrum for a pixel in the image of a scene, often store hundreds of channels per pixel and contain an order of magnitude more information than a typical similarly-sized color image.
Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images
The results demonstrate the potential of our method to find objects in a Hyperspectral Image while bypassing the burden of manual search of the optimal parameters.
Hyperspectral Pixel Unmixing with Latent Dirichlet Variational Autoencoder
We present a method for hyperspectral pixel {\it unmixing}.
The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study
We use five dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional hyperspectral pixels.
Real-time Video Summarization on Commodity Hardware
Our method on average is able to generate a video summary in time that is shorter than the duration of the video.
Joint Spatial and Layer Attention for Convolutional Networks
In this paper, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i. e., ``what'' feature abstraction to attend to) and different spatial locations of the selected feature map (i. e., ``where'') to perform the task at hand.
EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning
The edge generator hallucinates edges of the missing region (both regular and irregular) of the image, and the image completion network fills in the missing regions using hallucinated edges as a priori.
Ranked #10 on
Image Inpainting
on Places2
Compact Neural Network Solutions to Laplace's Equation in a Nanofluidic Device
We explore the use of neural networks to solve the Laplace equation in a two-dimensional geometry.
A Framework for Video-Driven Crowd Synthesis
We present a framework for video-driven crowd synthesis.
