Second, we employ a gradual refinement scheme in which we start from a simple CRF model to generate a result which is more robust to noise but less accurate, and then we gradually increase the model's complexity to improve the result.
To address the problem, we introduce a network joining day/night translation and stereo.
Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation on video sequences would lead to difficulty.
The sparse component is directly used for the detection, that is, the targets are simply detected at the non-zero entries of the sparse target HSI.
We demonstrate that the dense depth maps recovered from the relative pose of the RS camera can be used in a RS-aware warping for image rectification to recover high-quality Global Shutter (GS) images.
Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the highly nonlinear angular error objective.
We present a joint Structure-Stereo optimization model that is robust for disparity estimation under low-light conditions.
Optical flow estimation in rainy scenes is challenging due to degradation caused by rain streaks and rain accumulation, where the latter refers to the poor visibility of remote scenes due to intense rainfall.
In this paper, we propose a method for separating known targets of interests from the background in hyperspectral imagery.
Image and Video Processing Signal Processing
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty.
Ranked #1 on Motion Segmentation on KT3DMoSeg
Given a single input rainy image, our goal is to visually remove rain streaks and the veiling effect caused by scattering and transmission of rain streaks and rain droplets.
Given a target prior information, our goal is to propose a method for automatically separating targets of interests from the background in hyperspectral imagery.
This paper proposes a two-stream flow-guided convolutional attention networks for action recognition in videos.
The choice of motion models is vital in applications like image/video stitching and video stabilization.
To handle rain accumulation, our method decomposes the image into a piecewise-smooth background layer and a high-frequency detail layer.
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence.
Low-rank matrix completion is a problem of immense practical importance.