Multi-view stereo: A tutorial

Y Furukawa, C Hernández - Foundations and trends® in …, 2015 - nowpublishers.com
This tutorial presents a hands-on view of the field of multi-view stereo with a focus on
practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D …

Playing with duality: An overview of recent primal? dual approaches for solving large-scale optimization problems

N Komodakis, JC Pesquet - IEEE Signal Processing Magazine, 2015 - ieeexplore.ieee.org
Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

[KNJIGA][B] Mathematics for machine learning

MP Deisenroth, AA Faisal, CS Ong - 2020 - books.google.com
The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …

Deeppruner: Learning efficient stereo matching via differentiable patchmatch

S Duggal, S Wang, WC Ma, R Hu… - Proceedings of the …, 2019 - openaccess.thecvf.com
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms
to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch …

Neural markov random field for stereo matching

T Guan, C Wang, YH Liu - … of the IEEE/CVF Conference on …, 2024 - openaccess.thecvf.com
Stereo matching is a core task for many computer vision and robotics applications. Despite
their dominance in traditional stereo methods the hand-crafted Markov Random Field (MRF) …

New frontiers in spectral-spatial hyperspectral image classification: The latest advances based on mathematical morphology, Markov random fields, segmentation …

P Ghamisi, E Maggiori, S Li, R Souza… - … and remote sensing …, 2018 - ieeexplore.ieee.org
In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …

Learning a convolutional neural network for non-uniform motion blur removal

J Sun, W Cao, Z Xu, J Ponce - Proceedings of the IEEE …, 2015 - openaccess.thecvf.com
In this paper, we address the problem of estimating and removing non-uniform motion blur
from a single blurry image. We propose a deep learning approach to predicting the …

Iterative robust graph for unsupervised change detection of heterogeneous remote sensing images

Y Sun, L Lei, D Guan, G Kuang - IEEE Transactions on Image …, 2021 - ieeexplore.ieee.org
This work presents a robust graph map** approach for the unsupervised heterogeneous
change detection problem in remote sensing imagery. To address the challenge that …

Fast optical flow using dense inverse search

T Kroeger, R Timofte, D Dai, L Van Gool - Computer Vision–ECCV 2016 …, 2016 - Springer
Most recent works in optical flow extraction focus on the accuracy and neglect the time
complexity. However, in real-life visual applications, such as tracking, activity detection and …

Optical flow modeling and computation: A survey

D Fortun, P Bouthemy, C Kervrann - Computer Vision and Image …, 2015 - Elsevier
Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …