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Multi-view stereo: A tutorial
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 …
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
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 …
computer vision, and machine learning. For a long time, it has been recognized that looking …
[KNJIGA][B] Mathematics for machine learning
The fundamental mathematical tools needed to understand machine learning include linear
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability …
Deeppruner: Learning efficient stereo matching via differentiable patchmatch
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 …
to enable real-time inference. Towards this goal, we developed a differentiable PatchMatch …
Neural markov random field for stereo matching
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) …
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 …
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 …
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
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 …
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
This work presents a robust graph map** approach for the unsupervised heterogeneous
change detection problem in remote sensing imagery. To address the challenge that …
change detection problem in remote sensing imagery. To address the challenge that …
Fast optical flow using dense inverse search
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 …
complexity. However, in real-life visual applications, such as tracking, activity detection and …
Optical flow modeling and computation: A survey
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 …
computer vision. In 35 years, many methodological concepts have been introduced and …