1,146 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks
In the last decade, supervised deep learning approaches have been extensively
employed in visual odometry (VO) applications, which is not feasible in
environments where labelled data is not abundant. On the other hand,
unsupervised deep learning approaches for localization and mapping in unknown
environments from unlabelled data have received comparatively less attention in
VO research. In this study, we propose a generative unsupervised learning
framework that predicts 6-DoF pose camera motion and monocular depth map of the
scene from unlabelled RGB image sequences, using deep convolutional Generative
Adversarial Networks (GANs). We create a supervisory signal by warping view
sequences and assigning the re-projection minimization to the objective loss
function that is adopted in multi-view pose estimation and single-view depth
generation network. Detailed quantitative and qualitative evaluations of the
proposed framework on the KITTI and Cityscapes datasets show that the proposed
method outperforms both existing traditional and unsupervised deep VO methods
providing better results for both pose estimation and depth recovery.Comment: ICRA 2019 - accepte
3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset
3D dense reconstruction refers to the process of obtaining the complete shape
and texture features of 3D objects from 2D planar images. 3D reconstruction is
an important and extensively studied problem, but it is far from being solved.
This work systematically introduces classical methods of 3D dense
reconstruction based on geometric and optical models, as well as methods based
on deep learning. It also introduces datasets for deep learning and the
performance and advantages and disadvantages demonstrated by deep learning
methods on these datasets.Comment: 16 page
End-to-End, Sequence-to-Sequence Probabilistic Visual Odometry through Deep Neural Networks
This paper studies visual odometry (VO) from the perspective of deep learning. After tremendous efforts in the robotics and computer vision communities over the past few decades, state-of-the-art VO algorithms have demonstrated incredible performance. However, since the VO problem is typically formulated as a pure geometric problem, one of the key features still missing from current VO systems is the capability to automatically gain knowledge and improve performance through learning. In this paper, we investigate whether deep neural networks can be effective and beneficial to the VO problem. An end-to-end, sequence-to-sequence probabilistic visual odometry (ESP-VO) framework is proposed for the monocular VO based on deep recurrent convolutional neural networks. It is trained and deployed in an end-to-end manner, that is, directly inferring poses and uncertainties from a sequence of raw images (video) without adopting any modules from the conventional VO pipeline. It can not only automatically learn effective feature representation encapsulating geometric information through convolutional neural networks, but also implicitly model sequential dynamics and relation for VO using deep recurrent neural networks. Uncertainty is also derived along with the VO estimation without introducing much extra computation. Extensive experiments on several datasets representing driving, flying and walking scenarios show competitive performance of the proposed ESP-VO to the state-of-the-art methods, demonstrating a promising potential of the deep learning technique for VO and verifying that it can be a viable complement to current VO systems. </jats:p
On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise
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