3,198 research outputs found
Road Network Reconstruction from Satellite Images with Machine Learning Supported by Topological Methods
Automatic Extraction of road network from satellite images is a goal that can
benefit and even enable new technologies. Methods that combine machine learning
(ML) and computer vision have been proposed in recent years which make the task
semi-automatic by requiring the user to provide curated training samples. The
process can be fully automatized if training samples can be produced
algorithmically. Of course, this requires a robust algorithm that can
reconstruct the road networks from satellite images reliably so that the output
can be fed as training samples. In this work, we develop such a technique by
infusing a persistence-guided discrete Morse based graph reconstruction
algorithm into ML framework.
We elucidate our contributions in two phases. First, in a semi-automatic
framework, we combine a discrete-Morse based graph reconstruction algorithm
with an existing CNN framework to segment input satellite images. We show that
this leads to reconstructions with better connectivity and less noise. Next, in
a fully automatic framework, we leverage the power of the discrete-Morse based
graph reconstruction algorithm to train a CNN from a collection of images
without labelled data and use the same algorithm to produce the final output
from the segmented images created by the trained CNN. We apply the
discrete-Morse based graph reconstruction algorithm iteratively to improve the
accuracy of the CNN. We show promising experimental results of this new
framework on datasets from SpaceNet Challenge.Comment: 26 pages, 13 figures, ACM SIGSPATIAL 201
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images
Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automated delineation approaches. In the recent years Computer Vision witnessed a paradigm shift from mathematical modelling to data-driven methods based on Machine Learning. This led to improvements in performance and robustness of the detection algorithms. Nonetheless, most Machine Learning methods are general-purpose and they do not exploit the specificity of the delineation problem. In this thesis, we present learning methods suited for this task and we apply them to various kinds of microscopic and natural images, proving the general applicability of the presented solutions.
First, we introduce a topology loss - a new training loss term, which captures higher-level features of curvilinear networks such as smoothness, connectivity and continuity. This is in contrast to most Deep Learning segmentation methods that do not take into account the geometry of the resulting prediction. In order to compute the new loss term, we extract topology features of prediction and ground-truth using a pre-trained network, whose filters are activated by structures at different scales and orientations. We show that this approach yields better results in terms of conventional segmentation metrics and overall topology of the resulting delineation.
Although segmentation of curvilinear structures provides useful information, it is not always sufficient. In many cases, such as neuroscience and cartography, it is crucial to estimate the network connectivity. In order to find the graph representation of the structure depicted in the image, we propose an approach for joint segmentation and connection classification. Apart from pixel probabilities, this approach also returns the likelihood of a proposed path being a part of the reconstructed network. We show that segmentation and path classification are closely related tasks and can benefit from the synergy.
The aforementioned methods rely on Machine Learning, which requires significant amounts of annotated ground-truth data to train models. The labelling process often requires expertise, it is costly and tiresome. To alleviate this problem, we introduce an Active Learning method that significantly decreases the time spent on annotating images. It queries the annotator only about the most informative examples, in this case the hypothetical paths belonging to the structure of interest. Contrary to conventional Active Learning methods, our approach exploits local consistency of linear paths to pick the ones that stand out from their neighborhood.
Our final contribution is a method suited for both Active Learning and proofreading the result, which often requires more time than the automated delineation itself. It investigates edges of the delineation graph and determines the ones that are especially significant for the global reconstruction by perturbing their weights. Our Active Learning and proofreading strategies are combined with a new efficient formulation of an optimal subgraph computation and reduce the annotation effort by up to 80%
clDice -- a Novel Topology-Preserving Loss Function for Tubular Structure Segmentation
Accurate segmentation of tubular, network-like structures, such as vessels,
neurons, or roads, is relevant to many fields of research. For such structures,
the topology is their most important characteristic; particularly preserving
connectedness: in the case of vascular networks, missing a connected vessel
entirely alters the blood-flow dynamics. We introduce a novel similarity
measure termed centerlineDice (short clDice), which is calculated on the
intersection of the segmentation masks and their (morphological) skeleta. We
theoretically prove that clDice guarantees topology preservation up to homotopy
equivalence for binary 2D and 3D segmentation. Extending this, we propose a
computationally efficient, differentiable loss function (soft-clDice) for
training arbitrary neural segmentation networks. We benchmark the soft-clDice
loss on five public datasets, including vessels, roads and neurons (2D and 3D).
Training on soft-clDice leads to segmentation with more accurate connectivity
information, higher graph similarity, and better volumetric scores.Comment: * The authors Suprosanna Shit and Johannes C. Paetzold contributed
equally to the wor
Review on Active and Passive Remote Sensing Techniques for Road Extraction
Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe
Equivariant geometric learning for digital rock physics: estimating formation factor and effective permeability tensors from Morse graph
We present a SE(3)-equivariant graph neural network (GNN) approach that
directly predicting the formation factor and effective permeability from
micro-CT images. FFT solvers are established to compute both the formation
factor and effective permeability, while the topology and geometry of the pore
space are represented by a persistence-based Morse graph. Together, they
constitute the database for training, validating, and testing the neural
networks. While the graph and Euclidean convolutional approaches both employ
neural networks to generate low-dimensional latent space to represent the
features of the micro-structures for forward predictions, the SE(3) equivariant
neural network is found to generate more accurate predictions, especially when
the training data is limited. Numerical experiments have also shown that the
new SE(3) approach leads to predictions that fulfill the material frame
indifference whereas the predictions from classical convolutional neural
networks (CNN) may suffer from spurious dependence on the coordinate system of
the training data. Comparisons among predictions inferred from training the CNN
and those from graph convolutional neural networks (GNN) with and without the
equivariant constraint indicate that the equivariant graph neural network seems
to perform better than the CNN and GNN without enforcing equivariant
constraints
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