6,532 research outputs found
Iterative Deep Learning for Road Topology Extraction
This paper tackles the task of estimating the topology of road networks from
aerial images. Building on top of a global model that performs a dense
semantical classification of the pixels of the image, we design a Convolutional
Neural Network (CNN) that predicts the local connectivity among the central
pixel of an input patch and its border points. By iterating this local
connectivity we sweep the whole image and infer the global topology of the road
network, inspired by a human delineating a complex network with the tip of
their finger. We perform an extensive and comprehensive qualitative and
quantitative evaluation on the road network estimation task, and show that our
method also generalizes well when moving to networks of retinal vessels.Comment: BMVC 2018 camera ready. Code:
https://github.com/carlesventura/iterative-deep-learning. arXiv admin note:
substantial text overlap with arXiv:1712.0121
Iterative Deep Learning for Network Topology Extraction
This paper tackles the task of estimating the topology of filamentary
networks such as retinal vessels and road networks. Building on top of a global
model that performs a dense semantical classification of the pixels of the
image, we design a Convolutional Neural Network (CNN) that predicts the local
connectivity between the central pixel of an input patch and its border points.
By iterating this local connectivity we sweep the whole image and infer the
global topology of the filamentary network, inspired by a human delineating a
complex network with the tip of their finger.
We perform an extensive and comprehensive qualitative and quantitative
evaluation on two tasks: retinal veins and arteries topology extraction and
road network estimation. In both cases, represented by two publicly available
datasets (DRIVE and Massachusetts Roads), we show superior performance to very
strong baselines
Topological Map Extraction from Overhead Images
We propose a new approach, named PolyMapper, to circumvent the conventional
pixel-wise segmentation of (aerial) images and predict objects in a vector
representation directly. PolyMapper directly extracts the topological map of a
city from overhead images as collections of building footprints and road
networks. In order to unify the shape representation for different types of
objects, we also propose a novel sequentialization method that reformulates a
graph structure as closed polygons. Experiments are conducted on both existing
and self-collected large-scale datasets of several cities. Our empirical
results demonstrate that our end-to-end learnable model is capable of drawing
polygons of building footprints and road networks that very closely approximate
the structure of existing online map services, in a fully automated manner.
Quantitative and qualitative comparison to the state-of-the-art also shows that
our approach achieves good levels of performance. To the best of our knowledge,
the automatic extraction of large-scale topological maps is a novel
contribution in the remote sensing community that we believe will help develop
models with more informed geometrical constraints.Comment: ICCV 201
Joint Segmentation and Path Classification of Curvilinear Structures
Detection of curvilinear structures in images has long been of interest. One
of the most challenging aspects of this problem is inferring the graph
representation of the curvilinear network. Most existing delineation approaches
first perform binary segmentation of the image and then refine it using either
a set of hand-designed heuristics or a separate classifier that assigns
likelihood to paths extracted from the pixel-wise prediction. In our work, we
bridge the gap between segmentation and path classification by training a deep
network that performs those two tasks simultaneously. We show that this
approach is beneficial because it enforces consistency across the whole
processing pipeline. We apply our approach on roads and neurons datasets
Leveraging Crowdsourced GPS Data for Road Extraction from Aerial Imagery
Deep learning is revolutionizing the mapping industry. Under lightweight
human curation, computer has generated almost half of the roads in Thailand on
OpenStreetMap (OSM) using high-resolution aerial imagery. Bing maps are
displaying 125 million computer-generated building polygons in the U.S. While
tremendously more efficient than manual mapping, one cannot map out everything
from the air. Especially for roads, a small prediction gap by image occlusion
renders the entire road useless for routing. Misconnections can be more
dangerous. Therefore computer-based mapping often requires local verifications,
which is still labor intensive. In this paper, we propose to leverage
crowdsourced GPS data to improve and support road extraction from aerial
imagery. Through novel data augmentation, GPS rendering, and 1D transpose
convolution techniques, we show almost 5% improvements over previous
competition winning models, and much better robustness when predicting new
areas without any new training data or domain adaptation.Comment: To be published in IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation
We develop a connection sensitive attention U-Net(CSAU) for accurate retinal
vessel segmentation. This method improves the recent attention U-Net for
semantic segmentation with four key improvements: (1) connection sensitive loss
that models the structure properties to improve the accuracy of pixel-wise
segmentation; (2) attention gate with novel neural network structure and
concatenating DOWN-Link to effectively learn better attention weights on fine
vessels; (3) integration of connection sensitive loss and attention gate to
further improve the accuracy on detailed vessels by additionally concatenating
attention weights to features before output; (4) metrics of connection
sensitive accuracy to reflect the segmentation performance on boundaries and
thin vessels.
Our method can effectively improve state-of-the-art vessel segmentation
methods that suffer from difficulties in presence of abnormalities, bifurcation
and microvascular. This connection sensitive loss tightly integrates with the
proposed attention U-Net to accurately (i) segment retinal vessels, and (ii)
reserve the connectivity of thin vessels by modeling the structural properties.
Our method achieves the leading position on DRIVE, STARE and HRF datasets among
the state-of-the-art methods
Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
Inferring road graphs from satellite imagery is a challenging computer vision
task. Prior solutions fall into two categories: (1) pixel-wise
segmentation-based approaches, which predict whether each pixel is on a road,
and (2) graph-based approaches, which predict the road graph iteratively. We
find that these two approaches have complementary strengths while suffering
from their own inherent limitations.
In this paper, we propose a new method, Sat2Graph, which combines the
advantages of the two prior categories into a unified framework. The key idea
in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which
encodes the road graph into a tensor representation. GTE makes it possible to
train a simple, non-recurrent, supervised model to predict a rich set of
features that capture the graph structure directly from an image. We evaluate
Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior
methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior
work only infers planar road graphs, our approach is capable of inferring
stacked roads (e.g., overpasses), and does so robustly.Comment: ECCV 202
Topo-boundary: A Benchmark Dataset on Topological Road-boundary Detection Using Aerial Images for Autonomous Driving
Road-boundary detection is important for autonomous driving. It can be used
to constrain autonomous vehicles running on road areas to ensure driving
safety. Compared with online road-boundary detection using on-vehicle
cameras/Lidars, offline detection using aerial images could alleviate the
severe occlusion issue. Moreover, the offline detection results can be directly
employed to annotate high-definition (HD) maps. In recent years, deep-learning
technologies have been used in offline detection. But there still lacks a
publicly available dataset for this task, which hinders the research progress
in this area. So in this paper, we propose a new benchmark dataset, named
\textit{Topo-boundary}, for offline topological road-boundary detection. The
dataset contains 25,295 -sized 4-channel aerial images. Each
image is provided with 8 training labels for different sub-tasks. We also
design a new entropy-based metric for connectivity evaluation, which could
better handle noises or outliers. We implement and evaluate 3
segmentation-based baselines and 5 graph-based baselines using the dataset. We
also propose a new imitation-learning-based baseline which is enhanced from our
previous work. The superiority of our enhancement is demonstrated from the
comparison. The dataset and our-implemented code for the baselines are
available at \texttt{\url{https://tonyxuqaq.github.io/Topo-boundary/}}.Comment: Accepted by IEEE Robotics and Automation Letters(RA-L) and The
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
202
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
A novel centerline extraction framework is reported which combines an
end-to-end trainable multi-task fully convolutional network (FCN) with a
minimal path extractor. The FCN simultaneously computes centerline distance
maps and detects branch endpoints. The method generates single-pixel-wide
centerlines with no spurious branches. It handles arbitrary tree-structured
object with no prior assumption regarding depth of the tree or its bifurcation
pattern. It is also robust to substantial scale changes across different parts
of the target object and minor imperfections of the object's segmentation mask.
To the best of our knowledge, this is the first deep-learning based centerline
extraction method that guarantees single-pixel-wide centerline for a complex
tree-structured object. The proposed method is validated in coronary artery
centerline extraction on a dataset of 620 patients (400 of which used as test
set). This application is challenging due to the large number of coronary
branches, branch tortuosity, and large variations in length, thickness, shape,
etc. The proposed method generates well-positioned centerlines, exhibiting
lower number of missing branches and is more robust in the presence of minor
imperfections of the object segmentation mask. Compared to a state-of-the-art
traditional minimal path approach, our method improves patient-level success
rate of centerline extraction from 54.3% to 88.8% according to independent
human expert review.Comment: Accepted by the international conference on Information Processing in
Medical Imaging (IPMI) 201
Inferring and Improving Street Maps with Data-Driven Automation
Street maps are a crucial data source that help to inform a wide range of
decisions, from navigating a city to disaster relief and urban planning.
However, in many parts of the world, street maps are incomplete or lag behind
new construction. Editing maps today involves a tedious process of manually
tracing and annotating roads, buildings, and other map features.
Over the past decade, many automatic map inference systems have been proposed
to automatically extract street map data from satellite imagery, aerial
imagery, and GPS trajectory datasets. However, automatic map inference has
failed to gain traction in practice due to two key limitations: high error
rates (low precision), which manifest in noisy inference outputs, and a lack of
end-to-end system design to leverage inferred data to update existing street
maps.
At MIT and QCRI, we have developed a number of algorithms and approaches to
address these challenges, which we combined into a new system we call Mapster.
Mapster is a human-in-the-loop street map editing system that incorporates
three components to robustly accelerate the mapping process over traditional
tools and workflows: high-precision automatic map inference, data refinement,
and machine-assisted map editing.
Through an evaluation on a large-scale dataset including satellite imagery,
GPS trajectories, and ground-truth map data in forty cities, we show that
Mapster makes automation practical for map editing, and enables the curation of
map datasets that are more complete and up-to-date at less cost
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