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.

[1]  Elisa Ricci,et al.  Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification , 2007, IEEE Transactions on Medical Imaging.

[2]  Geoffrey E. Hinton,et al.  Machine Learning for Aerial Image Labeling , 2013 .

[3]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[4]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[5]  Yi Li,et al.  Fully Convolutional Instance-Aware Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Raquel Urtasun,et al.  DeepRoadMapper: Extracting Road Topology from Aerial Images , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[7]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[8]  Max A. Viergever,et al.  Ridge-based vessel segmentation in color images of the retina , 2004, IEEE Transactions on Medical Imaging.

[9]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Tomasz Malisiewicz,et al.  RoomNet: End-to-End Room Layout Estimation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[11]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[14]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[15]  Luc Van Gool,et al.  Deep Retinal Image Understanding , 2016, MICCAI.

[16]  Max W. K. Law,et al.  Three Dimensional Curvilinear Structure Detection Using Optimally Oriented Flux , 2008, ECCV.

[17]  Stephen Lin,et al.  DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field , 2016, MICCAI.

[18]  Li Cheng,et al.  Learning to Boost Filamentary Structure Segmentation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  David J. Kriegman,et al.  Dense Volume-to-Volume Vascular Boundary Detection , 2016, MICCAI.

[20]  Jia Deng,et al.  Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.

[21]  Min Bai,et al.  TorontoCity: Seeing the World with a Million Eyes , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[22]  Jon Atli Benediktsson,et al.  Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images , 2010, Pattern Recognit. Lett..

[23]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[24]  Vincent Lepetit,et al.  Projection onto the Manifold of Elongated Structures for Accurate Extraction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[25]  Jitendra Malik,et al.  Learning to detect natural image boundaries using local brightness, color, and texture cues , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Chia-Ling Tsai,et al.  A Broadly Applicable 3-D Neuron Tracing Method Based on Open-Curve Snake , 2011, Neuroinformatics.

[27]  Vincent Lepetit,et al.  Supervised Feature Learning for Curvilinear Structure Segmentation , 2013, MICCAI.

[28]  Jan Dirk Wegner,et al.  A Higher-Order CRF Model for Road Network Extraction , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Feng Lin,et al.  Tracing Retinal Blood Vessels by Matrix-Forest Theorem of Directed Graphs , 2014, MICCAI.

[30]  Vincent Lepetit,et al.  Multiscale Centerline Detection by Learning a Scale-Space Distance Transform , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[31]  Matthew B. Blaschko,et al.  Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images , 2014, MICCAI.

[32]  P. Bankhead,et al.  Fast Retinal Vessel Detection and Measurement Using Wavelets and Edge Location Refinement , 2012, PloS one.