Learning Deep Structured Active Contours End-to-End

The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Netw...

Full description

Saved in:
Bibliographic Details
Published in:2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 8877 - 8885
Main Authors: Zhang, Lisa, Bai, Min, Liao, Renjie, Urtasun, Raquel, Marcos, Diego, Tuia, Devis, Kellenberger, Benjamin
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2018
Subjects:
Active contours
Buildings
Force
Image segmentation
Inference algorithms
Semantics
Training
ISSN:1063-6919
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). However, even the latest evolutions struggle to precisely delineating borders, which often leads to geometric distortions and inadvertent fusion of adjacent building instances. We propose to overcome this issue by exploiting the distinct geometric properties of buildings. To this end, we present Deep Structured Active Contours (DSAC), a novel framework that integrates priors and constraints into the segmentation process, such as continuous boundaries, smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models (ACM), a family of constraint- and prior-based polygonal models. We learn ACM parameterizations per instance using a CNN, and show how to incorporate all components in a structured output model, making DSAC trainable end-to-end. We evaluate DSAC on three challenging building instance segmentation datasets, where it compares favorably against state-of-the-art. Code will be made available on https://github.com/dmarcosg/DSAC.
AbstractList The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). However, even the latest evolutions struggle to precisely delineating borders, which often leads to geometric distortions and inadvertent fusion of adjacent building instances. We propose to overcome this issue by exploiting the distinct geometric properties of buildings. To this end, we present Deep Structured Active Contours (DSAC), a novel framework that integrates priors and constraints into the segmentation process, such as continuous boundaries, smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models (ACM), a family of constraint- and prior-based polygonal models. We learn ACM parameterizations per instance using a CNN, and show how to incorporate all components in a structured output model, making DSAC trainable end-to-end. We evaluate DSAC on three challenging building instance segmentation datasets, where it compares favorably against state-of-the-art. Code will be made available on https://github.com/dmarcosg/DSAC.
Author Zhang, Lisa
Urtasun, Raquel
Bai, Min
Tuia, Devis
Marcos, Diego
Liao, Renjie
Kellenberger, Benjamin
Author_xml – sequence: 1
  givenname: Lisa
  surname: Zhang
  fullname: Zhang, Lisa
– sequence: 2
  givenname: Min
  surname: Bai
  fullname: Bai, Min
– sequence: 3
  givenname: Renjie
  surname: Liao
  fullname: Liao, Renjie
– sequence: 4
  givenname: Raquel
  surname: Urtasun
  fullname: Urtasun, Raquel
– sequence: 5
  givenname: Diego
  surname: Marcos
  fullname: Marcos, Diego
– sequence: 6
  givenname: Devis
  surname: Tuia
  fullname: Tuia, Devis
– sequence: 7
  givenname: Benjamin
  surname: Kellenberger
  fullname: Kellenberger, Benjamin
BookMark eNotzMtKxDAUgOEoCo5j1y7c5AVST-7JcqgzKhQUb9uhTc5IRdMhTQXf3gFdfZuf_5ycpDEhIZccas7BXzdvj0-1AO5qAC_0Eam8dVxLZ4wS4I_JgoORzHjuz0g1TR8AIIyTTukFkS12OQ3pnd4g7ulzyXMoc8ZIV6EM30ibMZVxzhNdp8jKyA5ckNNd9zlh9e-SvG7WL80dax9u75tVywZudWEyRNujUd5yG71A30c0gmNEZ4JVve65jDYYExCl0BhABXdIOxtc6NROLsnV33dAxO0-D19d_tk6bT0IKX8BgoxHHw
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/CVPR.2018.00925
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP) 1998-present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Applied Sciences
EISBN 9781538664209
1538664208
EISSN 1063-6919
EndPage 8885
ExternalDocumentID 8579023
Genre orig-research
GroupedDBID 6IE
6IH
6IL
6IN
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
IJVOP
OCL
RIE
RIL
RIO
ID FETCH-LOGICAL-i175t-3cd7be649717d92e9bde621ede86c74b5b13d7c66cee325ec04c817da7c8ca4f3
IEDL.DBID RIE
ISICitedReferencesCount 135
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000457843609005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Wed Aug 27 02:52:16 EDT 2025
IsPeerReviewed false
IsScholarly true
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i175t-3cd7be649717d92e9bde621ede86c74b5b13d7c66cee325ec04c817da7c8ca4f3
PageCount 9
ParticipantIDs ieee_primary_8579023
PublicationCentury 2000
PublicationDate 2018-Jun
PublicationDateYYYYMMDD 2018-06-01
PublicationDate_xml – month: 06
  year: 2018
  text: 2018-Jun
PublicationDecade 2010
PublicationTitle 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
PublicationTitleAbbrev CVPR
PublicationYear 2018
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0002683845
ssj0003211698
Score 2.5212739
Snippet The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently,...
SourceID ieee
SourceType Publisher
StartPage 8877
SubjectTerms Active contours
Buildings
Force
Image segmentation
Inference algorithms
Semantics
Training
Title Learning Deep Structured Active Contours End-to-End
URI https://ieeexplore.ieee.org/document/8579023
WOSCitedRecordID wos000457843609005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV07bwMhDLaSqEOntE2qvnVDx9IcB8djrNJEnaKoL2WLOPBVWS5RXr-_wJ3SDl068RADGLCxsT8D3DstGc-ZIMxQR7gtUmJchK3MjJJeP3BZnWxCTiZqNtPTFjwcYmEQMTqf4WOoxr98t7S7YCobqFxqL2Pa0JZS1LFaB3tKJhRTzQ9ZaDOv2QitGjQfmurB8HP6Gny5gvOkDqmxf6VTidJk3P3fPE6g_xOWl0wPAucUWlidQbd5RybNLd30gDWgqV_JM-IqeYsQsbu1H_MUuVsSIKn8Gdoko8qR7ZL4og8f49H78IU0yRHIwkv8LWHWyQIF114fczpDXTgUGUWHSljJi7ygzEkrhJ8Uy3K0KbfKDzXSKmt4yc6hUy0rvIDEGmrLgtFSWsqNTg01KfVbpywvSs8RL6EXaDBf1fgX82b5V393X8NxIHLtTnUDHb9GvIUju98uNuu7uGnf7CqWsw
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEJ4gmugJFYxv9-DRynbb7eNoEIIRCVE03Ei37Rouu4SHv992adCDF099pId2-phOO_N9ALdGckJTwhBR2CCqsxgpU8FWJkpwZx-YZEM2wYdDMZnIUQ3utrEw1trK-cze-2z1l29KvfZPZW2Rcul0zA7seuasEK21fVFJmCAi_JH5MnG2DZMi4PngWLY7H6NX783l3SelJ8f-RahS6ZNe4389OYTWT2BeNNqqnCOo2eIYGuEmGYV9umwCCbCpn9GjtfPorQKJXS9cm4fqfIs8KJVbRcuoWxi0KpFLWvDe6447fRToEdDM6fwVItrwzDIqnUVmZGJlZixLsDVWMM1plmaYGK4Zc50iSWp1TLVwTRXXQiuakxOoF2VhTyHSCus8IzjnGlMlY4VVjN3kCU2z3J2JZ9D0MpjONwgY0zD887-rb2C_P34ZTAdPw-cLOPAC3zhXXULdjddewZ7-Ws2Wi-tqAr8B-EiZ_A
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2018+IEEE%2FCVF+Conference+on+Computer+Vision+and+Pattern+Recognition&rft.atitle=Learning+Deep+Structured+Active+Contours+End-to-End&rft.au=Zhang%2C+Lisa&rft.au=Bai%2C+Min&rft.au=Liao%2C+Renjie&rft.au=Urtasun%2C+Raquel&rft.date=2018-06-01&rft.pub=IEEE&rft.eissn=1063-6919&rft.spage=8877&rft.epage=8885&rft_id=info:doi/10.1109%2FCVPR.2018.00925&rft.externalDocID=8579023