Markov random field modeling in computer vision
Markov random field modeling in computer vision
Modeling and rendering architecture from photographs: a hybrid geometry- and image-based approach
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
EM algorithms for PCA and SPCA
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Calibrated, Registered Images of an Extended Urban Area
International Journal of Computer Vision
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
ACM SIGGRAPH 2003 Papers
A Computational Model for Periodic Pattern Perception Based on Frieze and Wallpaper Groups
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Automated Method for Large-Scale, Ground-Based City Model Acquisition
International Journal of Computer Vision
Modelling and Interpretation of Architecture from Several Images
International Journal of Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Near-regular texture analysis and manipulation
ACM SIGGRAPH 2004 Papers
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
MCMC-Based Particle Filtering for Tracking a Variable Number of Interacting Targets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bottom-up/Top-Down Image Parsing by Attribute Graph Grammar
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Image-based procedural modeling of facades
ACM SIGGRAPH 2007 papers
Tracking dynamic near-regular texture under occlusion and rapid movements
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Discovering texture regularity as a higher-order correspondence problem
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Region filling and object removal by exemplar-based image inpainting
IEEE Transactions on Image Processing
Spatiotemporal Inpainting for Recovering Texture Maps of Occluded Building Facades
IEEE Transactions on Image Processing
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
Facade Structure Parameterization Based on Similarity Detection from Single Image
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Exploiting repetitive object patterns for model compression and completion
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Unsupervised facade segmentation using repetitive patterns
Proceedings of the 32nd DAGM conference on Pattern recognition
Translation-symmetry-based perceptual grouping with applications to urban scenes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
Reconstruction of structure and texture of city building facades
Programming and Computing Software
Gable roof detection in terrestrial images
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Applications of Geometry Processing: Grammar-based 3D facade segmentation and reconstruction
Computers and Graphics
A three-layered approach to facade parsing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision
Image and Vision Computing
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As part of an architectural modeling project, this paper investigates the problem of understanding and manipulating images of buildings. Our primary motivation is to automatically detect and seamlessly remove unwanted foreground elements from urban scenes. Without explicit handling, these objects will appear pasted as artifacts on the model. Recovering the building facade in a video sequence is relatively simple because parallax induces foreground/background depth layers, but here we consider static images only. We develop a series of methods that enable foreground removal from images of buildings or brick walls. The key insight is to use a prioriknowledge about grid patterns on building facades that can be modeled as Near Regular Textures (NRT). We describe a Markov Random Field (MRF) model for such textures and introduce a Markov Chain Monte Carlo (MCMC) optimization procedure for discovering them. This simple spatial rule is then used as a starting point for inference of missing windows, facade segmentation, outlier identification, and foreground removal.