Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Character extraction of license plates from video
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Binarization of Low Quality Text Using a Markov Random Field Model
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Learning to Detect Scene Text Using a Higher-Order MRF with Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Handwritten character skeletonisation for forensic document analysis
Proceedings of the 2005 ACM symposium on Applied computing
On foreground — background separation in low quality document images
International Journal on Document Analysis and Recognition
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Models for Patch Based Image Restoration
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Adaptive degraded document image binarization
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient belief propagation for higher-order cliques using linear constraint nodes
Computer Vision and Image Understanding
Contrast Enhancement in Multispectral Images by Emphasizing Text Regions
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
International Journal of Computer Vision
Markov Random Field Modeling in Image Analysis
Markov Random Field Modeling in Image Analysis
Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
P³ & Beyond: Move Making Algorithms for Solving Higher Order Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
RSLDI: Restoration of single-sided low-quality document images
Pattern Recognition
Document Image Binarisation Using Markov Field Model
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Spatial and Spectral Based Segmentation of Text in Multispectral Images of Ancient Documents
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
ICDAR 2009 Document Image Binarization Contest (DIBCO 2009)
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Comparison of energy minimization algorithms for highly connected graphs
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Multispectral Filter-Wheel Cameras: Geometric Distortion Model and Compensation Algorithms
IEEE Transactions on Image Processing
Registration of multi-spectral manuscript images
VAST'07 Proceedings of the 8th International conference on Virtual Reality, Archaeology and Intelligent Cultural Heritage
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
A learning framework for the optimization and automation of document binarization methods
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Multi-spectral imaging for the analysis and preservation of ancient documents has gained high attention in recent years. While readability enhancement is based on the multi-spectral image corpus, foreground-background separation still relies mainly on gray level or color images. In this paper we propose a foreground-background separation algorithm designed for multi-spectral images. The main contribution is the simultaneously utilization of spectral and spatial features. While spectral features incorporate the spectral components of the multi-spectral images, the spatial features are based on stroke properties. Higher order Markov Random Fields enables an efficient way to combine both features. To solve higher order energy functions, we introduce a new message update rule in the well known belief propagation algorithm based on a higher order potential function.