Distance transformations in digital images
Computer Vision, Graphics, and Image Processing
Measures of correspondence between binary patterns
Image and Vision Computing
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Robust comparison of binary images
Pattern Recognition Letters
Sequential Operations in Digital Picture Processing
Journal of the ACM (JACM)
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Picture Processing
SemQuery: Semantic Clustering and Querying on Heterogeneous Features for Visual Data
IEEE Transactions on Knowledge and Data Engineering
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Euclidean Distance Transform Using Grayscale Morphology Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Time Euclidean Distance Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hausdorff Kernel for 3D Object Acquisition and Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Robust Face Detection Using the Hausdorff Distance
AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pictorial Queries by Image Similarity
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Word Spotting in Chinese Document Images without Layout Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
An Approach to Word Image Matching Based on Weighted Hausforff Distance
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A fast binary-image comparison method with local-dissimilarity quantification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
IEEE Transactions on Software Engineering
A new Hausdorff distance for image matching
Pattern Recognition Letters
Object matching algorithms using robust Hausdorff distance measures
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A Non-symmetrical Method of Image Local-Difference Comparison for Ancient Impressions Dating
Graphics Recognition. Recent Advances and New Opportunities
Evaluation of Brain MRI Alignment with the Robust Hausdorff Distance Measures
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A new image quality measure considering perceptual information and local spatial feature
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Journal of Mathematical Imaging and Vision
Appropriate formulation of the objective function for the history matching of seismic attributes
Computers & Geosciences
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In this paper, we present a method for binary image comparison. For binary images, intensity information is poor and shape extraction is often difficult. Therefore binary images have to be compared without using feature extraction. Due to the fact that different scene patterns can be present in the images, we propose a modified Hausdorff distance (HD) locally measured in an adaptive way. The resulting set of measures is richer than a single global measure. The local HD measures result in a local-dissimilarity map (LDMap) including the dissimilarity spatial layout. A classification of the images in function of their similarity is carried out on the LDMaps using a support vector machine. The proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency.