Explicit Modelling of Invariances in Bernoulli Mixtures for Binary Images

Authors:
Verónica Romero;Adrià Giménez;Alfons Juan
Affiliations:
Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València (Spain),;Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València (Spain),;Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Camí de Vera s/n, 46022 València (Spain),
Venue:
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Year:
2007

Citing 2
Cited 2

Statistical Pattern Recognition: A Review

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bernoulli Mixture Models for Binary Images

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03

Bernoulli HMMs at Subword Level for Handwritten Word Recognition

IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Machine learning using Bernoulli mixture models: Clustering, rule extraction and dimensionality reduction

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bernoulli mixture models have been recently proposed as simple yet powerful probabilistic models for binary images in which each image pattern is modelled by a different Bernoulli prototype (component). A possible limitation of these models, however, is that usual geometric transformations of image patterns are not explicitly modelled and, therefore, each natural transformation of an image pattern has to be independentlymodelled using a different, rigidprototype. In this work, we propose a simple technique to make these rigid prototypes more flexible by explicit modelling of invariances to translation, scaling and rotation. Results are reported on a task of handwritten Indian digits recognition.