Minimal representations for translation-invariant set mappings by mathematical morphology
SIAM Journal on Applied Mathematics
Elements of information theory
Elements of information theory
Optimal mean-square N-observation digital morphological filters: i. optimal binary filters
CVGIP: Image Understanding
Original Contribution: Stacked generalization
Neural Networks
Secondarily constrained Boolean filters
Signal Processing
Multiresolution Analysis for Optimal Binary Filters
Journal of Mathematical Imaging and Vision
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Binary Image Operator Design Based on Stacked Generalization
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
A Maximum-Likelihood Approach for Multiresolution W-Operator Design
SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
W-operator window design by minimization of mean conditional entropy
Pattern Analysis & Applications
Two-stage Binary Image Operator Design: an Approach Based on Interaction Information
SIBGRAPI '07 Proceedings of the XX Brazilian Symposium on Computer Graphics and Image Processing
Multilevel Training of Binary Morphological Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial neural networks applied to statistical design of window operators
Pattern Recognition Letters
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The design of translation invariant and locally defined binary image operators over large windows is made difficult by decreased statistical precision and increased training time. We present a complete framework for the application of stacked design, a recently proposed technique to create two-stage operators that circumvents that difficulty. We propose a novel algorithm, based on Information Theory, to find groups of pixels that should be used together to predict the output value. We employ this algorithm to automate the process of creating a set of first-level operators that are later combined in a global operator. We also propose a principled way to guide this combination, by using feature selection and model comparison. Experimental results show that the proposed framework leads to better results than single stage design.