Neural networks in visualization of multispectral medical images
Information Sciences: an International Journal
Morphological bidirectional associative memories
Neural Networks
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Journal of Mathematical Imaging and Vision
Pattern Classification (2nd Edition)
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A single individual evolutionary strategy for endmember search in hyperspectral images
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: FEA 2002
Convex Optimization
A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing
IEEE Transactions on Signal Processing
Morphological associative memories
IEEE Transactions on Neural Networks
Feature identification and extraction in function fields
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
Permutation-based finite implicative fuzzy associative memories
Information Sciences: an International Journal
Lattice independent component analysis for functional magnetic resonance imaging
Information Sciences: an International Journal
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
Hybrid computational methods for hyperspectral image analysis
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Fusion of supervised and unsupervised learning for improved classification of hyperspectral images
Information Sciences: an International Journal
Quantale-based autoassociative memories with an application to the storage of color images
Pattern Recognition Letters
An approach to SWIR hyperspectral hand biometrics
Information Sciences: an International Journal
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In this manuscript we propose a method for the autonomous determination of endmembers in hyperspectral imagery based on recent theoretical advancements on lattice auto-associative memories. Given a hyperspectral image, the lattice algebra approach finds in a single-pass all possible candidate endmembers from which various affinely independent sets of final endmembers may be derived. In contrast to other endmember detection methods, the endmembers found using two dual canonical lattice matrices are geometrically linked to the data set spectra. The mathematical foundation of the proposed method is first described in some detail followed by application examples that illustrate the key steps of the proposed lattice based method.