Fundamentals of digital image processing
Fundamentals of digital image processing
Stability of Oja's PCA subspace rule
Neural Computation
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Random Iterative Models
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Handwritten Digit Recognition by Local Principal Components Analysis
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Pattern Recognition by Invariant Reference Points
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Local Subspace Method for Pattern Recognition
CAIP '97 Proceedings of the 7th International Conference on Computer Analysis of Images and Patterns
An automatic system for model-based coding of faces
DCC '95 Proceedings of the Conference on Data Compression
Principal component extraction using recursive least squares learning
IEEE Transactions on Neural Networks
Stability Analysis of Oja-RLS Learning Rule
Fundamenta Informaticae
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The results of theoretical analysis for stochastic convergence of the modified Oja-RLS learning rule are presented. The rule is used to find Karhunen Loeve Transform. Based on this algorithm, an image compression scheme is developed by combining approximated 2D KLT transform and JPEG standard quantization and entropy coding stages. Though 2D KLT transform is of higher complexity than 2D DCT, the resulting PSNR quality of reconstructed images is better even by 2[dB].