Fractals everywhere
A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels
Pattern Recognition Letters
A fractal image analysis systems for fabric inspection based on a box-counting method
Computer Networks and ISDN Systems - Special issue on graphics research and education on the World Wide Web
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Digital Image Processing
Digital Picture Processing
Computing 2-D Min, Median, and Max Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The evaluation of normalized cross correlations for defect detection
Pattern Recognition Letters
Machine Vision: Theory, Algorithms, Practicalities
Machine Vision: Theory, Algorithms, Practicalities
Machine vision for feedback control in a steel rolling mill
Computers in Industry
A Generalised Approach to the use of Sampling for Rapid Object Location
International Journal of Applied Mathematics and Computer Science - Applied Image Processing
Local Correlation and Entropy Maps as Tools for Detecting Defects in Industrial Images
International Journal of Applied Mathematics and Computer Science - Applied Image Processing
Remarks on Hardware Implementation of Image Processing Algorithms
International Journal of Applied Mathematics and Computer Science - Applied Image Processing
Testing (non-)existence of input-output relationships by estimating fractal dimensions
IEEE Transactions on Signal Processing
Parallel implementation of local thresholding in Mitrion-C
International Journal of Applied Mathematics and Computer Science
Hi-index | 0.00 |
Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data ...