Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Digital Image Processing Methods
Digital Image Processing Methods
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
Robust, object-based high-resolution image reconstruction from low-resolution video
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
It is widely accepted that the design of morphological filters, which are optimal in some sense, is a difficult task. In this paper a novel method for optimal learning of morphological filtering parameters (Genetic training algorithm for morphological filters, GTAMF) is presented. GTAMF adopts new crossover and mutation operators called the curved cylinder crossover and master-slave mutation to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and markedly improves the performances of morphological filters. The operation of a morphological filter can be divided into, two basic problems including morphological operation and structuring element (SE) selection: The rules for morphological operations are predefined so that the filter's properties depend merely on the selection of SE. By means of adaptive optimization training, structuring elements possess the shape and structural characteristics of image targets, and give specific information to SE. Morphological filters formed in this way become certainly intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.