Sensitive segmentation of low-contrast multispectral images based on multiparameter space-resonance imaging method

Alexander M. Akhmetshin; Lyudmila G. Akhmetshin

doi:10.1117/12.444193

5 October 2001 Sensitive segmentation of low-contrast multispectral images based on multiparameter space-resonance imaging method

Alexander M. Akhmetshin, Lyudmila G. Akhmetshin

Author Affiliations +

Proceedings Volume 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision; (2001) https://doi.org/10.1117/12.444193
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

A new method of low contrast multispectral, hyperspectral and multiparameter images segmentation is outlined. The one has significant advantage in sensitivity and space resolving power of segmentation in comparison with known methods such as principal component transformation and fuzzy C-means clustering segmentation ones. New method is based on using of two important stages: 1) application virtual long-wave holographic transformation to each separate image of analyzed multispectral sequence (it is needed for increasing sensitivity of further analysis); 2) to each pixel of analyzed multispectral image is compare a virtual nonrecursive digital filter with complex coefficients. The one is characterized by its amplitude-frequency (AFC) and phase-frequency (PFC) characteristics. Information features used for visualization and segmentation are frequencies corresponded to maximum (resonance point) or minimum (antiresonance point) of AFC and group delay function calculated on base PFC. Information possibilities of new method are demonstrated on examples of multispectral remote sensing, various physical nature geophysical fields fusion and multiparameter MRI brain tumor hidden area influence detection.

Citation Download Citation

Alexander M. Akhmetshin and Lyudmila G. Akhmetshin "Sensitive segmentation of low-contrast multispectral images based on multiparameter space-resonance imaging method", Proc. SPIE 4572, Intelligent Robots and Computer Vision XX: Algorithms, Techniques, and Active Vision, (5 October 2001); https://doi.org/10.1117/12.444193

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