Visual morphology

Morphology is one of the most active research areas in nonlinear image processing and has many applications. The basic reason for this is the good noise attenuation and edge preservation performance, quantitative description of geometrical structure while maintaining low computational complexity. Over the past twenty years, many efforts have been devoted in psychophysics and physiology to analyzes the response of the human visual system which can perfectly detect and recognize the image information. Because the human visual system is an excellent image processor, it is natural to find and use a gap between the human visual system and morphology. The goal of this article is to introduce a new concept,Visual Morphology, which shares many of the important characteristics of the human visual system and morphology. This technique has been employed on NASA's Earth Observing System satellite data for the purpose of anomaly detection and visualization. Also, it has shown the beneficial effect of applying SAR images. It helps to improve the detection of small targets. Both theoretical analysis and simulation results have shown that the proposed concept has a great future.

[1]  D G Pelli,et al.  Uncertainty explains many aspects of visual contrast detection and discrimination. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  Moncef Gabbouj,et al.  Median-rational hybrid filters , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[3]  David Casasent,et al.  Optical morphological processors: gray scale with binary structuring elements, detection, and clutter reduction , 1992, Other Conferences.

[4]  Jean-Charles Pinoli,et al.  A general comparative study of the multiplicative homomorphic, log-ratio and logarithmic image processing approaches , 1997, Signal Process..

[5]  Jean-Charles Pinoli,et al.  The Logarithmic Image Processing Model: Connections with Human Brightness Perception and Contrast Estimators , 1997, Journal of Mathematical Imaging and Vision.