Decorrelation process in pattern recognition

Izrail S. Gorian; V. T. Fissenko; V. N. Zelentsov

doi:10.1117/12.138305

16 September 1992 Decorrelation process in pattern recognition

Izrail S. Gorian, V. T. Fissenko, V. N. Zelentsov

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138305
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

In order image objects to be recognized efficiently, it is possible to use statistical encoding methods and information theory results and then to analyze only typical elements groups on the picture. As the typical groups revealing by means of complete sorting is impossible, then one can suppose that the visual analyzer selected such group during the evolution and fixed them in its own mechanism. Hubel's fields situated in visual cortex and responding to linear differently oriented segments are such determined mechanisms. In this paper the original methods of line segments distinguishing as the typical groups for subsequent recognition are investigated. The further development consists of low (1 %) contrast lines detection. The special logical multidimensional filters were created for this purpose. They counted up the line peculiarities on the discrete raster. When this, the every sloping line (within the certain angle sector) is given as a set of some vertical (horizontal) concrete length segments. For example, the slope relatively vertical from 0° to 18° is composed with the vertical segments. Their length cannot be less than 3. The line bent within the range 18° - 26° is built by segments of 3 and 2 element length. In order to enhance the determination of the line head and tail coordinates the cross scanning is applied while filtering. The obtained results were used for space photos analysis with the aim of roads, rivers and other prolongated object detection.

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Izrail S. Gorian, V. T. Fissenko, and V. N. Zelentsov "Decorrelation process in pattern recognition", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138305

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KEYWORDS

Image processing

Object recognition

Digital filtering

Visualization

Image enhancement

Visual analytics

Nonlinear filtering

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