Color image segmentation

Kimberley A. McCrae; Dennis W. Ruck; Steven K. Rogers; Mark E. Oxley

doi:10.1117/12.169979

2 March 1994 Color image segmentation

Kimberley A. McCrae, Dennis W. Ruck, Steven K. Rogers, Mark E. Oxley

Proceedings Volume 2243, Applications of Artificial Neural Networks V; (1994) https://doi.org/10.1117/12.169979
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

The most difficult stage of automated target recognition is segmentation. Current segmentation problems include faces and tactical targets; previous efforts to segment these objects have used intensity and motion cues. This paper develops a color preprocessing scheme to be used with the other segmentation techniques. A neural network is trained to identify the color of a desired object, eliminating all but that color from the scene. Gabor correlations and 2D wavelet transformations will be performed on stationary images; and 3D wavelet transforms on multispectral data will incorporate color and motion detection into the machine visual system. The paper will demonstrate that color and motion cues can enhance a computer segmentation system. Results from segmenting faces both from the AFIT data base and from video taped television are presented; results from tactical targets such as tanks and airplanes are also given. Color preprocessing is shown to greatly improve the segmentation in most cases.

Citation Download Citation

Kimberley A. McCrae, Dennis W. Ruck, Steven K. Rogers, and Mark E. Oxley "Color image segmentation", Proc. SPIE 2243, Applications of Artificial Neural Networks V, (2 March 1994); https://doi.org/10.1117/12.169979

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