Pixel-level object segmentation from multispectral sensor imagery

Keith C. Drake; Richard Y. Kim; Tony Y. Kim

doi:10.1117/12.169458

25 February 1994 Pixel-level object segmentation from multispectral sensor imagery

Keith C. Drake, Richard Y. Kim, Tony Y. Kim

Proceedings Volume 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs; (1994) https://doi.org/10.1117/12.169458
Event: 22nd Applied Imagery Pattern Recognition Workshop, 1993, Washington, DC, United States

Abstract

Successful object classification is highly dependent upon initial segmentation of an object from its background. For complex, real-world imaging applications, this task is extremely challenging and critical to the success of the recognition system. Traditional object segmentation techniques often rely heavily upon noise removal during preprocessing and subsequently employ image-level segmentation strategies. Because effective noise-removal strategies are often difficult to develop for real-world imagery, alternate methods are required for object segmentation. An alternate approach is to determine target/nontarget status of image regions at the pixel level. In this manner, noise removal and object segmentation are performed in a single process. The approach takes advantage of the large amount of information contained in present-day, multispectral imagery. The key issues associated with this approach are a robust pixel information representation and an information fusion algorithm to process pixel-level information.

Citation Download Citation

Keith C. Drake, Richard Y. Kim, and Tony Y. Kim "Pixel-level object segmentation from multispectral sensor imagery", Proc. SPIE 2103, 22nd AIPR Workshop: Interdisciplinary Computer Vision: Applications and Changing Needs, (25 February 1994); https://doi.org/10.1117/12.169458

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