17 December 1998 Data fusion using a hierarchy of self-organizing feature maps
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Abstract
The synergistic use of data acquired from difference sensors will enable autonomous manufacturing equipment to make faster and more intelligent decisions about the current status of the workspace. Multisensor data fusion deals with mathematical and statistical issues arising from the combination of different sources of sensory information into a single representational format. A fundamental problem in data fusion is associating the data captured by one sensor with that from another sensor or the same sensor at a different point in time. This paper describes a non- statistical unsupervised hierarchical clustering algorithm used to associate the complementary feature vectors extracted from different data sets. Each level in the hierarchy consists of one or more self-organizing feature maps that contain a small number of cluster units based on the combined feature set derived from the original data. The unsupervised learning algorithm ensures that 'similar' feature vectors will be assigned to cluster units that lie in close spatial proximity in the feature map. If the sum- of-square error for the feature vectors associated with a cluster unit is greater than a predefined tolerance, then those vectors are used to create another feature map at the next level of the hierarchy. This growing procedure enables the feature set to control the number of cluster units generated. The hierarchical structure provides an efficient mechanism to deal with uncertainties in correct classification. Experimental studies are present din order to illustrate the robustness of this technique.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
George K. Knopf, George K. Knopf, "Data fusion using a hierarchy of self-organizing feature maps", Proc. SPIE 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics, (17 December 1998); doi: 10.1117/12.332783; https://doi.org/10.1117/12.332783
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