Abstract
We present a method for automated grading of texture samples which grades the sample based on a sequential scan of overlapping blocks, whose texture is classified using a soft partitioned SOM, where the soft clusters have been automatically generated using a labelled training set. The method was devised as an alternative to manual selection of hard clusters in a SOM for machine vision inspection of tuna meat. We take advantage of the sequential scan of the sample to perform a sub-optimal search in the SOM for the classification of the blocks, which allows real time implementation.
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Martín-Herrero, J., Ferreiro-Armán, M., Alba-Castro, J.L. (2004). Grading Textured Surfaces with Automated Soft Clustering in a Supervised SOM. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_40
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DOI: https://doi.org/10.1007/978-3-540-30126-4_40
Publisher Name: Springer, Berlin, Heidelberg
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