A novel audio color watermarking scheme based on self-organizing map
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Surface grading of bamboo strips using multi-scale color texture features in eigenspace
Computers and Electronics in Agriculture
Discrimination of bark from wood chips through texture analysis by image processing
Computers and Electronics in Agriculture
HEp-2 cell images classification based on textural and statistic features using self-organizing map
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
Performance analysis of colour descriptors for parquet sorting
Expert Systems with Applications: An International Journal
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In order to produce a high quality rubberwood fingerjoint with highly uniform colour, wood boards of naturally different shades and colours are required to be elaborately classified and grouped. Within each group, wood boards of comparable shade and colour are then cut and joined to form a highly uniform shade and colour fingerjoint of the required dimensions. Currently, many manufacturers in Thailand still rely heavily on a manual classification process by an expert. In this paper, an automatic approach based on a combination of an image processing technique and an artificial neural network is presented. The Kohonen self organizing map (SOM) is selected and used for training with modified histogram data from the hue colour component of the rubberwood boards' images. The outcome SOM is then used to classify an unknown colour rubberwood board with a novel colour group identification algorithm. The overall approach has proved effective in classifying the unknown colour of boards with as high as 95% accuracy without human intervention. In many cases, the approach provides invaluable information to guide an operator to easily classify the remaining 5%.