Abstract
In this paper, an asbestos counting method from microscope images of building materials is proposed. Since asbestos particles have unique color and shape, we use color and shape features for detecting and counting asbestos by computer. To classify asbestos and other particles, the Support Vector Machine (SVM) is used. When one kernel is applied to a feature vector which consists of color and shape, the similarity of each feature is not used effectively. Thus, kernels are applied to color and shape independently, and the summation kernel of color and shape is used. We confirm that the accuracy of asbestos detection is improved by using the summation kernel.
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Nomoto, A., Hotta, K., Takahashi, H. (2009). An Asbestos Counting Method from Microscope Images of Building Materials Using Summation Kernel of Color and Shape. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_82
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DOI: https://doi.org/10.1007/978-3-642-03040-6_82
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03039-0
Online ISBN: 978-3-642-03040-6
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