Abstract
A new illumination invariant feature extraction method for texture classification is proposed. In order to capture the local image texture, texture pattern transform (TPT) in a local neighborhood of a monochrome texture image is introduced. The TPT is robust against any monotonic transformation of the gray scale. The joint distributions of two different TPT, which can be characterized using a pattern co-occurrence matrix (PCM), can be used for texture classification. The PCM technique only requires comparison and counting operations, and thus is highly computationally efficient. The properties of PCM include translation and illuminant invariance, which is highly desirable in real-world applications. Illumination invariant texture classification experimental results show that the texture features derived from PCM achieve good discrimination.
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© 2011 Springer-Verlag Berlin Heidelberg
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Song, Q. (2011). Illumination Invariant Texture Classification with Pattern Co-occurrence Matrix. In: Shen, G., Huang, X. (eds) Advanced Research on Computer Science and Information Engineering. CSIE 2011. Communications in Computer and Information Science, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21402-8_11
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DOI: https://doi.org/10.1007/978-3-642-21402-8_11
Publisher Name: Springer, Berlin, Heidelberg
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