Textured surface identification in noisy color images

Mehmet Celenk

doi:10.1117/12.243580

26 June 1996 Textured surface identification in noisy color images

Mehmet Celenk

Proceedings Volume 2753, Visual Information Processing V; (1996) https://doi.org/10.1117/12.243580
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Automatic identification of textured surfaces is essential in many imaging applications such as image data compression and scene recognition. In these applications, a vision system is required to detect and identify irregular textures in the noisy color images. This work proposes a method for texture field characterization based on the local textural features. We first divide a given color image into n multiplied by n local windows and extract textural features in each window independently. In this step, the size of a window should be small enough so that each window can include only two texture fields. Separation of texture areas in a local window is first carried out by the Otsu or Kullback threshold selection technique on three color components separately. The 3-D class separation is then performed using the Fisher discriminant. The result of local texture classification is combined by the K-means clustering algorithm. The texture fields detected in a window are characterized by their mean vectors and an element-to-set membership relation. We have experimented with the local feature extraction part of the method using a color image of irregular textures. Results show that the method is effective for capturing the local textural features.

Citation Download Citation

Mehmet Celenk "Textured surface identification in noisy color images", Proc. SPIE 2753, Visual Information Processing V, (26 June 1996); https://doi.org/10.1117/12.243580

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