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Image Processing Applications Based on Texture and Fractal Analysis

Image Processing Applications Based on Texture and Fractal Analysis

Radu Dobrescu, Dan Popescu
Copyright: © 2011 |Pages: 25
ISBN13: 9781609604776|ISBN10: 1609604776|EISBN13: 9781609604783
DOI: 10.4018/978-1-60960-477-6.ch014
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MLA

Dobrescu, Radu, and Dan Popescu. "Image Processing Applications Based on Texture and Fractal Analysis." Applied Signal and Image Processing: Multidisciplinary Advancements, edited by Rami Qahwaji, et al., IGI Global, 2011, pp. 226-250. https://doi.org/10.4018/978-1-60960-477-6.ch014

APA

Dobrescu, R. & Popescu, D. (2011). Image Processing Applications Based on Texture and Fractal Analysis. In R. Qahwaji, R. Green, & E. Hines (Eds.), Applied Signal and Image Processing: Multidisciplinary Advancements (pp. 226-250). IGI Global. https://doi.org/10.4018/978-1-60960-477-6.ch014

Chicago

Dobrescu, Radu, and Dan Popescu. "Image Processing Applications Based on Texture and Fractal Analysis." In Applied Signal and Image Processing: Multidisciplinary Advancements, edited by Rami Qahwaji, Roger Green, and Evor L. Hines, 226-250. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-477-6.ch014

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Abstract

Texture analysis research attempts to solve two important kinds of problems: texture segmentation and texture classification. In some applications, textured image segmentation can be solved by classification of small regions obtained from image partition. Two classes of features are proposed in the decision theoretic recognition problem for textured image classification. The first class derives from the mean co-occurrence matrices: contrast, energy, entropy, homogeneity, and variance. The second class is based on fractal dimension and is derived from a box-counting algorithm. For the purpose of increasing texture classification performance, the notions “mean co-occurrence matrix” and “effective fractal dimension” are introduced and utilized. Some applications of the texture and fractal analyses are presented: road analysis for moving objective, defect detection in textured surfaces, malignant tumour detection, remote land classification, and content based image retrieval. The results confirm the efficiency of the proposed methods and algorithms.

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