Abstract
In present paper, a non-Euclidean approach for finding high dimensional objects has been proposed. The approach is based on the fact that fractal dimension represents the roughness of 2D objects in digital images which can be measured and used to infer about the structure of objects. Since fractal dimension varies in the range 2.0 to 3.0, where the objects having higher value of fractal dimension represent more dense objects in terms of their space filling property, the measurement of fractal dimension leads to discriminate various objects. The image texture obtained from fractal map has been used for this discrimination. The texture map is segmented on the basis of fractal dimension values and segmentation evaluation has been done. The results obtained for the test images are promising and show that the image texture can be segmented using fractal dimension values. The possible future scope of the work has also been highlighted with the applications in real life, e.g., computer vision.
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Pant, T. (2013). Implementation of Fractal Dimension for Finding 3D Objects: A Texture Segmentation and Evaluation Approach. In: Agrawal, A., Tripathi, R.C., Do, E.YL., Tiwari, M.D. (eds) Intelligent Interactive Technologies and Multimedia. IITM 2013. Communications in Computer and Information Science, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37463-0_26
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DOI: https://doi.org/10.1007/978-3-642-37463-0_26
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