AN E-LEARNING PLATFORM FOR TEXTURE IMAGE ANALYSIS AND CLASSIFICATION

We propose an e-Learning platform or a web-based application for texture image analysis and classification using algorithms that assess fractal methods like: fractal dimension, lacunarity and succolarity. Fractal dimension measures the geometrical complexity of an object and is used as a tool for texture classification assignments. The lacunarity property can improve texture characteristics determined with fractal dimension by the fact that it specifies the texture orientation and shows how texture elements fill space (lacunarity reveals the gap distribution of the texture), while the fractal dimension measures how much space is occupied by texture elements. Succolarity is the fractal property that presents the percolation degree of a fluid flowing around and through an object in an image on a given direction. Various input texture images can be used in the application and the application itself can make automated conversions, adjustments or even offering advises. For classification purposes, multiple algorithms and strategies can be tested and the highest accuracy rate will be chosen for the best classification model. We are using Matlab web server technology to develop the application, mostly because Matlab is a solid and stable environment to develop such applications and one can benefit from the built-in image analysis routines. We intend that this e-Learning platform can be used in educational field for teaching students on image analysis and classification, image segmentation or object recognition issues. Other applicable cases for using this web-based application are research domains where generic or specialized datasets like phytopathology and entomology, tree bark or generic agricultural datasets are used.