Simultaneous Separation of Low Level Features in Color Images using Orthogonal Polynomials - Krishnamoorthy, R; Thennavan, S; Harini, R.G; Narayani, K.K. Manisha (International Journal of Computer Sciences and Engineering, 2025)
In this paper, a new method for simultaneous separation of features in color images using Orthogonal Polynomials is proposed. The low-level features,edge and texture present in the color image under analysis are extracted simultaneously in frequency domain usingOrthogonal Polynomials Transformation. The transformed coefficientsobtained from Orthogonal Polynomials Transformation are categorized into color coefficients, texture coefficients and edge coefficients based on the linear contrast due to Orthogonal Polynomials Transformation in different coordinate axes. A Simplified Gradient Measure approach (SGM approach) is used to extract the edge and texture part of the color image from the categorized coefficients simultaneously after careful examination and representation of color textures. The proposed method is tested with various standard color texture images. The results obtained using this proposed feature separation method is encouraging.