Feature Asymmetry of the Conformal Monogenic Signal

Local properties of image (phase, amplitude and orientation) can be estimated practically using quadrature filters kernel and can be easily represented in two dimensions using the monogenic signal. This powerful feature representation has given rise to robust phase-based edge detection. Nonetheless, it is limited to the class of intrinsically onedimensional signals, such as lines and edges. All other possible local patterns such as corners and junction are of intrinsic dimension two. Our aim in this paper is to present a new edge detection method for extracting local features of any curved signal. It is based on the conformal monogenic signal which is in practical applications compatible with intrinsically one and two-dimensional signal. Using different filters, our model have been tested and compared with classical models and some recent ones. The preliminary results show that our detection technique is more efficient and more accurate.