A digital image can be perceived as a 2.5D surface consisting of pixel coordinates and the intensity of pixel as height of the point in the surface. Such surfaces can be efficiently represented by the pair of dual plane graphs: neighborhood (primal) graph and its dual. By defining ori-entation of edges in the primal graph and use of Local Binary Patters(LBPs), we can categorize the vertices corresponding to the pixel into critical (maximum, minimum, saddle) or slope points. Basic operation of contraction and removal of edges in primal graph result in configuration of graphs with different combinations of critical and non-critical points.The faces of graph resemble a slope region after restoration of the contin-uous surface by successive monotone cubic interpolation. In this paper, we define orientation of edges in the dual graph such that it remains consistent with the primal graph. Further we deliver the necessary and sufficient conditions for merging of two adjacent slope regions


Batavia, D., Kropatsch, W. G., Casablanca, R. M., & Gonzalez-Diaz, R. (2019). Congratulations! Dual Graphs Are Now Orientated! In Graph-Based Representations in Pattern Recognition (pp. 131–140). https://doi.org/10.1007/978-3-030-20081-7_13