Extraction of 2.1D Sketch from Monocular Images Using DIRECT Optimization Algorithm
Rashmi R. Deshpande, Mahabaleswara Ram Bhatt · 2017
A performance and time efficient 2.1D sketch extraction from a given monocular image is proposed in a global optimization framework that exploits the divided rectangles (DIRECTs) but otherwise extracted by heuristic global optimization methods, like genetic algorithms, particle swarm evolution algorithms, and simulated annealing. An appeal of these algorithms is that they are guaranteed to yield the global minimum in probability one sense. The flip side of these algorithms is that they are usually time consuming and also may not reproduce the results consistently. Contrastingly, the currently proposed in this paper, we formulate 2.1D sketch extraction using DIRECT algorithm, which not only apparently aims at providing the better global minimum but as a deterministic approach. Further, the extraction of 2.1D sketch are found to yield comparatively better than the results obtained by the hybrid differential evolution algorithm [1]. Interestingly, the proposed algorithm is superior since it takes far less computations in converging to either same global minimum or far better global minimum value that gives a 2.1 D sketch extraction.