Fuzzy Generalized Hough Transform Invariant to Rotation and Scale in Noisy Environment

Generatized Hough Transform (GHT) is an efficient method for detecting curves by exploiting the duality between points on a curve and parameters of that curve. However GHT has some practical limitations such as high computational cost and huge memory requirement for detecting scaled and rotated objects. In this paper a new method, namely Fuzzy Generalized Hough Transform (FGHT), is proposed that alleviates these deficiencies by utilizing the concept of fuzzy inference system. In FGHT the R-table consists of a set of fuzzy rules which are fired by the gradient direction of edge pixels and vote for the possible location of the center. Moreover, the proposed method can identify the boundary of the rotated and scaled object via a new voting strategy. To evaluate the effectiveness of FGHT several experiments with scaled, rotated, occluded and noisy images are conducted. The results are compared with two extensions of GHT and have revealed that the proposed method can locate and detect the prototype object with least error under various conditions.