Paper Info
Title
Fuzzy generalized hough transform invariant to rotation and scale in noisy environment
Abstract
Generalized 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.
Year
DOI
Venue
2009
10.1109/FUZZY.2009.5277217
FUZZ-IEEE
Keywords
Field
DocType
noisy environment,fuzzy generalized hough transform,fuzzy inference system,generalized hough transform,fuzzy generalized hough,edge pixel,efficient method,new method,fuzzy rule,prototype object,new voting strategy,gradient method,object recognition,pixel,noise,noise measurement,prototypes,fuzzy sets
Gradient method,Pattern recognition,Noise measurement,Computer science,Fuzzy logic,Hough transform,Fuzzy set,Invariant (mathematics),Pixel,Artificial intelligence,Machine learning,Cognitive neuroscience of visual object recognition
Conference
Citations 
PageRank 
References 
2
0.39
9
Authors
3
Name
Order
Citations
PageRank
Hamid Izadinia116411.16
Fereshteh Sadeghi21005.65
Mohammad Mehdi Ebadzadeh337227.36