Adaptive Hough transform based on sample distributions

An adaptive Hough transform (AHT) method was proposed, which aims at reducing effects of the quantization unit of the parameter space on Hough transform(HT) in detecting line features. First, the sample model was built up by using samples and computing parameters of the model. Then, according to changes in the model parameters and sample distributions,the method was established to get the appropriate quantization parameters. Finally, the optimized quantization units were obtained and applied to feature extraction in a structured environment. The results show that the proposed method can optimize the quantization units, reduce the line detection error,and improve detection accuracy.