Application of the tensor voting technique for perceptual grouping to grey-level images: quantitative evaluation

Abstract

This paper presents a quantitative evaluation of the application of the perceptual grouping method known as tensor voting to grey-level images. For that purpose, we have introduced the use of local orientation tensors computed from a set of Gabor filters. While inputs formerly consisted of binary images or sparse edgel maps, we use oriented input tokens and the locations of junctions from images as input to the perceptual grouping. Here, we introduce a benchmark test to estimate the precision of our method with regards to angular and positional error. Results on these test images show that the computation of the tensorial input tokens is highly precise and robust against noise. Both aspects arc further improved by the subsequent grouping process.