Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.329
This paper is concerned with Modulus Maximum of Wavelet Transform (MMWT) and a graph theoretic method. The methods are applicable to extracting features of seafloor sonar image and data association problems. We will first get image’s modulus and modulus’ direction matrix by MMWT method. And according to calculating modulus’ threshold, obtain the geometric features of the image or the point features. Then calculate geometric centrobaric coordinate of the geometric features as matching point.For point feature, feature’s Vector will be created by the combination of region direction of modulus. For geometric feature, feature’s Vector is its perimeter and area information. At last, the key points between images will be associated by Maximum Common Subgraph method and validated by the feature vectors. The experimental results show that the methods are reliable and robust in continuous sonar image of seafloor.
Sonar Image Feature, MMWT, Maximum Common Subgraph, Matching
S. Shi and D. Xu, "A Robust Approach of Sonar Image Feature Detection and Matching," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 523-527.