Title :
Harris feature vector descriptor
Author :
Wang, Xu-guang ; Su, Jie ; Cheng, Hai-yan
Author_Institution :
Dept. of Autom., North China Electr. Power Univ., Baoding, China
Abstract :
This paper defines a new image feature called Harris feature vector, which is able to describe the image gradient distribution in an effective way. By computing the mean and the standard deviation of the Harris feature vector in a local image region, novel descriptors are constructed for feature matching which are invariable to image rigid transformation and linear intensity change. Experimental evidence suggests that the novel descriptor for point matching has a good adaptability to slight view point changing, JPEG compression and nonlinear changing of intensity, besides, the descriptor for line matching performs well too.
Keywords :
data compression; feature extraction; gradient methods; image coding; image matching; Harris feature vector descriptor; JPEG compression; feature matching; image gradient distribution; image rigid transformation; linear intensity change; local image region; Cybernetics; Detectors; Feature extraction; Image coding; Machine learning; Transform coding; Vectors; Feature descriptor; Feature matching; HFV; Orthogonal transformation;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2010 International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-6526-2
DOI :
10.1109/ICMLC.2010.5581008