Object Recognition Based on Maximally Stable Extremal Region and Scale-Invariant Feature Transform
Abstract: For the defect in
describing affine and blur invariable of scale-invariant feature transform
(SIFT) at large viewpoint variation, a new object recognition method is
proposed in this paper, which used maximally stable extremal region (MSER)
detecting MSERs and SIFT describing local feature of these regions. First, a
new most stability criterion is adopt to improve the detection effect at
irregular shaped regions and under blur conditions; then, the local feature
descriptors of MSERs is extracted by the SIFT; and finally, the method proposed
is comparing then correct rate of SIFT and the proposed through image recognition
with standard test images. Experimental results show that the method proposed
can still achieve more than 74% recognition correct rate at different
viewpoint, which is better than SIFT.
Author: Hongjun Guo, Lili Chen
Journal Code: jptkomputergg160228
