Object Recognition Based on Maximally Stable Extremal Region and Scale-Invariant Feature Transform

Hongjun Guo, Lili Chen


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.

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DOI: http://dx.doi.org/10.12928/telkomnika.v14i2.2754

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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