Image Matching Based on 2DPCA-SIFT - Multimedia and Signal Processing - page 382

algorithm integrally retains image information, which avoids structuring scatter
matrix in the high dimensional vector space, and reduces computation complexity and
saves operating time. The experimental results show that: the algorithm has
exceptional anti-scaling rotation ability and resistant to affine transformation and
JPEG compression ability, which ensures higher matching rate, and reduces
computation complexity. In addition in some cases, the dimension of feature matrix of
the proposed algorithm still is sill very big, therefore the next research focus is to
reduce the dimension.
Acknowledgments. Project is supported by the National Natural Science Foundation
of China under Grant (No. 61073121), Natural Science Foundation of Hebei Province
of China (No. F2009000215) and Medical Engineering Alternate Research Center
Open Foundation of Hebei University (No. BM201102).
References
1. Moravec, H.: Towards automatic visual obstacle avoidance. In: Processing of the 5th
International Joint Conference on Artificial Intelligence, p. 584 (1977)
2. Harris, C., Stephens, M.: A combined corner and edge detector. In: The Alvey Vision
Conference, pp. 147-151 (1988)
3. Lowe, D.G.: Object recognition from local scale-invariant features. In: International
Conference on Computer Vision, pp. 1150-1157 (1999)
4. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International
Journal of Computer Vision 60(2), 91-110 (2004)
5. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image
descriptors. In: IEEE Computer Society Conference on Computer Vision and Pattern
Recognition, pp. 511-517 (2004)
6. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded Up Robust Features. In: Leonardis,
A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404-417. Springer,
Heidelberg (2006)
7. Cui, C., Ngan, K.: Scale- and Affine-Invariant Fan Feature. IEEE Transactions on Image
Processing 20(6), 1627-1640 (2011)
8. Yao, S., Guturu, P., Buckles, B.P.: Wireless Capsule Endoscopy Video Segmentation
Using an Unsupervised Learning Approach Based on Probabilistic Latent Semantic
Analysis With Scale Invariant Features. IEEE Transactions on Information Technology in
Biomedicine 16(1), 98-105 (2011)
9. Wang, S., You, H., Fu, K.: BFSIFT: A Novel Method to Find Feature Matches for SAR
Image Registration. Geoscience and Remote Sensing Letters 9(4), 1-5 (2011)
10. Goncalves, H., Corte-Real, L., Goncalves, J.A.: Automatic Image Registration Through
Image Segmentation and SIFT. IEEE Transactions on Geoscience and Remote
Sensing 49(7), 2589-2600 (2011)
11. Yang, J., Zhang, D., Frangi, A.F.: Two-Dimensional PCA: A New Approach to
Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern
Analysis and Machine Intelligence 26(1), 131-137 (2004)