Random projection and orthonormality for lossy image compression

Author:

Amador José J.

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing

Reference40 articles.

1. Image Processing, Analysis, and Machine Vision;Sonka,1999

2. E. Bingham, H. Mannila, Random projection in dimensionality reduction: applications to image and text data, in: Proceedings on 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 245–250.

3. J. L. Crowley, K. Schwerdt, Robust tracking and compression for video communication, Proceedings of International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (1999) 2–9.

4. H. Chen, C. H. Chen, Hyperspectral image data unsupervised classification using Gauss-Markov random fields and PCA principle, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (2002) 1431–1433.

5. E. Choi, H. Choi, C. Lee, Principal component analysis with pre-emphasis for compression of hyperspectral imagery, Proceedings of IEEE International Geoscience and Remote Sensing Symposium (2005) 704–706.

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