Computationally efficient modeling of imagery using scaled, extracted principal components

Abstract

A computationally efficient modeling system for imagery scales both the original image and corresponding principal component tiles in the same proportion to be able to extract scaled principal components. The system includes recovery of feature weights for the image model by extracting the weights from the reduced size principal component tiles. The use of the reduced size tiles to derive weights dramatically reduces computer overhead both in the generation of the files and in the generation of the weights, and is made possible by the fact that the weights from the scaled down tiles are nearly equal to the weights of the tiles associated with the full size image. The subject system thus reduces computation and the number of bits required to represent features by first scaling the image and then tiling the image in the same proportion. In one embodiment, the scaled down tiles are used as training exemplars used to generate the principal components.

Description

CROSS REFERENCE TO RELATED APPLICATIONS[0001]This application claims rights under U.S. Provisional Application Ser. No. 60 / 353,476, filed Mar. 31, 2002.[0002]This application is a Reissue application of U.S. Ser. No. 10 / 334,816, filed Dec. 31, 2002, now U.S. Pat. No. 7,113,654, granted Sep. 26, 2006, which claims the benefit of Provisional Application No. 60 / 353,476, filed Jan. 31, 2002.STATEMENT OF GOVERNMENT INTEREST[0003]This invention was made with U.S. Government support under Contract No. DAAL01-96-2-0002 with the Army Research Laboratory, and the U.S. Government has certain rights in the invention.FIELD OF INVENTION[0004]This invention relates to image processing and more particularly to an efficient system for image modeling and compression.BACKGROUND OF THE INVENTION[0005]The extraction of principal components from images is well known, with one extraction technique using neural networks as described in U.S. Pat. No. 5,377,305. Principal components are those which have self...

AI Technical Summary

Benefits of technology

[0014]As will be seen, in the subject invention a method is described which makes feasible a complete principal component analysis of an image (whether standard or hyperspectral). This is because the subject system includes a method which significantly reduces computation. Moreover, the features derived are image adaptive, unlike fixed basis methods, with the adaptability allowing the possibility of better representation, especially for non-standard imagery.

[0015]In one embodiment, the subject system allows extraction of principal components from any kind of image in a computationally efficient manner. The method is based on self-similarity in the same way the wavelet methods described above are based on self-similarity. However, in the subject invention the goal is to introduce scale not just for its own sake but also to reduce computation and the overhead of using data adaptive features. While there are methods for image compression and methods for principal component extraction, the combination of using principal component features to represent imagery while extracting them in a computationally efficient way is unique.

[0016]In the subject invention, a computationally efficient modeling system for imagery scales both the original image and corresponding principal component tiles in the same proportion to be able to extract scaled principal components. The system includes recovery of feature weights for the image model by extracting the weights from the reduced size principal component tiles. The use of the reduced size tiles to derive weights dramatically reduces computer overhead, and is made possible by the finding that the weights from the scaled down tiles are nearly equal to the weights of the tiles associated with the full size image. In short, not only are the scaled down images self similar, the scaled down tiles are self similar. This permits the scaled down tiles to be used to generate weights. Using scaled down tiles dramatically reduces computation and the number of bits required to represent features. First scaling the image and then tiling the image in the same proportion provides reduced size tiles which when dot multiplied by the original image produces the required weights. Image transmission involves transmitting only the principal component tiles and the weights which effects the compression. The computational savings using the scaled down tiles is both in generating the tiles and in generating the weights. In one embodiment, the scaled down tiles are used as training exemplars used to generate the principal components.

[0018]In summary, a computationally efficient modeling system for imagery scales both the original image and corresponding principal component tiles in the same proportion to be able to extract scaled principal components. The system includes recovery of feature weights for the image model by extracting the weights from the reduced size principal component tiles. The use of the reduced size tiles to derive weights dramatically reduces computer overhead both in the generation of the files and in the generation of the weights, and is made possible by the fact that the weights from the scaled down tiles are nearly equal to the weights of the tiles associated with the full size image. The subject system thus reduces computation and the number of bits required to represent features by first scaling the image and then tiling the image in the same proportion. In one embodiment, the scaled down tiles are used as training exemplars used to generate the principal components.

Problems solved by technology

Up until recently, standard compression has not been thought suitable for principal component image modeling and compression, which can involve temporal and characteristics other than spatial characteristics.

The fixed basis of JPEG and others cannot handle the expanded feature set associated with hyperspectral imagery.

Nor can these techniques handle voxels which are used to encode numbers of additional features of an image.

How to do this in a computationally efficient manner and one which is universal across all platforms is a challenge.

As a result, wavelet decomposition which provides control over computation is limited by decimation.

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