Set Of Eigenimages Research Articles

Compressing the illumination-adjustable images with principal component analysis

The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the tradeoff is the enormous increase of storage requirement. In this paper, we propose a compression scheme that effectively reduces the data volume while maintaining the real-time relighting capability. The proposed method is based on principal component analysis (PCA). A block-wise PCA is used to practically process the huge input data. The output of PCA is a set of eigenimages and the corresponding relighting coefficients. By dropping those low-energy eigenimages, the data size is drastically reduced. To further compress the data, eigenimages left are compressed using transform coding and quantization while the relighting coefficients are compressed using uniform quantization. We also suggest the suitable target bit rate for each phase of the compression method in order to preserve the visual quality. Finally, we propose a real-time engine that relights images from the compressed data.

Principal-component characterization of noise for infrared images.

Principal-component decomposition is applied to the analysis of noise for infrared images. It provides a set of eigenimages, the principal components, that represents spatial patterns associated with different types of noise. We provide a method to classify the principal components into processes that explain a given amount of the variance of the images under analysis. Each process can reconstruct the set of data, thus allowing a calculation of the weight of the given process in the total noise. The method is successfully applied to an actual set of infrared images. The extension of the method to images in the visible spectrum is possible and would provide similar results.

Magnetic resonance image synthesis using a flexible model

Image synthesis methods are based on the hypothesis that a magnetic resonance (MR) image with optimized contrast can be reproduced by synthesis from three calculated basic images of T1, T2 and spin density. This method, however, is limited by noise due to uncertainties in the initial measurements. The principal component analysis (PCA) method is based on an information theory approach that decomposes MR images into a small set of characteristic feature images. PCA images, or eigenimages, show morphology by condensing the structural information from the source images. Eigenimages have also been shown to improve contrast-to-noise ratio (CNR) compared with source images. In this study we have developed a method of synthesizing MR images using a flexible model, comprising a set of eigenimages derived from PCA. A matching process has been carried out to find the best fit between the model and a synthetic image calculated from the Bloch equations. The method has been applied to MR images obtained from a group of patients with intracranial lesions. The images derived from the flexible model show increased lesion conspicuity, reduced artefact and comparable CNR to the directly acquired images while maintaining the MR characteristic information for diagnosis.

Optical processor for recognition of three-dimensional targets viewed from any direction

A procedure is presented for generating a small bank of optical correlation filters that can recognize a large number of perspective views of an object. The method applies to general kinds of image distortions in addition to those generated by different perspective views. The holographic filters are also invariant to image intensity and position (translation invariance). The method of design is to decompose the entire set of object variations into a set of eigenimages. These eigenimages contain complete information about the target set. An iterative procedure combines the eigenimages with different relative phases, so that complete target information can be extracted in an optical implementation. An example illustrates that a set of only 20 holographic filters recognizes a three-dimensional target over a continuous range of viewing angles.