Introduction: Clinical applications such as exercise stress cardiac MRI require the use of accelerated scan techniques to minimize acquisition time while maintaining image quality. In this context, maintaining sufficient signal-to-noise ratio (SNR) is crucial in order to preserve clinically relevant information. While the combination of echo planar readout and parallel reconstruction successful...

From the birth of multi-spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray-scale images, instead of an ensemble of an n-D realization. However, it has been proved that using vector-based tools leads to a more appropriate understanding of color images and thus more efficient algorithms for processing them. Such tools are...

The ability to change illumination is a crucial factor in image-based modeling and rendering. Image-based relighting offers such capability. However, the trade-off 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...

We propose Fast Eigen Matching, a method for accelerating the matching and learning processes of the eigenspace method for rotation invariant template matching (RITM). Correlation-based template matching is one of the basic techniques used in computer vision. Among them, rotation invariant template matching (RITM), which locates a known template in a query irrespective of the template’s transla...

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The maj...

We present an efficient and accurate algorithm for principal component analysis (PCA) of a large set of two-dimensional images and, for each image, the set of its uniform rotations in the plane and its reflection. The algorithm starts by expanding each image, originally given on a Cartesian grid, in the Fourier-Bessel basis for the disk. Because the images are essentially band limited in the Fo...

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper, we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The ma...

since the birth of multi–spectral imaging techniques, there has been a tendency to consider and process this new type of data as a set of parallel gray–scale images, instead of an ensemble of an n–d realization. although, even now, some researchers make the same assumption, it is proved that using vector geometries leads to better results. in this paper, first a method is proposed to extract th...

Recent biological evidence suggests that position and orientation can be estimated from an adequately compressed set of environment snapshots and their relationships. In this paper we present a pure appearance-based localisation method using an eigenspace representation of panoramic images. We rst review several types of rotational invariant representation of panoramic images in terms of their ...