IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Construction of Orientation and Velocity Selective Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Directional Analysis of Images in Scale Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generic Neighborhood Operators
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multisensor image fusion using the wavelet transform
Graphical Models and Image Processing
A lie group approach to steerable filters
Pattern Recognition Letters
Image representation and compression with steered Hermite transforms
Signal Processing
Signal Processing for Computer Vision
Signal Processing for Computer Vision
Finding Edges and Lines in Images
Finding Edges and Lines in Images
Noise reduction in computerized tomography images by means of polynomial transforms
Journal of Visual Communication and Image Representation
The multiscale Hermite transform for local orientation analysis
IEEE Transactions on Image Processing
Image fusion algorithm using the multiresolution directional-oriented hermite transform
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
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The Hermite transform is introduced as an image representation model that can be used to tackle the problem of fusion in multimodal medical imagery. This model includes some important properties of human visual perception, such as local orientation analysis and the Guassian derivative model of early vision. Local analysis is achieved by windowing the image with a Gaussian function, then a local expansion into orthogonal polynomials takes place at every window position. Expansion coefficients are called Hermite coefficients and it is shown that they can be directly obtained by convolving the image with Gaussian derivative filters, in agreement with psychophysical insights of human visual perception. A compact representation can be obtained by locally steering the Hermite coefficients towards the direction of local maximum energy. Image fusion is achieved by combining the steered Hermite coefficients of both source images with the method of verification of consistency. Fusion results are compared with a competitive wavelet-based technique, proving that the Hermite transform provides better reconstruction of relevant image structures.