Image Field Categorization and Edge/Corner Detection from Gradient Covariance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Case-Based Approach to Image Recognition
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Scale-Space Image Analysis Based on Hermite Polynomials Theory
International Journal of Computer Vision
Hypotheses for Image Features, Icons and Textons
International Journal of Computer Vision
Extracting image orientation feature by using integration operator
Pattern Recognition
The hermite transform: a survey
EURASIP Journal on Applied Signal Processing
A biologically motivated multiresolution approach to contour detection
EURASIP Journal on Applied Signal Processing
Local orientation estimation by tomographic Hermite slices
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
IEEE Transactions on Information Technology in Biomedicine
Accurate image rotation using Hermite expansions
IEEE Transactions on Image Processing
Local orientation estimation by Tomographic Hermite slices
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
IEEE Transactions on Image Processing
Scale-space image analysis based on Hermite polynomials theory
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
Maximum likelihood orientation estimation of 1-D patterns in laguerre-gauss subspaces
IEEE Transactions on Image Processing
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Image fusion algorithm using the multiresolution directional-oriented hermite transform
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Image features and the 1-D, 2nd
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
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We introduce new theoretical results on how the local differential structure in images can be described, processed, and coded efficiently by means of the Hermite transform. It is shown that the Hermite transform can take many alternative forms, all of which have their specific advantages. The various forms of the Hermite transform correspond to different ways of coding the local orientation in the image, and we describe a systematic theory, based on well-known principles from vector spaces, for deriving such alternative forms. One specific case, called the sampled Hermite transform, is shown to be especially interesting for adaptive image processing and coding. An application in the field of adaptive noise reduction is included by way of illustration