The Second Order Local-Image-Structure Solid
IEEE Transactions on Pattern Analysis and Machine Intelligence
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
International Journal of Computer Vision
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In this paper, we investigate to which extent the "raw" mapping of Taylor series coefficients into jet-space can be used as a "language" for describing local image structure in terms of geometrical image features. Based on empirical data from the van Hateren database, we discuss modelling of probability densities for different feature types, calculate feature posterior maps, and finally perform classification or simultaneous feature detection in a Bayesian framework. We introduce the Brownian image model as a generic background class and extend with empirically estimated densities for edges and blobs. We give examples of simultaneous feature detection across scale.