Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model

Kim Steenstrup Pedersen, Pieter van Dorst, Marco Loog

Research output: Other contributionResearch

Abstract

We present a novel approach to image feature and scale detection based on the fractional Brownian image model in which images are realisations of a Gaussian random process on the plane. Image features are points of interest usually sparsely distributed in images. We propose to detect such points and their intrinsic scale by detecting points in scale-space that locally minimises the likelihood under the model.
Original languageEnglish
Publication date2006
Publication statusPublished - 2006
Externally publishedYes

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