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

Research output: Other contribution › Research

Standard

Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model. / Pedersen, Kim Steenstrup; van Dorst, Pieter; Loog, Marco.

2006, Andet.

Research output: Other contribution › Research

Harvard

Pedersen, KS, van Dorst, P & Loog, M 2006, Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model.. <http://www.dcs.shef.ac.uk/ml/gpip/abstract/pedersen.pdf>

APA

Pedersen, K. S., van Dorst, P., & Loog, M. (2006). Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model. http://www.dcs.shef.ac.uk/ml/gpip/abstract/pedersen.pdf

Vancouver

Pedersen KS, van Dorst P, Loog M. Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model. 2006.

Author

Pedersen, Kim Steenstrup ; van Dorst, Pieter ; Loog, Marco. / Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model. 2006.

Bibtex

@misc{e2fdff10475211ddb7b4000ea68e967b,
title = "Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model",
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.",
author = "Pedersen, {Kim Steenstrup} and {van Dorst}, Pieter and Marco Loog",
year = "2006",
language = "English",
type = "Other",

}

RIS

TY - GEN

T1 - Minimum Likelihood Image Feature and Scale Detection Based on the Brownian Image Model

AU - Pedersen, Kim Steenstrup

AU - van Dorst, Pieter

AU - Loog, Marco

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

M3 - Other contribution

ER -

ID: 4850836