Work

On the merits of the Gaussian Mixture as a model for oriented edgel distributions

Year: 2008

Type: article

Abstract: The aim of this report is to establish the credibility of the Gaussian Mixture Model (GMM) as a model for the distributions of oriented edgels of rigid and biological objects in noisy images. This is tackled in two stages: first, the response of the Sobel filter to noisy pixels is analysed to show that the result holds for smooth rigid objects. Secondly, arguments are presented to support the proposition that the model can also effectively capture the added uncertainty introduced by natural shape variation, as found in images of biological objects. The result has particular application in the extension of the Generalized Hough Transform (GHT) to deformable shapes; in particular it offers a tailored and manipulable alternative to the non-parametric kernel density estimate used by Ecabert and Thiran in [3]. 1

Source: Lincoln Repository (University of Lincoln)

Authors: Philip Assheton, Andrew Hunter

Language: English

Topic: Image and Object Detection Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Institutes of Health, National Center for Advancing Translational Sciences, National Center for Complementary and Integrative Health

Awards: 2016 International Conference on Plant Sterols, Health & Diseases, G4769891388, UCLA Clinical and Translational Science Institute, Cardiovascular protection by phytosterols in dyslipidemic mothers and progeny, Institutional Career Development Core

OpenAlex ID: W30101294

FWCI: 0.406

Cites: 12

Cited by: 1

Related to: 10