Paper Info
Title
High Definition Feature Map for GVF Snake by Using Harris Function
Abstract
In image segmentation the gradient vector flow snake model is widely used. For concave curvatures snake model has good convergence capabilities, but poor contrast or saddle corner points may result in a loss of contour. We have introduced a new external force component and an optimal initial border, approaching the final boundary as close as possible. We apply keypoints defined by corner functions and their corresponding scale to outline the envelope around the object. The Gradient Vector Flow (GVF) field is generated by the eigenvalues of Harris matrix and/or the scale of the feature point. The GVF field is featured by new functions characterizing the edginess and cornerness in one function. We have shown that the max(0,log[max(λ 1, λ 2)]) function fulfills the requirements for any active contour definitions in case of difficult shapes and background conditions. This new GVF field has several advantages: smooth transitions are robustly taken into account, while sharp corners and contour scragginess can be perfectly detected.
Year
DOI
Venue
2010
10.1007/978-3-642-17688-3_17
ACIVS (1)
Keywords
Field
DocType
corner detection,active-contour,shape analysis,harris function.,smooth transition,image segmentation,active contour
Active contour model,Saddle,Convergence (routing),Computer vision,Corner detection,Image segmentation,Vector flow,Artificial intelligence,Mathematics,Eigenvalues and eigenvectors,Shape analysis (digital geometry)
Conference
Citations 
PageRank 
References 
4
0.45
12
Authors
2
Name
Order
Citations
PageRank
Andrea Kovács1373.56
Tamás Szirányi215226.92